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Merge pull request #7497 from freqtrade/new_release

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New release 2022.9
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Matthias 2022-09-29 18:06:57 +02:00 committed by GitHub
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37
.github/workflows/ci.yml vendored
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@ -24,7 +24,7 @@ jobs:
strategy:
matrix:
os: [ ubuntu-18.04, ubuntu-20.04, ubuntu-22.04 ]
python-version: ["3.8", "3.9", "3.10"]
python-version: ["3.8", "3.9", "3.10.6"]
steps:
- uses: actions/checkout@v3
@ -121,7 +121,7 @@ jobs:
strategy:
matrix:
os: [ macos-latest ]
python-version: ["3.8", "3.9", "3.10"]
python-version: ["3.8", "3.9", "3.10.6"]
steps:
- uses: actions/checkout@v3
@ -205,7 +205,7 @@ jobs:
strategy:
matrix:
os: [ windows-latest ]
python-version: ["3.8", "3.9", "3.10"]
python-version: ["3.8", "3.9", "3.10.6"]
steps:
- uses: actions/checkout@v3
@ -272,6 +272,16 @@ jobs:
pip install pyaml
python build_helpers/pre_commit_update.py
pre-commit:
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: "3.10"
- uses: pre-commit/action@v3.0.0
docs_check:
runs-on: ubuntu-20.04
steps:
@ -302,7 +312,7 @@ jobs:
# Notify only once - when CI completes (and after deploy) in case it's successfull
notify-complete:
needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check ]
needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check, pre-commit ]
runs-on: ubuntu-20.04
# Discord notification can't handle schedule events
if: (github.event_name != 'schedule')
@ -327,7 +337,7 @@ jobs:
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
deploy:
needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check ]
needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check, pre-commit ]
runs-on: ubuntu-20.04
if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
@ -397,15 +407,6 @@ jobs:
run: |
build_helpers/publish_docker_multi.sh
- name: Discord notification
uses: rjstone/discord-webhook-notify@v1
if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false) && (github.event_name != 'schedule')
with:
severity: info
details: Deploy Succeeded!
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
deploy_arm:
needs: [ deploy ]
# Only run on 64bit machines
@ -433,3 +434,11 @@ jobs:
BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}
run: |
build_helpers/publish_docker_arm64.sh
- name: Discord notification
uses: rjstone/discord-webhook-notify@v1
if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false) && (github.event_name != 'schedule')
with:
severity: info
details: Deploy Succeeded!
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}

6
.pre-commit-config.yaml
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@ -15,7 +15,7 @@ repos:
additional_dependencies:
- types-cachetools==5.2.1
- types-filelock==3.2.7
- types-requests==2.28.9
- types-requests==2.28.11
- types-tabulate==0.8.11
- types-python-dateutil==2.8.19
# stages: [push]
@ -34,7 +34,9 @@ repos:
exclude: |
(?x)^(
tests/.*|
.*\.svg
.*\.svg|
.*\.yml|
.*\.json
)$
- id: mixed-line-ending
- id: debug-statements

2
Dockerfile
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@ -1,4 +1,4 @@
FROM python:3.10.6-slim-bullseye as base
FROM python:3.10.7-slim-bullseye as base
# Setup env
ENV LANG C.UTF-8

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6
build_helpers/install_windows.ps1
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@ -6,13 +6,13 @@ python -m pip install --upgrade pip wheel
$pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
if ($pyv -eq '3.8') {
pip install build_helpers\TA_Lib-0.4.24-cp38-cp38-win_amd64.whl
pip install build_helpers\TA_Lib-0.4.25-cp38-cp38-win_amd64.whl
}
if ($pyv -eq '3.9') {
pip install build_helpers\TA_Lib-0.4.24-cp39-cp39-win_amd64.whl
pip install build_helpers\TA_Lib-0.4.25-cp39-cp39-win_amd64.whl
}
if ($pyv -eq '3.10') {
pip install build_helpers\TA_Lib-0.4.24-cp310-cp310-win_amd64.whl
pip install build_helpers\TA_Lib-0.4.25-cp310-cp310-win_amd64.whl
}
pip install -r requirements-dev.txt
pip install -e .

9
config_examples/config_freqai.example.json
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@ -53,7 +53,6 @@
],
"freqai": {
"enabled": true,
"startup_candles": 10000,
"purge_old_models": true,
"train_period_days": 15,
"backtest_period_days": 7,
@ -75,9 +74,11 @@
"weight_factor": 0.9,
"principal_component_analysis": false,
"use_SVM_to_remove_outliers": true,
"stratify_training_data": 0,
"indicator_max_period_candles": 20,
"indicator_periods_candles": [10, 20]
"indicator_periods_candles": [
10,
20
],
"plot_feature_importances": 0
},
"data_split_parameters": {
"test_size": 0.33,

23
config_examples/config_full.example.json
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@ -64,8 +64,8 @@
"stoploss_on_exchange_limit_ratio": 0.99
},
"order_time_in_force": {
"entry": "gtc",
"exit": "gtc"
"entry": "GTC",
"exit": "GTC"
},
"pairlists": [
{"method": "StaticPairList"},
@ -172,7 +172,24 @@
"jwt_secret_key": "somethingrandom",
"CORS_origins": [],
"username": "freqtrader",
"password": "SuperSecurePassword"
"password": "SuperSecurePassword",
"ws_token": "secret_ws_t0ken."
},
"external_message_consumer": {
"enabled": false,
"producers": [
{
"name": "default",
"host": "127.0.0.2",
"port": 8080,
"ws_token": "secret_ws_t0ken."
}
],
"wait_timeout": 300,
"ping_timeout": 10,
"sleep_time": 10,
"remove_entry_exit_signals": false,
"message_size_limit": 8
},
"bot_name": "freqtrade",
"db_url": "sqlite:///tradesv3.sqlite",

1
docker/Dockerfile.freqai
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@ -6,4 +6,3 @@ FROM ${sourceimage}:${sourcetag}
COPY requirements-freqai.txt /freqtrade/
RUN pip install -r requirements-freqai.txt --user --no-cache-dir

3
docker/Dockerfile.jupyter
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@ -1,7 +1,8 @@
FROM freqtradeorg/freqtrade:develop_plot
RUN pip install jupyterlab --user --no-cache-dir
# Pin jupyter-client to avoid tornado version conflict
RUN pip install jupyterlab jupyter-client==7.3.4 --user --no-cache-dir
# Empty the ENTRYPOINT to allow all commands
ENTRYPOINT []

2
docker/docker-compose-jupyter.yml
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@ -10,7 +10,7 @@ services:
ports:
- "127.0.0.1:8888:8888"
volumes:
- "./user_data:/freqtrade/user_data"
- "../user_data:/freqtrade/user_data"
# Default command used when running `docker compose up`
command: >
jupyter lab --port=8888 --ip 0.0.0.0 --allow-root

3
docs/advanced-hyperopt.md
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@ -17,6 +17,7 @@ from typing import Any, Dict
from pandas import DataFrame
from freqtrade.constants import Config
from freqtrade.optimize.hyperopt import IHyperOptLoss
TARGET_TRADES = 600
@ -31,7 +32,7 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
config: Dict, processed: Dict[str, DataFrame],
config: Config, processed: Dict[str, DataFrame],
backtest_stats: Dict[str, Any],
*args, **kwargs) -> float:
"""

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26
docs/backtesting.md
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@ -107,7 +107,7 @@ Strategy arguments:
## Test your strategy with Backtesting
Now you have good Buy and Sell strategies and some historic data, you want to test it against
Now you have good Entry and exit strategies and some historic data, you want to test it against
real data. This is what we call [backtesting](https://en.wikipedia.org/wiki/Backtesting).
Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHLCV) data from `user_data/data/<exchange>` by default.
@ -215,7 +215,7 @@ Sometimes your account has certain fee rebates (fee reductions starting with a c
To account for this in backtesting, you can use the `--fee` command line option to supply this value to backtesting.
This fee must be a ratio, and will be applied twice (once for trade entry, and once for trade exit).
For example, if the buying and selling commission fee is 0.1% (i.e., 0.001 written as ratio), then you would run backtesting as the following:
For example, if the commission fee per order is 0.1% (i.e., 0.001 written as ratio), then you would run backtesting as the following:
```bash
freqtrade backtesting --fee 0.001
@ -252,9 +252,9 @@ The most important in the backtesting is to understand the result.
A backtesting result will look like that:
```
========================================================= BACKTESTING REPORT ==========================================================
| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins Draws Loss Win% |
|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:-------------|-------------------------:|
========================================================= BACKTESTING REPORT =========================================================
| Pair | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins Draws Loss Win% |
|:---------|--------:|---------------:|---------------:|-----------------:|---------------:|:-------------|-------------------------:|
| ADA/BTC | 35 | -0.11 | -3.88 | -0.00019428 | -1.94 | 4:35:00 | 14 0 21 40.0 |
| ARK/BTC | 11 | -0.41 | -4.52 | -0.00022647 | -2.26 | 2:03:00 | 3 0 8 27.3 |
| BTS/BTC | 32 | 0.31 | 9.78 | 0.00048938 | 4.89 | 5:05:00 | 18 0 14 56.2 |
@ -275,15 +275,15 @@ A backtesting result will look like that:
| ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 0 15 31.8 |
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 0 243 43.4 |
========================================================= EXIT REASON STATS ==========================================================
| Exit Reason | Sells | Wins | Draws | Losses |
| Exit Reason | Exits | Wins | Draws | Losses |
|:-------------------|--------:|------:|-------:|--------:|
| trailing_stop_loss | 205 | 150 | 0 | 55 |
| stop_loss | 166 | 0 | 0 | 166 |
| exit_signal | 56 | 36 | 0 | 20 |
| force_exit | 2 | 0 | 0 | 2 |
====================================================== LEFT OPEN TRADES REPORT ======================================================
| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Win Draw Loss Win% |
|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|--------------------:|
| Pair | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Win Draw Loss Win% |
|:---------|---------:|---------------:|---------------:|-----------------:|---------------:|:---------------|--------------------:|
| ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 0 0 100 |
| LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 0 0 100 |
| TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 0 0 100 |
@ -356,7 +356,7 @@ The column `Avg Profit %` shows the average profit for all trades made while the
The column `Tot Profit %` shows instead the total profit % in relation to the starting balance.
In the above results, we have a starting balance of 0.01 BTC and an absolute profit of 0.00762792 BTC - so the `Tot Profit %` will be `(0.00762792 / 0.01) * 100 ~= 76.2%`.
Your strategy performance is influenced by your buy strategy, your exit strategy, and also by the `minimal_roi` and `stop_loss` you have set.
Your strategy performance is influenced by your entry strategy, your exit strategy, and also by the `minimal_roi` and `stop_loss` you have set.
For example, if your `minimal_roi` is only `"0": 0.01` you cannot expect the bot to make more profit than 1% (because it will exit every time a trade reaches 1%).
@ -515,7 +515,7 @@ You can then load the trades to perform further analysis as shown in the [data a
Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
- Exchange [trading limits](#trading-limits-in-backtesting) are respected
- Buys happen at open-price
- Entries happen at open-price
- All orders are filled at the requested price (no slippage, no unfilled orders)
- Exit-signal exits happen at open-price of the consecutive candle
- Exit-signal is favored over Stoploss, because exit-signals are assumed to trigger on candle's open
@ -612,9 +612,9 @@ There will be an additional table comparing win/losses of the different strategi
Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy.
```
=========================================================== STRATEGY SUMMARY =========================================================================
| Strategy | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses | Drawdown % |
|:------------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|-----------:|
=========================================================== STRATEGY SUMMARY ===========================================================================
| Strategy | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses | Drawdown % |
|:------------|---------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|-----------:|
| Strategy1 | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 | 45.2 |
| Strategy2 | 1487 | -0.13 | -197.58 | -0.00988917 | -98.79 | 4:43:00 | 662 | 0 | 825 | 241.68 |
```

71
docs/configuration.md
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@ -58,9 +58,20 @@ This is similar to using multiple `--config` parameters, but simpler in usage as
!!! Tip "Use multiple configuration files to keep secrets secret"
You can use a 2nd configuration file containing your secrets. That way you can share your "primary" configuration file, while still keeping your API keys for yourself.
The 2nd file should only specify what you intend to override.
If a key is in more than one of the configurations, then the "last specified configuration" wins (in the above example, `config-private.json`).
For one-off commands, you can also use the below syntax by specifying multiple "--config" parameters.
``` bash
freqtrade trade --config user_data/config1.json --config user_data/config-private.json <...>
```
The below is equivalent to the example above - but having 2 configuration files in the configuration, for easier reuse.
``` json title="user_data/config.json"
"add_config_files": [
"config1.json",
"config-private.json"
]
```
@ -69,17 +80,6 @@ This is similar to using multiple `--config` parameters, but simpler in usage as
freqtrade trade --config user_data/config.json <...>
```
The 2nd file should only specify what you intend to override.
If a key is in more than one of the configurations, then the "last specified configuration" wins (in the above example, `config-private.json`).
For one-off commands, you can also use the below syntax by specifying multiple "--config" parameters.
``` bash
freqtrade trade --config user_data/config.json --config user_data/config-private.json <...>
```
This is equivalent to the example above - but `config-private.json` is specified as cli argument.
??? Note "config collision handling"
If the same configuration setting takes place in both `config.json` and `config-import.json`, then the parent configuration wins.
In the below case, `max_open_trades` would be 3 after the merging - as the reusable "import" configuration has this key overwritten.
@ -111,6 +111,8 @@ This is similar to using multiple `--config` parameters, but simpler in usage as
}
```
If multiple files are in the `add_config_files` section, then they will be assumed to be at identical levels, having the last occurrence override the earlier config (unless a parent already defined such a key).
## Configuration parameters
The table below will list all configuration parameters available.
@ -223,14 +225,16 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `webhook.webhookexitcancel` | Payload to send on exit order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| `webhook.webhookexitfill` | Payload to send on exit order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
| | **Rest API / FreqUI**
| | **Rest API / FreqUI / Producer-Consumer**
| `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** Boolean
| `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** IPv4
| `api_server.listen_port` | Bind Port. See the [API Server documentation](rest-api.md) for more details. <br>**Datatype:** Integer between 1024 and 65535
| `api_server.verbosity` | Logging verbosity. `info` will print all RPC Calls, while "error" will only display errors. <br>**Datatype:** Enum, either `info` or `error`. Defaults to `info`.
| `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
| `api_server.password` | Password for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
| `api_server.ws_token` | API token for the Message WebSocket. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
| `bot_name` | Name of the bot. Passed via API to a client - can be shown to distinguish / name bots.<br> *Defaults to `freqtrade`*<br> **Datatype:** String
| `external_message_consumer` | Enable [Producer/Consumer mode](producer-consumer.md) for more details. <br> **Datatype:** Dict
| | **Other**
| `initial_state` | Defines the initial application state. If set to stopped, then the bot has to be explicitly started via `/start` RPC command. <br>*Defaults to `stopped`.* <br> **Datatype:** Enum, either `stopped` or `running`
| `force_entry_enable` | Enables the RPC Commands to force a Trade entry. More information below. <br> **Datatype:** Boolean
@ -525,21 +529,28 @@ It means if the order is not executed immediately AND fully then it is cancelled
It is the same as FOK (above) except it can be partially fulfilled. The remaining part
is automatically cancelled by the exchange.
The `order_time_in_force` parameter contains a dict with buy and sell time in force policy values.
**PO (Post only):**
Post only order. The order is either placed as a maker order, or it is canceled.
This means the order must be placed on orderbook for at at least time in an unfilled state.
#### time_in_force config
The `order_time_in_force` parameter contains a dict with entry and exit time in force policy values.
This can be set in the configuration file or in the strategy.
Values set in the configuration file overwrites values set in the strategy.
The possible values are: `gtc` (default), `fok` or `ioc`.
The possible values are: `GTC` (default), `FOK` or `IOC`.
``` python
"order_time_in_force": {
"entry": "gtc",
"exit": "gtc"
"entry": "GTC",
"exit": "GTC"
},
```
!!! Warning
This is ongoing work. For now, it is supported only for binance and kucoin.
This is ongoing work. For now, it is supported only for binance, gate, ftx and kucoin.
Please don't change the default value unless you know what you are doing and have researched the impact of using different values for your particular exchange.
### What values can be used for fiat_display_currency?
@ -650,17 +661,7 @@ You should also make sure to read the [Exchanges](exchanges.md) section of the d
### Using proxy with Freqtrade
To use a proxy with freqtrade, add the kwarg `"aiohttp_trust_env"=true` to the `"ccxt_async_kwargs"` dict in the exchange section of the configuration.
An example for this can be found in `config_examples/config_full.example.json`
``` json
"ccxt_async_config": {
"aiohttp_trust_env": true
}
```
Then, export your proxy settings using the variables `"HTTP_PROXY"` and `"HTTPS_PROXY"` set to the appropriate values
To use a proxy with freqtrade, export your proxy settings using the variables `"HTTP_PROXY"` and `"HTTPS_PROXY"` set to the appropriate values.
``` bash
export HTTP_PROXY="http://addr:port"
@ -668,6 +669,20 @@ export HTTPS_PROXY="http://addr:port"
freqtrade
```
#### Proxy just exchange requests
To use a proxy just for exchange connections (skips/ignores telegram and coingecko) - you can also define the proxies as part of the ccxt configuration.
``` json
"ccxt_config": {
"aiohttp_proxy": "http://addr:port",
"proxies": {
"http": "http://addr:port",
"https": "http://addr:port"
},
}
```
## Next step
Now you have configured your config.json, the next step is to [start your bot](bot-usage.md).

118
docs/data-download.md
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@ -25,9 +25,8 @@ usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[--include-inactive-pairs]
[--timerange TIMERANGE] [--dl-trades]
[--exchange EXCHANGE]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
[--erase]
[--data-format-ohlcv {json,jsongz,hdf5}]
[-t TIMEFRAMES [TIMEFRAMES ...]] [--erase]
[--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}]
[--data-format-trades {json,jsongz,hdf5}]
[--trading-mode {spot,margin,futures}]
[--prepend]
@ -37,7 +36,8 @@ optional arguments:
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--pairs-file FILE File containing a list of pairs to download.
--pairs-file FILE File containing a list of pairs. Takes precedence over
--pairs or pairs configured in the configuration.
--days INT Download data for given number of days.
--new-pairs-days INT Download data of new pairs for given number of days.
Default: `None`.
@ -50,18 +50,18 @@ optional arguments:
as --timeframes/-t.
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
config is provided.
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
-t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
Specify which tickers to download. Space-separated
list. Default: `1m 5m`.
--erase Clean all existing data for the selected
exchange/pairs/timeframes.
--data-format-ohlcv {json,jsongz,hdf5}
--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}
Storage format for downloaded candle (OHLCV) data.
(default: `json`).
--data-format-trades {json,jsongz,hdf5}
Storage format for downloaded trades data. (default:
`jsongz`).
--trading-mode {spot,margin,futures}
--trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
Select Trading mode
--prepend Allow data prepending. (Data-appending is disabled)
@ -76,7 +76,7 @@ Common arguments:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
-d PATH, --datadir PATH, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
@ -179,9 +179,11 @@ freqtrade download-data --exchange binance --pairs ETH/USDT XRP/USDT BTC/USDT --
Freqtrade currently supports 3 data-formats for both OHLCV and trades data:
* `json` (plain "text" json files)
* `jsongz` (a gzip-zipped version of json files)
* `hdf5` (a high performance datastore)
* `json` - plain "text" json files
* `jsongz` - a gzip-zipped version of json files
* `hdf5` - a high performance datastore
* `feather` - a dataformat based on Apache Arrow
* `parquet` - columnar datastore
By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data.
@ -200,38 +202,74 @@ If the default data-format has been changed during download, then the keys `data
!!! Note
You can convert between data-formats using the [convert-data](#sub-command-convert-data) and [convert-trade-data](#sub-command-convert-trade-data) methods.
#### Dataformat comparison
The following comparisons have been made with the following data, and by using the linux `time` command.
```
Found 6 pair / timeframe combinations.
+----------+-------------+--------+---------------------+---------------------+
| Pair | Timeframe | Type | From | To |
|----------+-------------+--------+---------------------+---------------------|
| BTC/USDT | 5m | spot | 2017-08-17 04:00:00 | 2022-09-13 19:25:00 |
| ETH/USDT | 1m | spot | 2017-08-17 04:00:00 | 2022-09-13 19:26:00 |
| BTC/USDT | 1m | spot | 2017-08-17 04:00:00 | 2022-09-13 19:30:00 |
| XRP/USDT | 5m | spot | 2018-05-04 08:10:00 | 2022-09-13 19:15:00 |
| XRP/USDT | 1m | spot | 2018-05-04 08:11:00 | 2022-09-13 19:22:00 |
| ETH/USDT | 5m | spot | 2017-08-17 04:00:00 | 2022-09-13 19:20:00 |
+----------+-------------+--------+---------------------+---------------------+
```
Timings have been taken in a not very scientific way with the following command, which forces reading the data into memory.
``` bash
time freqtrade list-data --show-timerange --data-format-ohlcv <dataformat>
```
| Format | Size | timing |
|------------|-------------|-------------|
| `json` | 149Mb | 25.6s |
| `jsongz` | 39Mb | 27s |
| `hdf5` | 145Mb | 3.9s |
| `feather` | 72Mb | 3.5s |
| `parquet` | 83Mb | 3.8s |
Size has been taken from the BTC/USDT 1m spot combination for the timerange specified above.
To have a best performance/size mix, we recommend the use of either feather or parquet.
#### Sub-command convert data
```
usage: freqtrade convert-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]] --format-from
{json,jsongz,hdf5} --format-to
{json,jsongz,hdf5} [--erase]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
{json,jsongz,hdf5,feather,parquet} --format-to
{json,jsongz,hdf5,feather,parquet} [--erase]
[--exchange EXCHANGE]
[-t TIMEFRAMES [TIMEFRAMES ...]]
[--trading-mode {spot,margin,futures}]
[--candle-types {spot,,futures,mark,index,premiumIndex,funding_rate} [{spot,,futures,mark,index,premiumIndex,funding_rate} ...]]
[--candle-types {spot,futures,mark,index,premiumIndex,funding_rate} [{spot,futures,mark,index,premiumIndex,funding_rate} ...]]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--format-from {json,jsongz,hdf5}
--format-from {json,jsongz,hdf5,feather,parquet}
Source format for data conversion.
--format-to {json,jsongz,hdf5}
--format-to {json,jsongz,hdf5,feather,parquet}
Destination format for data conversion.
--erase Clean all existing data for the selected
exchange/pairs/timeframes.
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
Specify which tickers to download. Space-separated
list. Default: `1m 5m`.
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
config is provided.
--trading-mode {spot,margin,futures}
-t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
Specify which tickers to download. Space-separated
list. Default: `1m 5m`.
--trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
Select Trading mode
--candle-types {spot,,futures,mark,index,premiumIndex,funding_rate} [{spot,,futures,mark,index,premiumIndex,funding_rate} ...]
--candle-types {spot,futures,mark,index,premiumIndex,funding_rate} [{spot,futures,mark,index,premiumIndex,funding_rate} ...]
Select candle type to use
Common arguments:
@ -245,7 +283,7 @@ Common arguments:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
-d PATH, --datadir PATH, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
@ -267,20 +305,24 @@ freqtrade convert-data --format-from json --format-to jsongz --datadir ~/.freqtr
usage: freqtrade convert-trade-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]] --format-from
{json,jsongz,hdf5} --format-to
{json,jsongz,hdf5} [--erase]
{json,jsongz,hdf5,feather,parquet}
--format-to
{json,jsongz,hdf5,feather,parquet}
[--erase] [--exchange EXCHANGE]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-
Limit command to these pairs. Pairs are space-
separated.
--format-from {json,jsongz,hdf5}
--format-from {json,jsongz,hdf5,feather,parquet}
Source format for data conversion.
--format-to {json,jsongz,hdf5}
--format-to {json,jsongz,hdf5,feather,parquet}
Destination format for data conversion.
--erase Clean all existing data for the selected
exchange/pairs/timeframes.
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
config is provided.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
@ -293,7 +335,7 @@ Common arguments:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
-d PATH, --datadir PATH, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
@ -318,9 +360,9 @@ This command will allow you to repeat this last step for additional timeframes w
usage: freqtrade trades-to-ohlcv [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
[-t TIMEFRAMES [TIMEFRAMES ...]]
[--exchange EXCHANGE]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}]
[--data-format-trades {json,jsongz,hdf5}]
optional arguments:
@ -328,12 +370,12 @@ optional arguments:
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
-t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
Specify which tickers to download. Space-separated
list. Default: `1m 5m`.
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
config is provided.
--data-format-ohlcv {json,jsongz,hdf5}
--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}
Storage format for downloaded candle (OHLCV) data.
(default: `json`).
--data-format-trades {json,jsongz,hdf5}
@ -351,7 +393,7 @@ Common arguments:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
-d PATH, --datadir PATH, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
@ -371,7 +413,7 @@ You can get a list of downloaded data using the `list-data` sub-command.
```
usage: freqtrade list-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [--exchange EXCHANGE]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}]
[-p PAIRS [PAIRS ...]]
[--trading-mode {spot,margin,futures}]
[--show-timerange]
@ -380,13 +422,13 @@ optional arguments:
-h, --help show this help message and exit
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
config is provided.
--data-format-ohlcv {json,jsongz,hdf5}
--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}
Storage format for downloaded candle (OHLCV) data.
(default: `json`).
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--trading-mode {spot,margin,futures}
--trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
Select Trading mode
--show-timerange Show timerange available for available data. (May take
a while to calculate).
@ -402,7 +444,7 @@ Common arguments:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
-d PATH, --datadir PATH, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.

5
docs/developer.md
View File

@ -409,8 +409,9 @@ Determine if crucial bugfixes have been made between this commit and the current
* Merge the release branch (stable) into this branch.
* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7.1` should we need to do a second release that month. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi.
* Commit this part
* push that branch to the remote and create a PR against the stable branch
* Commit this part.
* push that branch to the remote and create a PR against the stable branch.
* Update develop version to next version following the pattern `2019.8-dev`.
### Create changelog from git commits

15
docs/exchanges.md
View File

@ -57,12 +57,13 @@ This configuration enables kraken, as well as rate-limiting to avoid bans from t
Binance supports [time_in_force](configuration.md#understand-order_time_in_force).
!!! Tip "Stoploss on Exchange"
Binance supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange..
Binance supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
On futures, Binance supports both `stop-limit` as well as `stop-market` orders. You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
### Binance Blacklist
For Binance, please add `"BNB/<STAKE>"` to your blacklist to avoid issues.
Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB trade unsellable as the expected amount is not there anymore.
For Binance, it is suggested to add `"BNB/<STAKE>"` to your blacklist to avoid issues, unless you are willing to maintain enough extra `BNB` on the account or unless you're willing to disable using `BNB` for fees.
Binance accounts may use `BNB` for fees, and if a trade happens to be on `BNB`, further trades may consume this position and make the initial BNB trade unsellable as the expected amount is not there anymore.
### Binance Futures
@ -205,8 +206,8 @@ Kucoin supports [time_in_force](configuration.md#understand-order_time_in_force)
### Kucoin Blacklists
For Kucoin, please add `"KCS/<STAKE>"` to your blacklist to avoid issues.
Accounts having KCS accounts use this to pay for fees - if your first trade happens to be on `KCS`, further trades will consume this position and make the initial KCS trade unsellable as the expected amount is not there anymore.
For Kucoin, it is suggested to add `"KCS/<STAKE>"` to your blacklist to avoid issues, unless you are willing to maintain enough extra `KCS` on the account or unless you're willing to disable using `KCS` for fees.
Kucoin accounts may use `KCS` for fees, and if a trade happens to be on `KCS`, further trades may consume this position and make the initial `KCS` trade unsellable as the expected amount is not there anymore.
## Huobi
@ -232,7 +233,7 @@ OKX requires a passphrase for each api key, you will therefore need to add this
!!! Warning "Futures"
OKX Futures has the concept of "position mode" - which can be Net or long/short (hedge mode).
Freqtrade supports both modes - but changing the mode mid-trading is not supported and will lead to exceptions and failures to place trades.
Freqtrade supports both modes (we recommend to use net mode) - but changing the mode mid-trading is not supported and will lead to exceptions and failures to place trades.
OKX also only provides MARK candles for the past ~3 months. Backtesting futures prior to that date will therefore lead to slight deviations, as funding-fees cannot be calculated correctly without this data.
## Gate.io
@ -278,7 +279,7 @@ For example, to test the order type `FOK` with Kraken, and modify candle limit t
"exchange": {
"name": "kraken",
"_ft_has_params": {
"order_time_in_force": ["gtc", "fok"],
"order_time_in_force": ["GTC", "FOK"],
"ohlcv_candle_limit": 200
}
//...

15
docs/faq.md
View File

@ -4,7 +4,7 @@
Freqtrade supports spot trading only.
### Can I open short positions?
### Can my bot open short positions?
Freqtrade can open short positions in futures markets.
This requires the strategy to be made for this - and `"trading_mode": "futures"` in the configuration.
@ -12,9 +12,9 @@ Please make sure to read the [relevant documentation page](leverage.md) first.
In spot markets, you can in some cases use leveraged spot tokens, which reflect an inverted pair (eg. BTCUP/USD, BTCDOWN/USD, ETHBULL/USD, ETHBEAR/USD,...) which can be traded with Freqtrade.
### Can I trade options or futures?
### Can my bot trade options or futures?
Futures trading is supported for selected exchanges.
Futures trading is supported for selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an uptodate list of supported exchanges.
## Beginner Tips & Tricks
@ -22,6 +22,13 @@ Futures trading is supported for selected exchanges.
## Freqtrade common issues
### Can freqtrade open multiple positions on the same pair in parallel?
No. Freqtrade will only open one position per pair at a time.
You can however use the [`adjust_trade_position()` callback](strategy-callbacks.md#adjust-trade-position) to adjust an open position.
Backtesting provides an option for this in `--eps` - however this is only there to highlight "hidden" signals, and will not work in live.
### The bot does not start
Running the bot with `freqtrade trade --config config.json` shows the output `freqtrade: command not found`.
@ -30,7 +37,7 @@ This could be caused by the following reasons:
* The virtual environment is not active.
* Run `source .env/bin/activate` to activate the virtual environment.
* The installation did not work correctly.
* The installation did not complete successfully.
* Please check the [Installation documentation](installation.md).
### I have waited 5 minutes, why hasn't the bot made any trades yet?

217
docs/freqai-configuration.md Normal file
View File

@ -0,0 +1,217 @@
# Configuration
`FreqAI` is configured through the typical [Freqtrade config file](configuration.md) and the standard [Freqtrade strategy](strategy-customization.md). Examples of `FreqAI` config and strategy files can be found in `config_examples/config_freqai.example.json` and `freqtrade/templates/FreqaiExampleStrategy.py`, respectively.
## Setting up the configuration file
Although there are plenty of additional parameters to choose from, as highlighted in the [parameter table](freqai-parameter-table.md#parameter-table), a `FreqAI` config must at minimum include the following parameters (the parameter values are only examples):
```json
"freqai": {
"enabled": true,
"purge_old_models": true,
"train_period_days": 30,
"backtest_period_days": 7,
"identifier" : "unique-id",
"feature_parameters" : {
"include_timeframes": ["5m","15m","4h"],
"include_corr_pairlist": [
"ETH/USD",
"LINK/USD",
"BNB/USD"
],
"label_period_candles": 24,
"include_shifted_candles": 2,
"indicator_periods_candles": [10, 20]
},
"data_split_parameters" : {
"test_size": 0.25
},
"model_training_parameters" : {
"n_estimators": 100
},
}
```
A full example config is available in `config_examples/config_freqai.example.json`.
## Building a `FreqAI` strategy
The `FreqAI` strategy requires including the following lines of code in the standard [Freqtrade strategy](strategy-customization.md):
```python
# user should define the maximum startup candle count (the largest number of candles
# passed to any single indicator)
startup_candle_count: int = 20
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# the model will return all labels created by user in `populate_any_indicators`
# (& appended targets), an indication of whether or not the prediction should be accepted,
# the target mean/std values for each of the labels created by user in
# `populate_any_indicators()` for each training period.
dataframe = self.freqai.start(dataframe, metadata, self)
return dataframe
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
"""
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + coin `
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
:param coin: the name of the coin which will modify the feature names.
"""
coin = pair.split('/')[0]
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
)
return df
```
Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/targets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
Notice also the location of the labels under `if set_generalized_indicators:` at the bottom of the example. This is where single features and labels/targets should be added to the feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`.
!!! Note
The `self.freqai.start()` function cannot be called outside the `populate_indicators()`.
!!! Note
Features **must** be defined in `populate_any_indicators()`. Defining `FreqAI` features in `populate_indicators()`
will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, the following structure inside `populate_any_indicators()` should be used
(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`):
```python
def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False):
...
# Add generalized indicators here (because in live, it will call only this function to populate
# indicators for retraining). Notice how we ensure not to add them multiple times by associating
# these generalized indicators to the basepair/timeframe
if set_generalized_indicators:
df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
)
```
Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`.
## Important dataframe key patterns
Below are the values you can expect to include/use inside a typical strategy dataframe (`df[]`):
| DataFrame Key | Description |
|------------|-------------|
| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside `FreqAI` (typically following the naming convention `&-s*`). The names of these dataframe columns are fed back as the predictions. For example, to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`), you would set `df['&-s_close']`. `FreqAI` makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
| `df['&*_std/mean']` | Standard deviation and mean values of the defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`). <br> **Datatype:** Float.
| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -1 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, `FreqAI` will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -1 and 2.
| `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence `FreqAI` has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Float.
| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from `FreqAI`. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
## Setting the `startup_candle_count`
The `startup_candle_count` in the `FreqAI` strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., Ta-Lib functions). In the presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`.
!!! Note
There are instances where the Ta-Lib functions actually require more data than just the passed `period` or else the feature dataset gets populated with NaNs. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Hence, it is typically safest to multiply the expected `startup_candle_count` by 2. Look out for this log message to confirm that the data is clean:
```
2022-08-31 15:14:04 - freqtrade.freqai.data_kitchen - INFO - dropped 0 training points due to NaNs in populated dataset 4319.
```
## Creating a dynamic target threshold
Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. `FreqAI` allows you to return additional information from the training of a model (more info [here](freqai-feature-engineering.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the target/label *during the most recent training*. Comparing a given prediction to these values allows you to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out.
```python
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
```
To consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, you would set `fit_live_prediction_candles` in the config to the number of historical prediction candles you wish to use to generate target statistics.
```json
"freqai": {
"fit_live_prediction_candles": 300,
}
```
If this value is set, `FreqAI` will initially use the predictions from the training data and subsequently begin introducing real prediction data as it is generated. `FreqAI` will save this historical data to be reloaded if you stop and restart a model with the same `identifier`.
## Using different prediction models
`FreqAI` has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `Catboost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`. However, it is possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to let these customize various aspects of the training procedures.
### Setting classifier targets
`FreqAI` includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example:
```python
df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
```
Additionally, the example classifier models do not accommodate multiple labels, but they do allow multi-class classification within a single label column.

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# Development
## Project architecture
The architecture and functions of `FreqAI` are generalized to encourages development of unique features, functions, models, etc.
The class structure and a detailed algorithmic overview is depicted in the following diagram:
![image](assets/freqai_algorithm-diagram.jpg)
As shown, there are three distinct objects comprising `FreqAI`:
* **IFreqaiModel** - A singular persistent object containing all the necessary logic to collect, store, and process data, engineer features, run training, and inference models.
* **FreqaiDataKitchen** - A non-persistent object which is created uniquely for each unique asset/model. Beyond metadata, it also contains a variety of data processing tools.
* **FreqaiDataDrawer** - A singular persistent object containing all the historical predictions, models, and save/load methods.
There are a variety of built-in [prediction models](freqai-configuration.md#using-different-prediction-models) which inherit directly from `IFreqaiModel`. Each of these models have full access to all methods in `IFreqaiModel` and can therefore override any of those functions at will. However, advanced users will likely stick to overriding `fit()`, `train()`, `predict()`, and `data_cleaning_train/predict()`.
## Data handling
`FreqAI` aims to organize model files, prediction data, and meta data in a way that simplifies post-processing and enhances crash resilience by automatic data reloading. The data is saved in a file structure,`user_data_dir/models/`, which contains all the data associated with the trainings and backtests. The `FreqaiDataKitchen()` relies heavily on the file structure for proper training and inferencing and should therefore not be manually modified.
### File structure
The file structure is automatically generated based on the model `identifier` set in the [config](freqai-configuration.md#setting-up-the-configuration-file). The following structure shows where the data is stored for post processing:
| Structure | Description |
|-----------|-------------|
| `config_*.json` | A copy of the model specific configuration file. |
| `historic_predictions.pkl` | A file containing all historic predictions generated during the lifetime of the `identifier` model during live deployment. `historic_predictions.pkl` is used to reload the model after a crash or a config change. A backup file is always held incase of corruption on the main file. **`FreqAI` automatically detects corruption and replaces the corrupted file with the backup**. |
| `pair_dictionary.json` | A file containing the training queue as well as the on disk location of the most recently trained model. |
| `sub-train-*_TIMESTAMP` | A folder containing all the files associated with a single model, such as: <br>
|| `*_metadata.json` - Metadata for the model, such as normalization max/mins, expected training feature list, etc. <br>
|| `*_model.*` - The model file saved to disk for reloading from a crash. Can be `joblib` (typical boosting libs), `zip` (stable_baselines), `hd5` (keras type), etc. <br>
|| `*_pca_object.pkl` - The [Principal component analysis (PCA)](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis) transform (if `principal_component_analysis: true` is set in the config) which will be used to transform unseen prediction features. <br>
|| `*_svm_model.pkl` - The [Support Vector Machine (SVM)](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm) model which is used to detect outliers in unseen prediction features. <br>
|| `*_trained_df.pkl` - The dataframe containing all the training features used to train the `identifier` model. This is used for computing the [Dissimilarity Index (DI)](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di) and can also be used for post-processing. <br>
|| `*_trained_dates.df.pkl` - The dates associated with the `trained_df.pkl`, which is useful for post-processing. |
The example file structure would look like this:
```
├── models
│   └── unique-id
│   ├── config_freqai.example.json
│   ├── historic_predictions.backup.pkl
│   ├── historic_predictions.pkl
│   ├── pair_dictionary.json
│   ├── sub-train-1INCH_1662821319
│   │   ├── cb_1inch_1662821319_metadata.json
│   │   ├── cb_1inch_1662821319_model.joblib
│   │   ├── cb_1inch_1662821319_pca_object.pkl
│   │   ├── cb_1inch_1662821319_svm_model.joblib
│   │   ├── cb_1inch_1662821319_trained_dates_df.pkl
│   │   └── cb_1inch_1662821319_trained_df.pkl
│   ├── sub-train-1INCH_1662821371
│   │   ├── cb_1inch_1662821371_metadata.json
│   │   ├── cb_1inch_1662821371_model.joblib
│   │   ├── cb_1inch_1662821371_pca_object.pkl
│   │   ├── cb_1inch_1662821371_svm_model.joblib
│   │   ├── cb_1inch_1662821371_trained_dates_df.pkl
│   │   └── cb_1inch_1662821371_trained_df.pkl
│   ├── sub-train-ADA_1662821344
│   │   ├── cb_ada_1662821344_metadata.json
│   │   ├── cb_ada_1662821344_model.joblib
│   │   ├── cb_ada_1662821344_pca_object.pkl
│   │   ├── cb_ada_1662821344_svm_model.joblib
│   │   ├── cb_ada_1662821344_trained_dates_df.pkl
│   │   └── cb_ada_1662821344_trained_df.pkl
│   └── sub-train-ADA_1662821399
│   ├── cb_ada_1662821399_metadata.json
│   ├── cb_ada_1662821399_model.joblib
│   ├── cb_ada_1662821399_pca_object.pkl
│   ├── cb_ada_1662821399_svm_model.joblib
│   ├── cb_ada_1662821399_trained_dates_df.pkl
│   └── cb_ada_1662821399_trained_df.pkl
```

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# Feature engineering
## Defining the features
Low level feature engineering is performed in the user strategy within a function called `populate_any_indicators()`. That function sets the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. One important syntax rule is that all `base features` string names are prepended with `%`, while labels/targets are prepended with `&`.
Meanwhile, high level feature engineering is handled within `"feature_parameters":{}` in the `FreqAI` config. Within this file, it is possible to decide large scale feature expansions on top of the `base_features` such as "including correlated pairs" or "including informative timeframes" or even "including recent candles."
It is advisable to start from the template `populate_any_indicators()` in the source provided example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy:
```python
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
"""
Function designed to automatically generate, name, and merge features
from user-indicated timeframes in the configuration file. The user controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + coin `
(see convention below). I.e., the user should not prepend any supporting metrics
(e.g., bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
:param coin: the name of the coin which will modify the feature names.
"""
coin = pair.split('/')[0]
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=t, stds=2.2
)
informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
informative[f"%-{coin}bb_width-period_{t}"] = (
informative[f"{coin}bb_upperband-period_{t}"]
- informative[f"{coin}bb_lowerband-period_{t}"]
) / informative[f"{coin}bb_middleband-period_{t}"]
informative[f"%-{coin}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
)
informative[f"%-{coin}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
)
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
)
return df
```
In the presented example, the user does not wish to pass the `bb_lowerband` as a feature to the model,
and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the
model for training/prediction and has therefore prepended it with `%`.
After having defined the `base features`, the next step is to expand upon them using the powerful `feature_parameters` in the configuration file:
```json
"freqai": {
//...
"feature_parameters" : {
"include_timeframes": ["5m","15m","4h"],
"include_corr_pairlist": [
"ETH/USD",
"LINK/USD",
"BNB/USD"
],
"label_period_candles": 24,
"include_shifted_candles": 2,
"indicator_periods_candles": [10, 20]
},
//...
}
```
The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `populate_any_indicators()` in the strategy. In the presented case, the user is asking for the `5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set.
You can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example).
`include_shifted_candles` indicates the number of previous candles to include in the feature set. For example, `include_shifted_candles: 2` tells `FreqAI` to include the past 2 candles for each of the features in the feature set.
In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
$= 3 * 3 * 3 * 2 * 2 = 108$.
### Returning additional info from training
Important metrics can be returned to the strategy at the end of each model training by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside the custom prediction model class.
`FreqAI` takes the `my_new_value` assigned in this dictionary and expands it to fit the dataframe that is returned to the strategy. You can then use the returned metrics in your strategy through `dataframe['my_new_value']`. An example of how return values can be used in `FreqAI` are the `&*_mean` and `&*_std` values that are used to [created a dynamic target threshold](freqai-configuration.md#creating-a-dynamic-target-threshold).
Another example, where the user wants to use live metrics from the trade database, is shown below:
```json
"freqai": {
"extra_returns_per_train": {"total_profit": 4}
}
```
You need to set the standard dictionary in the config so that `FreqAI` can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, the preset values are what will be returned.
## Feature normalization
`FreqAI` is strict when it comes to data normalization. The train features, $X^{train}$, are always normalized to [-1, 1] using a shifted min-max normalization:
$$X^{train}_{norm} = 2 * \frac{X^{train} - X^{train}.min()}{X^{train}.max() - X^{train}.min()} - 1$$
All other data (test data and unseen prediction data in dry/live/backtest) is always automatically normalized to the training feature space according to industry standards. `FreqAI` stores all the metadata required to ensure that test and prediction features will be properly normalized and that predictions are properly denormalized. For this reason, it is not recommended to eschew industry standards and modify `FreqAI` internals - however - advanced users can do so by inheriting `train()` in their custom `IFreqaiModel` and using their own normalization functions.
## Data dimensionality reduction with Principal Component Analysis
You can reduce the dimensionality of your features by activating the `principal_component_analysis` in the config:
```json
"freqai": {
"feature_parameters" : {
"principal_component_analysis": true
}
}
```
This will perform PCA on the features and reduce their dimensionality so that the explained variance of the data set is >= 0.999. Reducing data dimensionality makes training the model faster and hence allows for more up-to-date models.
## Inlier metric
The `inlier_metric` is a metric aimed at quantifying how similar a the features of a data point are to the most recent historic data points.
You define the lookback window by setting `inlier_metric_window` and `FreqAI` computes the distance between the present time point and each of the previous `inlier_metric_window` lookback points. A Weibull function is fit to each of the lookback distributions and its cumulative distribution function (CDF) is used to produce a quantile for each lookback point. The `inlier_metric` is then computed for each time point as the average of the corresponding lookback quantiles. The figure below explains the concept for an `inlier_metric_window` of 5.
![inlier-metric](assets/freqai_inlier-metric.jpg)
`FreqAI` adds the `inlier_metric` to the training features and hence gives the model access to a novel type of temporal information.
This function does **not** remove outliers from the data set.
## Weighting features for temporal importance
`FreqAI` allows you to set a `weight_factor` to weight recent data more strongly than past data via an exponential function:
$$ W_i = \exp(\frac{-i}{\alpha*n}) $$
where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. Below is a figure showing the effect of different weight factors on the data points in a feature set.
![weight-factor](assets/freqai_weight-factor.jpg)
## Outlier detection
Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. `FreqAI` implements a variety of methods to identify such outliers and hence mitigate risk.
### Identifying outliers with the Dissimilarity Index (DI)
The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction made by the model.
You can tell `FreqAI` to remove outlier data points from the training/test data sets using the DI by including the following statement in the config:
```json
"freqai": {
"feature_parameters" : {
"DI_threshold": 1
}
}
```
The DI allows predictions which are outliers (not existent in the model feature space) to be thrown out due to low levels of certainty. To do so, `FreqAI` measures the distance between each training data point (feature vector), $X_{a}$, and all other training data points:
$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$
where $d_{ab}$ is the distance between the normalized points $a$ and $b$, and $p$ is the number of features, i.e., the length of the vector $X$. The characteristic distance, $\overline{d}$, for a set of training data points is simply the mean of the average distances:
$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$
$\overline{d}$ quantifies the spread of the training data, which is compared to the distance between a new prediction feature vectors, $X_k$ and all the training data:
$$ d_k = \arg \min d_{k,i} $$
This enables the estimation of the Dissimilarity Index as:
$$ DI_k = d_k/\overline{d} $$
You can tweak the DI through the `DI_threshold` to increase or decrease the extrapolation of the trained model. A higher `DI_threshold` means that the DI is more lenient and allows predictions further away from the training data to be used whilst a lower `DI_threshold` has the opposite effect and hence discards more predictions.
Below is a figure that describes the DI for a 3D data set.
![DI](assets/freqai_DI.jpg)
### Identifying outliers using a Support Vector Machine (SVM)
You can tell `FreqAI` to remove outlier data points from the training/test data sets using a Support Vector Machine (SVM) by including the following statement in the config:
```json
"freqai": {
"feature_parameters" : {
"use_SVM_to_remove_outliers": true
}
}
```
The SVM will be trained on the training data and any data point that the SVM deems to be beyond the feature space will be removed.
`FreqAI` uses `sklearn.linear_model.SGDOneClassSVM` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDOneClassSVM.html) (external website)) and you can elect to provide additional parameters for the SVM, such as `shuffle`, and `nu`.
The parameter `shuffle` is by default set to `False` to ensure consistent results. If it is set to `True`, running the SVM multiple times on the same data set might result in different outcomes due to `max_iter` being to low for the algorithm to reach the demanded `tol`. Increasing `max_iter` solves this issue but causes the procedure to take longer time.
The parameter `nu`, *very* broadly, is the amount of data points that should be considered outliers and should be between 0 and 1.
### Identifying outliers with DBSCAN
You can configure `FreqAI` to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config:
```json
"freqai": {
"feature_parameters" : {
"use_DBSCAN_to_remove_outliers": true
}
}
```
DBSCAN is an unsupervised machine learning algorithm that clusters data without needing to know how many clusters there should be.
Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters the data set by setting all data points that have $N-1$ other data points within a distance of $\varepsilon$ as *core points*. A data point that is within a distance of $\varepsilon$ from a *core point* but that does not have $N-1$ other data points within a distance of $\varepsilon$ from itself is considered an *edge point*. A cluster is then the collection of *core points* and *edge points*. Data points that have no other data points at a distance $<\varepsilon$ are considered outliers. The figure below shows a cluster with $N = 3$.
![dbscan](assets/freqai_dbscan.jpg)
`FreqAI` uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) (external website)) with `min_samples` ($N$) taken as 1/4 of the no. of time points in the feature set. `eps` ($\varepsilon$) is computed automatically as the elbow point in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set.

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# Parameter table
The table below will list all configuration parameters available for `FreqAI`. Some of the parameters are exemplified in `config_examples/config_freqai.example.json`.
Mandatory parameters are marked as **Required** and have to be set in one of the suggested ways.
| Parameter | Description |
|------------|-------------|
| | **General configuration parameters**
| `freqai` | **Required.** <br> The parent dictionary containing all the parameters for controlling `FreqAI`. <br> **Datatype:** Dictionary.
| `train_period_days` | **Required.** <br> Number of days to use for the training data (width of the sliding window). <br> **Datatype:** Positive integer.
| `backtest_period_days` | **Required.** <br> Number of days to inference from the trained model before sliding the `train_period_days` window defined above, and retraining the model during backtesting (more info [here](freqai-running.md#backtesting)). This can be fractional days, but beware that the provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest. <br> **Datatype:** Float.
| `identifier` | **Required.** <br> A unique ID for the current model. If models are saved to disk, the `identifier` allows for reloading specific pre-trained models/data. <br> **Datatype:** String.
| `live_retrain_hours` | Frequency of retraining during dry/live runs. <br> **Datatype:** Float > 0. <br> Default: 0 (models retrain as often as possible).
| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old. <br> **Datatype:** Positive integer. <br> Default: 0 (models never expire).
| `purge_old_models` | Delete obsolete models. <br> **Datatype:** Boolean. <br> Default: `False` (all historic models remain on disk).
| `save_backtest_models` | Save models to disk when running backtesting. Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when you wish to tune entry/exit parameters). Saving backtesting models to disk also allows to use the same model files for starting a dry/live instance with the same model `identifier`. <br> **Datatype:** Boolean. <br> Default: `False` (no models are saved).
| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)). <br> **Datatype:** Positive integer.
| `follow_mode` | Use a `follower` that will look for models associated with a specific `identifier` and load those for inferencing. A `follower` will **not** train new models. <br> **Datatype:** Boolean. <br> Default: `False`.
| `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)). <br> **Datatype:** Boolean. <br> Default: `False`.
| | **Feature parameters**
| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](freqai-feature-engineering.md). <br> **Datatype:** Dictionary.
| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).
| `include_corr_pairlist` | A list of correlated coins that `FreqAI` will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not. <br> **Datatype:** Positive integer.
| `include_shifted_candles` | Add features from previous candles to subsequent candles with the intent of adding historical information. If used, `FreqAI` will duplicate and shift all features from the `include_shifted_candles` previous candles so that the information is available for the subsequent candle. <br> **Datatype:** Positive integer.
| `weight_factor` | Weight training data points according to their recency (see details [here](freqai-feature-engineering.md#weighting-features-for-temporal-importance)). <br> **Datatype:** Positive float (typically < 1).
| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `populate_any_indicators()` for indicator creation. `FreqAI` uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN <br> **Datatype:** Positive integer.
| `indicator_periods_candles` | Time periods to calculate indicators for. The indicators are added to the base indicator dataset. <br> **Datatype:** List of positive integers.
| `stratify_training_data` | Split the feature set into training and testing datasets. For example, `stratify_training_data: 2` would set every 2nd data point into a separate dataset to be pulled from during training/testing. See details about how it works [here](freqai-running.md#data-stratification-for-training-and-testing-the-model). <br> **Datatype:** Positive integer.
| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean. defaults to `false`.
| `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features.<br> **Datatype:** Integer, defaults to `0`.
| `DI_threshold` | Activates the use of the Dissimilarity Index for outlier detection when set to > 0. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Positive float (typically < 1).
| `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training dataset, as well as from incoming data points. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Boolean.
| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Dictionary.
| `use_DBSCAN_to_remove_outliers` | Cluster data using the DBSCAN algorithm to identify and remove outliers from training and prediction data. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan). <br> **Datatype:** Boolean.
| `inlier_metric_window` | If set, `FreqAI` adds an `inlier_metric` to the training feature set and set the lookback to be the `inlier_metric_window`, i.e., the number of previous time points to compare the current candle to. Details of how the `inlier_metric` is computed can be found [here](freqai-feature-engineering.md#inlier-metric). <br> **Datatype:** Integer. <br> Default: 0.
| `noise_standard_deviation` | If set, `FreqAI` adds noise to the training features with the aim of preventing overfitting. `FreqAI` generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in `FreqAI` is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation). <br> **Datatype:** Integer. <br> Default: 0.
| `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, `FreqAI` will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset. <br> **Datatype:** Float. <br> Default: `30`.
| `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, you should be careful to understand the unorthodox nature of this parameter before employing it. <br> **Datatype:** Boolean. <br> Default: `False` (no reversal).
| | **Data split parameters**
| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). <br> **Datatype:** Dictionary.
| `test_size` | The fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.
| `shuffle` | Shuffle the training data points during training. Typically, for time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean.
| | **Model training parameters**
| `model_training_parameters` | A flexible dictionary that includes all parameters available by the selected model library. For example, if you use `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If you select a different model, this dictionary can contain any parameter from that model. <br> **Datatype:** Dictionary.
| `n_estimators` | The number of boosted trees to fit in regression. <br> **Datatype:** Integer.
| `learning_rate` | Boosting learning rate during regression. <br> **Datatype:** Float.
| `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names. <br> **Datatype:** Float.
| | **Extraneous parameters**
| `keras` | If the selected model makes use of Keras (typical for Tensorflow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards. <br> **Datatype:** Boolean. <br> Default: `False`.
| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: 2.

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# Running FreqAI
There are two ways to train and deploy an adaptive machine learning model - live deployment and historical backtesting. In both cases, `FreqAI` runs/simulates periodic retraining of models as shown in the following figure:
![freqai-window](assets/freqai_moving-window.jpg)
## Live deployments
FreqAI can be run dry/live using the following command:
```bash
freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor
```
When launched, FreqAI will start training a new model, with a new `identifier`, based on the config settings. Following training, the model will be used to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the coin pairs to try to keep all models equally up to date. FreqAI will always use the most recently trained model to make predictions on incoming live data. If you do not want FreqAI to retrain new models as often as possible, you can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before training a new model. Additionally, you can set `expired_hours` to tell FreqAI to avoid making predictions on models that are older than that number of hours.
Trained models are by default saved to disk to allow for reuse during backtesting or after a crash. You can opt to [purge old models](#purging-old-model-data) to save disk space by setting `"purge_old_models": true` in the config.
To start a dry/live run from a saved backtest model (or from a previously crashed dry/live session), you only need to specify the `identifier` of the specific model:
```json
"freqai": {
"identifier": "example",
"live_retrain_hours": 0.5
}
```
In this case, although FreqAI will initiate with a pre-trained model, it will still check to see how much time has elapsed since the model was trained. If a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will start training a new model.
### Automatic data download
FreqAI automatically downloads the proper amount of data needed to ensure training of a model through the defined `train_period_days` and `startup_candle_count` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters).
### Saving prediction data
All predictions made during the lifetime of a specific `identifier` model are stored in `historical_predictions.pkl` to allow for reloading after a crash or changes made to the config.
### Purging old model data
FreqAI stores new model files after each successful training. These files become obsolete as new models are generated to adapt to new market conditions. If you are planning to leave FreqAI running for extended periods of time with high frequency retraining, you should enable `purge_old_models` in the config:
```json
"freqai": {
"purge_old_models": true,
}
```
This will automatically purge all models older than the two most recently trained ones to save disk space.
## Backtesting
The FreqAI backtesting module can be executed with the following command:
```bash
freqtrade backtesting --strategy FreqaiExampleStrategy --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701
```
If this command has never been executed with the existing config file, FreqAI will train a new model
for each pair, for each backtesting window within the expanded `--timerange`.
Backtesting mode requires [downloading the necessary data](#downloading-data-to-cover-the-full-backtest-period) before deployment (unlike in dry/live mode where FreqAI handles the data downloading automatically). You should be careful to consider that the time range of the downloaded data is more than the backtesting time range. This is because FreqAI needs data prior to the desired backtesting time range in order to train a model to be ready to make predictions on the first candle of the set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-size-of-the-sliding-training-window-and-backtesting-duration).
!!! Note "Model reuse"
Once the training is completed, you can execute the backtesting again with the same config file and
FreqAI will find the trained models and load them instead of spending time training. This is useful
if you want to tweak (or even hyperopt) buy and sell criteria inside the strategy. If you
*want* to retrain a new model with the same config file, you should simply change the `identifier`.
This way, you can return to using any model you wish by simply specifying the `identifier`.
---
### Saving prediction data
To allow for tweaking your strategy (**not** the features!), FreqAI will automatically save the predictions during backtesting so that they can be reused for future backtests and live runs using the same `identifier` model. This provides a performance enhancement geared towards enabling **high-level hyperopting** of entry/exit criteria.
An additional directory called `predictions`, which contains all the predictions stored in `hdf` format, will be created in the `unique-id` folder.
To change your **features**, you **must** set a new `identifier` in the config to signal to `FreqAI` to train new models.
To save the models generated during a particular backtest so that you can start a live deployment from one of them instead of training a new model, you must set `save_backtest_models` to `True` in the config.
### Downloading data to cover the full backtest period
For live/dry deployments, FreqAI will download the necessary data automatically. However, to use backtesting functionality, you need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). You need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that there is a sufficient amount of training data *before* the start of the backtesting timerange. The amount of additional data can be roughly estimated by moving the start date of the timerange backwards by `train_period_days` and the `startup_candle_count` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters) from the beginning of the desired backtesting timerange.
As an example, to backtest the `--timerange 20210501-20210701` using the [example config](freqai-configuration.md#setting-up-the-configuration-file) which sets `train_period_days` to 30, together with `startup_candle_count: 40` on a maximum `include_timeframes` of 1h, the start date for the downloaded data needs to be `20210501` - 30 days - 40 * 1h / 24 hours = 20210330 (31.7 days earlier than the start of the desired training timerange).
### Deciding the size of the sliding training window and backtesting duration
The backtesting timerange is defined with the typical `--timerange` parameter in the configuration file. The duration of the sliding training window is set by `train_period_days`, whilst `backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be
a float to indicate sub-daily retraining in live/dry mode). In the presented [example config](freqai-configuration.md#setting-up-the-configuration-file) (found in `config_examples/config_freqai.example.json`), the user is asking FreqAI to use a training period of 30 days and backtest on the subsequent 7 days. After the training of the model, FreqAI will backtest the subsequent 7 days. The "sliding window" then moves one week forward (emulating FreqAI retraining once per week in live mode) and the new model uses the previous 30 days (including the 7 days used for backtesting by the previous model) to train. This is repeated until the end of `--timerange`. This means that if you set `--timerange 20210501-20210701`, FreqAI will have trained 8 separate models at the end of `--timerange` (because the full range comprises 8 weeks).
!!! Note
Although fractional `backtest_period_days` is allowed, you should be aware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, by setting a `--timerange` of 10 days, and a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. Because of this, a true backtest of FreqAI adaptive training would take a *very* long time. The best way to fully test a model is to run it dry and let it train constantly. In this case, backtesting would take the exact same amount of time as a dry run.
## Defining model expirations
During dry/live mode, FreqAI trains each coin pair sequentially (on separate threads/GPU from the main Freqtrade bot). This means that there is always an age discrepancy between models. If you are training on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old. This may be undesirable if the characteristic time scale (the trade duration target) for a strategy is less than 4 hours. You can decide to only make trade entries if the model is less than a certain number of hours old by setting the `expiration_hours` in the config file:
```json
"freqai": {
"expiration_hours": 0.5,
}
```
In the presented example config, the user will only allow predictions on models that are less than 1/2 hours old.
## Data stratification for training and testing the model
You can stratify (group) the training/testing data using:
```json
"freqai": {
"feature_parameters" : {
"stratify_training_data": 3
}
}
```
This will split the data chronologically so that every Xth data point is used to test the model after training. In the example above, the user is asking for every third data point in the dataframe to be used for
testing; the other points are used for training.
The test data is used to evaluate the performance of the model after training. If the test score is high, the model is able to capture the behavior of the data well. If the test score is low, either the model does not capture the complexity of the data, the test data is significantly different from the train data, or a different type of model should be used.
## Controlling the model learning process
Model training parameters are unique to the selected machine learning library. FreqAI allows you to set any parameter for any library using the `model_training_parameters` dictionary in the config. The example config (found in `config_examples/config_freqai.example.json`) shows some of the example parameters associated with `Catboost` and `LightGBM`, but you can add any parameters available in those libraries or any other machine learning library you choose to implement.
Data split parameters are defined in `data_split_parameters` which can be any parameters associated with Scikit-learn's `train_test_split()` function. `train_test_split()` has a parameters called `shuffle` which allows to shuffle the data or keep it unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. More details about these parameters can be found the [Scikit-learn website](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
The FreqAI specific parameter `label_period_candles` defines the offset (number of candles into the future) used for the `labels`. In the presented [example config](freqai-configuration.md#setting-up-the-configuration-file), the user is asking for `labels` that are 24 candles in the future.
## Continual learning
You can choose to adopt a continual learning scheme by setting `"continual_learning": true` in the config. By enabling `continual_learning`, after training an initial model from scratch, subsequent trainings will start from the final model state of the preceding training. This gives the new model a "memory" of the previous state. By default, this is set to `false` which means that all new models are trained from scratch, without input from previous models.
## Hyperopt
You can hyperopt using the same command as for [typical Freqtrade hyperopt](hyperopt.md):
```bash
freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --timerange 20220428-20220507
```
`hyperopt` requires you to have the data pre-downloaded in the same fashion as if you were doing [backtesting](#backtesting). In addition, you must consider some restrictions when trying to hyperopt FreqAI strategies:
- The `--analyze-per-epoch` hyperopt parameter is not compatible with FreqAI.
- It's not possible to hyperopt indicators in the `populate_any_indicators()` function. This means that you cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
- The backtesting instructions also apply to hyperopt.
The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. You need to focus on hyperopting parameters that are not used in your features. For example, you should not try to hyperopt rolling window lengths in the feature creation, or any part of the FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only.
A good example of a hyperoptable parameter in FreqAI is a threshold for the [Dissimilarity Index (DI)](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di) `DI_values` beyond which we consider data points as outliers:
```python
di_max = IntParameter(low=1, high=20, default=10, space='buy', optimize=True, load=True)
dataframe['outlier'] = np.where(dataframe['DI_values'] > self.di_max.value/10, 1, 0)
```
This specific hyperopt would help you understand the appropriate `DI_values` for your particular parameter space.
## Setting up a follower
You can indicate to the bot that it should not train models, but instead should look for models trained by a leader with a specific `identifier` by defining:
```json
"freqai": {
"follow_mode": true,
"identifier": "example"
}
```
In this example, the user has a leader bot with the `"identifier": "example"`. The leader bot is already running or is launched simultaneously with the follower. The follower will load models created by the leader and inference them to obtain predictions instead of training its own models.

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![freqai-logo](assets/freqai_doc_logo.svg)
# FreqAI
# `FreqAI`
FreqAI is a module designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input features.
## Introduction
`FreqAI` is a software designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input features.
Features include:
* **Self-adaptive retraining**: retrain models during [live deployments](#running-the-model-live) to self-adapt to the market in an unsupervised manner.
* **Rapid feature engineering**: create large rich [feature sets](#feature-engineering) (10k+ features) based on simple user-created strategies.
* **High performance**: adaptive retraining occurs on a separate thread (or on GPU if available) from inferencing and bot trade operations. Newest models and data are kept in memory for rapid inferencing.
* **Realistic backtesting**: emulate self-adaptive retraining with a [backtesting module](#backtesting) that automates past retraining.
* **Modifiability**: use the generalized and robust architecture for incorporating any [machine learning library/method](#building-a-custom-prediction-model) available in Python. Eight examples are currently available, including classifiers, regressors, and a convolutional neural network.
* **Smart outlier removal**: remove outliers from training and prediction data sets using a variety of [outlier detection techniques](#outlier-removal).
* **Crash resilience**: store model to disk to make reloading from a crash fast and easy, and [purge obsolete files](#purging-old-model-data) for sustained dry/live runs.
* **Automatic data normalization**: [normalize the data](#feature-normalization) in a smart and statistically safe way.
* **Automatic data download**: compute the data download timerange and update historic data (in live deployments).
* **Cleaning of incoming data**: handle NaNs safely before training and prediction.
* **Dimensionality reduction**: reduce the size of the training data via [Principal Component Analysis](#reducing-data-dimensionality-with-principal-component-analysis).
* **Deploying bot fleets**: set one bot to train models while a fleet of [follower bots](#setting-up-a-follower) inference the models and handle trades.
* **Self-adaptive retraining** - Retrain models during [live deployments](freqai-running.md#live-deployments) to self-adapt to the market in a supervised manner
* **Rapid feature engineering** - Create large rich [feature sets](freqai-feature-engineering.md#feature-engineering) (10k+ features) based on simple user-created strategies
* **High performance** - Threading allows for adaptive model retraining on a separate thread (or on GPU if available) from model inferencing (prediction) and bot trade operations. Newest models and data are kept in RAM for rapid inferencing
* **Realistic backtesting** - Emulate self-adaptive training on historic data with a [backtesting module](freqai-running.md#backtesting) that automates retraining
* **Extensibility** - The generalized and robust architecture allows for incorporating any [machine learning library/method](freqai-configuration.md#using-different-prediction-models) available in Python. Eight examples are currently available, including classifiers, regressors, and a convolutional neural network
* **Smart outlier removal** - Remove outliers from training and prediction data sets using a variety of [outlier detection techniques](freqai-feature-engineering.md#outlier-detection)
* **Crash resilience** - Store trained models to disk to make reloading from a crash fast and easy, and [purge obsolete files](freqai-running.md#purging-old-model-data) for sustained dry/live runs
* **Automatic data normalization** - [Normalize the data](freqai-feature-engineering.md#feature-normalization) in a smart and statistically safe way
* **Automatic data download** - Compute timeranges for data downloads and update historic data (in live deployments)
* **Cleaning of incoming data** - Handle NaNs safely before training and model inferencing
* **Dimensionality reduction** - Reduce the size of the training data via [Principal Component Analysis](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis)
* **Deploying bot fleets** - Set one bot to train models while a fleet of [follower bots](freqai-running.md#setting-up-a-follower) inference the models and handle trades
## Quick start
The easiest way to quickly test FreqAI is to run it in dry mode with the following command
The easiest way to quickly test `FreqAI` is to run it in dry mode with the following command:
```bash
freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates
```
The user will see the boot-up process of automatic data downloading, followed by simultaneous training and trading.
You will see the boot-up process of automatic data downloading, followed by simultaneous training and trading.
The example strategy, example prediction model, and example config can be found in
An example strategy, prediction model, and config to use as a starting points can be found in
`freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`, and
`config_examples/config_freqai.example.json`, respectively.
## General approach
The user provides FreqAI with a set of custom *base* indicators (the same way as in a typical Freqtrade strategy) as well as target values (*labels*).
FreqAI trains a model to predict the target values based on the input of custom indicators, for each pair in the whitelist. These models are consistently retrained to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining) and deploy dry/live runs. In dry/live conditions, FreqAI can be set to constant retraining in a background thread in an effort to keep models as up to date as possible.
You provide `FreqAI` with a set of custom *base indicators* (the same way as in a [typical Freqtrade strategy](strategy-customization.md)) as well as target values (*labels*). For each pair in the whitelist, `FreqAI` trains a model to predict the target values based on the input of custom indicators. The models are then consistently retrained, with a predetermined frequency, to adapt to market conditions. `FreqAI` offers the ability to both backtest strategies (emulating reality with periodic retraining on historic data) and deploy dry/live runs. In dry/live conditions, `FreqAI` can be set to constant retraining in a background thread to keep models as up to date as possible.
An overview of the algorithm is shown below, explaining the data processing pipeline and the model usage.
An overview of the algorithm, explaining the data processing pipeline and model usage, is shown below.
![freqai-algo](assets/freqai_algo.jpg)
### Important machine learning vocabulary
**Features** - the quantities with which a model is trained. All features for a single candle is stored as a vector. In FreqAI, the user
builds the feature sets from anything they can construct in the strategy.
**Features** - the parameters, based on historic data, on which a model is trained. All features for a single candle is stored as a vector. In `FreqAI`, you build a feature data sets from anything you can construct in the strategy.
**Labels** - the target values that a model is trained
toward. Each set of features is associated with a single label that is
defined by the user within the strategy. These labels intentionally look into the
future, and are not available to the model during dry/live/backtesting.
**Labels** - the target values that a model is trained toward. Each feature vector is associated with a single label that is defined by you within the strategy. These labels intentionally look into the future, and are not available to the model during dry/live/backtesting.
**Training** - the process of feeding individual feature sets, composed of historic data, with associated labels into the
model with the goal of matching input feature sets to associated labels.
**Training** - the process of "teaching" the model to match the feature sets to the associated labels. Different types of models "learn" in different ways. More information about the different models can be found [here](freqai-configuration.md#using-different-prediction-models).
**Train data** - a subset of the historic data that is fed to the model during
training. This data directly influences weight connections in the model.
**Train data** - a subset of the feature data set that is fed to the model during training. This data directly influences weight connections in the model.
**Test data** - a subset of the historic data that is used to evaluate the performance of the model after training. This data does not influence nodal weights within the model.
**Test data** - a subset of the feature data set that is used to evaluate the performance of the model after training. This data does not influence nodal weights within the model.
**Inferencing** - the process of feeding a trained model new data on which it will make a prediction.
## Install prerequisites
The normal Freqtrade install process will ask the user if they wish to install FreqAI dependencies. The user should reply "yes" to this question if they wish to use FreqAI. If the user did not reply yes, they can manually install these dependencies after the install with:
The normal Freqtrade install process will ask if you wish to install `FreqAI` dependencies. You should reply "yes" to this question if you wish to use `FreqAI`. If you did not reply yes, you can manually install these dependencies after the install with:
``` bash
pip install -r requirements-freqai.txt
```
!!! Note
Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since Catboost does not provide wheels for this platform.
Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since it does not provide wheels for this platform.
### Usage with docker
For docker users, a dedicated tag with freqAI dependencies is available as `:freqai`.
As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`.
This image contains the regular freqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices.
If you are using docker, a dedicated tag with `FreqAI` dependencies is available as `:freqai`. As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular `FreqAI` dependencies. Similar to native installs, Catboost will not be available on ARM based devices.
## Setting up FreqAI
## Common pitfalls
### Parameter table
The table below will list all configuration parameters available for FreqAI, presented in the same order as `config_examples/config_freqai.example.json`.
Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
| Parameter | Description |
|------------|-------------|
| | **General configuration parameters**
| `freqai` | **Required.** <br> The parent dictionary containing all the parameters for controlling FreqAI. <br> **Datatype:** Dictionary.
| `startup_candles` | Number of candles needed for *backtesting only* to ensure all indicators are non NaNs at the start of the first train period. <br> **Datatype:** Positive integer.
| `purge_old_models` | Delete obsolete models (otherwise, all historic models will remain on disk). <br> **Datatype:** Boolean. Default: `False`.
| `train_period_days` | **Required.** <br> Number of days to use for the training data (width of the sliding window). <br> **Datatype:** Positive integer.
| `backtest_period_days` | **Required.** <br> Number of days to inference from the trained model before sliding the window defined above, and retraining the model. This can be fractional days, but beware that the user-provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest. <br> **Datatype:** Float.
| `identifier` | **Required.** <br> A unique name for the current model. This can be reused to reload pre-trained models/data. <br> **Datatype:** String.
| `live_retrain_hours` | Frequency of retraining during dry/live runs. <br> Default set to 0, which means the model will retrain as often as possible. <br> **Datatype:** Float > 0.
| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old. <br> Defaults set to 0, which means models never expire. <br> **Datatype:** Positive integer.
| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training data set. <br> **Datatype:** Positive integer.
| `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models. <br> **Datatype:** Boolean. Default: `False`.
| | **Feature parameters**
| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](#feature-engineering). <br> **Datatype:** Dictionary.
| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base asset feature set. <br> **Datatype:** List of timeframes (strings).
| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](#feature-engineering)) will be created for each coin in this list, and that set of features is added to the base asset feature set. <br> **Datatype:** List of assets (strings).
| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). The user can create custom labels, making use of this parameter or not. <br> **Datatype:** Positive integer.
| `include_shifted_candles` | Add features from previous candles to subsequent candles to add historical information. FreqAI takes all features from the `include_shifted_candles` previous candles, duplicates and shifts them so that the information is available for the subsequent candle. <br> **Datatype:** Positive integer.
| `weight_factor` | Used to set weights for training data points according to their recency. See details about how it works [here](#controlling-the-model-learning-process). <br> **Datatype:** Positive float (typically < 1).
| `indicator_max_period_candles` | The maximum period used in `populate_any_indicators()` for indicator creation. FreqAI uses this information in combination with the maximum timeframe to calculate how many data points that should be downloaded so that the first data point does not have a NaN. <br> **Datatype:** Positive integer.
| `indicator_periods_candles` | Calculate indicators for `indicator_periods_candles` time periods and add them to the feature set. <br> **Datatype:** List of positive integers.
| `stratify_training_data` | This value is used to indicate the grouping of the data. For example, 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing. See details about how it works [here](#stratifying-the-data-for-training-and-testing-the-model) <br> **Datatype:** Positive integer.
| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean.
| `DI_threshold` | Activates the Dissimilarity Index for outlier detection when > 0. See details about how it works [here](#removing-outliers-with-the-dissimilarity-index). <br> **Datatype:** Positive float (typically < 1).
| `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training data set, as well as from incoming data points. See details about how it works [here](#removing-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Boolean.
| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](#removing-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Dictionary.
| `use_DBSCAN_to_remove_outliers` | Cluster data using DBSCAN to identify and remove outliers from training and prediction data. See details about how it works [here](#removing-outliers-with-dbscan). <br> **Datatype:** Boolean.
| `outlier_protection_percentage` | If more than `outlier_protection_percentage` fraction of points are removed as outliers, FreqAI will log a warning message and ignore outlier detection while keeping the original dataset intact. <br> **Datatype:** float. Default: `30`
| | **Data split parameters**
| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). <br> **Datatype:** Dictionary.
| `test_size` | Fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.
| `shuffle` | Shuffle the training data points during training. Typically, for time-series forecasting, this is set to `False`. <br>
| | **Model training parameters**
| `model_training_parameters` | A flexible dictionary that includes all parameters available by the user selected model library. For example, if the user uses `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If the user selects a different model, this dictionary can contain any parameter from that model. <br> **Datatype:** Dictionary.**Datatype:** Boolean.
| `n_estimators` | The number of boosted trees to fit in regression. <br> **Datatype:** Integer.
| `learning_rate` | Boosting learning rate during regression. <br> **Datatype:** Float.
| `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names. <br> **Datatype:** Float.
| | **Extraneous parameters**
| `keras` | If your model makes use of Keras (typical for Tensorflow-based prediction models), activate this flag so that the model save/loading follows Keras standards. <br> **Datatype:** Boolean. Default: `False`.
| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. Default: 2.
### Important dataframe key patterns
Below are the values the user can expect to include/use inside a typical strategy dataframe (`df[]`):
| DataFrame Key | Description |
|------------|-------------|
| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). The names of these dataframe columns are fed back to the user as the predictions. For example, if the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`), they set `df['&-s_close']`. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
| `df['&*_std/mean']` | Standard deviation and mean values of the user-defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`). <br> **Datatype:** Float.
| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -1 and 2, which lets the user know if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](#removing-outliers-with-the-dissimilarity-index)) of the input data point is above the user-defined threshold, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -1 and 2.
| `df['DI_values']` | Dissimilarity Index values are proxies to the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. <br> **Datatype:** Float.
| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, the user can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#feature-engineering). <br> **Note**: Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, they can prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
### File structure
`user_data_dir/models/` contains all the data associated with the trainings and backtests.
This file structure is heavily controlled and inferenced by the `FreqaiDataKitchen()`
and should therefore not be modified.
### Example config file
The user interface is isolated to the typical Freqtrade config file. A FreqAI config should include:
```json
"freqai": {
"enabled": true,
"startup_candles": 10000,
"purge_old_models": true,
"train_period_days": 30,
"backtest_period_days": 7,
"identifier" : "unique-id",
"feature_parameters" : {
"include_timeframes": ["5m","15m","4h"],
"include_corr_pairlist": [
"ETH/USD",
"LINK/USD",
"BNB/USD"
],
"label_period_candles": 24,
"include_shifted_candles": 2,
"indicator_max_period_candles": 20,
"indicator_periods_candles": [10, 20]
},
"data_split_parameters" : {
"test_size": 0.25
},
"model_training_parameters" : {
"n_estimators": 100
},
}
```
## Building a FreqAI strategy
The FreqAI strategy requires the user to include the following lines of code in the standard Freqtrade strategy:
```python
def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist()
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
informative_pairs = []
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
for pair in whitelist_pairs:
informative_pairs.append((pair, tf))
for pair in corr_pairs:
if pair in whitelist_pairs:
continue # avoid duplication
informative_pairs.append((pair, tf))
return informative_pairs
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# the model will return all labels created by user in `populate_any_indicators`
# (& appended targets), an indication of whether or not the prediction should be accepted,
# the target mean/std values for each of the labels created by user in
# `populate_any_indicators()` for each training period.
dataframe = self.freqai.start(dataframe, metadata, self)
return dataframe
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
"""
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + coin `
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
:param coin: the name of the coin which will modify the feature names.
"""
coin = pair.split('/')[0]
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
)
return df
```
Notice how the `populate_any_indicators()` is where the user adds their own features ([more information](#feature-engineering)) and labels ([more information](#setting-classifier-targets)). See a full example at `templates/FreqaiExampleStrategy.py`.
## Creating a dynamic target
The `&*_std/mean` return values describe the statistical fit of the user defined label *during the most recent training*. This value allows the user to know the rarity of a given prediction. For example, `templates/FreqaiExampleStrategy.py`, creates a `target_roi` which is based on filtering out predictions that are below a given z-score of 1.25.
```python
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
```
If the user wishes to consider the population
of *historical predictions* for creating the dynamic target instead of the trained labels, (as discussed above) the user
can do so by setting `fit_live_prediction_candles` in the config to the number of historical prediction candles
the user wishes to use to generate target statistics.
```json
"freqai": {
"fit_live_prediction_candles": 300,
}
```
If the user sets this value, FreqAI will initially use the predictions from the training data
and subsequently begin introducing real prediction data as it is generated. FreqAI will save
this historical data to be reloaded if the user stops and restarts a model with the same `identifier`.
## Building a custom prediction model
FreqAI has multiple example prediction model libraries, such as `Catboost` regression (`freqai/prediction_models/CatboostRegressor.py`) and `LightGBM` regression.
However, the user can customize and create their own prediction models using the `IFreqaiModel` class.
The user is encouraged to inherit `train()` and `predict()` to let them customize various aspects of their training procedures.
## Feature engineering
Features are added by the user inside the `populate_any_indicators()` method of the strategy
by prepending indicators with `%`, and labels with `&`.
There are some important components/structures that the user *must* include when building their feature set; the use of these is shown below:
```python
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
"""
Function designed to automatically generate, name, and merge features
from user-indicated timeframes in the configuration file. The user controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + coin `
(see convention below). I.e., the user should not prepend any supporting metrics
(e.g., bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
:param coin: the name of the coin which will modify the feature names.
"""
coin = pair.split('/')[0]
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=t, stds=2.2
)
informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
informative[f"%-{coin}bb_width-period_{t}"] = (
informative[f"{coin}bb_upperband-period_{t}"]
- informative[f"{coin}bb_lowerband-period_{t}"]
) / informative[f"{coin}bb_middleband-period_{t}"]
informative[f"%-{coin}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
)
informative[f"%-{coin}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
)
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
)
return df
```
In the presented example strategy, the user does not wish to pass the `bb_lowerband` as a feature to the model,
and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the
model for training/prediction and has therefore prepended it with `%`.
The `include_timeframes` in the example config above are the timeframes (`tf`) of each call to `populate_any_indicators()` in the strategy. In the present case, the user is asking for the
`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set.
The user can ask for each of the defined features to be included also from
informative pairs using the `include_corr_pairlist`. This means that the feature
set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD`).
`include_shifted_candles` indicates the number of previous
candles to include in the feature set. For example, `include_shifted_candles: 2` tells
FreqAI to include the past 2 candles for each of the features in the feature set.
In total, the number of features the user of the presented example strat has created is:
length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
$= 3 * 3 * 3 * 2 * 2 = 108$.
Another structure to consider is the location of the labels at the bottom of the example function (below `if set_generalized_indicators:`).
This is where the user will add single features and labels to their feature set to avoid duplication of them from
various configuration parameters that multiply the feature set, such as `include_timeframes`.
!!! Note
Features **must** be defined in `populate_any_indicators()`. Definining FreqAI features in `populate_indicators()`
will cause the algorithm to fail in live/dry mode. If the user wishes to add generalized features that are not associated with
a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()`
(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`):
```python
def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False):
...
# Add generalized indicators here (because in live, it will call only this function to populate
# indicators for retraining). Notice how we ensure not to add them multiple times by associating
# these generalized indicators to the basepair/timeframe
if set_generalized_indicators:
df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
)
```
(Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`.)
## Setting classifier targets
FreqAI includes the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. The user should take care to set the classes using strings:
```python
df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
```
Additionally, the example classifier models do not accommodate multiple labels, but they do allow multi-class classification within a single label column.
## Running FreqAI
There are two ways to train and deploy an adaptive machine learning model. FreqAI enables live deployment as well as backtesting analyses. In both cases, a model is trained periodically, as shown in the following figure.
![freqai-window](assets/freqai_moving-window.jpg)
### Running the model live
FreqAI can be run dry/live using the following command:
```bash
freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor
```
By default, FreqAI will not find any existing models and will start by training a new one
based on the user's configuration settings. Following training, the model will be used to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the coin pairs to try to keep all models equally up to date. FreqAI will always use the most recently trained model to make predictions on incoming live data. If the user does not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before training a new model. Additionally, the user can set `expired_hours` to tell FreqAI to avoid making predictions on models that are older than that number of hours.
If the user wishes to start a dry/live run from a saved backtest model (or from a previously crashed dry/live session), the user only needs to reuse
the same `identifier` parameter:
```json
"freqai": {
"identifier": "example",
"live_retrain_hours": 0.5
}
```
In this case, although FreqAI will initiate with a
pre-trained model, it will still check to see how much time has elapsed since the model was trained,
and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will retrain.
### Backtesting
The FreqAI backtesting module can be executed with the following command:
```bash
freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701
```
Backtesting mode requires the user to have the data pre-downloaded (unlike in dry/live mode where FreqAI automatically downloads the necessary data). The user should be careful to consider that the time range of the downloaded data is more than the backtesting time range. This is because FreqAI needs data prior to the desired backtesting time range in order to train a model to be ready to make predictions on the first candle of the user-set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration).
If this command has never been executed with the existing config file, it will train a new model
for each pair, for each backtesting window within the expanded `--timerange`.
!!! Note "Model reuse"
Once the training is completed, the user can execute the backtesting again with the same config file and
FreqAI will find the trained models and load them instead of spending time training. This is useful
if the user wants to tweak (or even hyperopt) buy and sell criteria inside the strategy. If the user
*wants* to retrain a new model with the same config file, then they should simply change the `identifier`.
This way, the user can return to using any model they wish by simply specifying the `identifier`.
---
### Deciding the size of the sliding training window and backtesting duration
The user defines the backtesting timerange with the typical `--timerange` parameter in the
configuration file. The duration of the sliding training window is set by `train_period_days`, whilst
`backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be
a float to indicate sub-daily retraining in live/dry mode). In the presented example config,
the user is asking FreqAI to use a training period of 30 days and backtest on the subsequent 7 days.
This means that if the user sets `--timerange 20210501-20210701`,
FreqAI will train have trained 8 separate models at the end of `--timerange` (because the full range comprises 8 weeks). After the training of the model, FreqAI will backtest the subsequent 7 days. The "sliding window" then moves one week forward (emulating FreqAI retraining once per week in live mode) and the new model uses the previous 30 days (including the 7 days used for backtesting by the previous model) to train. This is repeated until the end of `--timerange`.
In live mode, the required training data is automatically computed and downloaded. However, in backtesting mode,
the user must manually enter the required number of `startup_candles` in the config. This value
is used to increase the data to FreqAI, which should be sufficient to enable all indicators
to be NaN free at the beginning of the first training. This is done by identifying the
longest timeframe (`4h` in presented example config) and the longest indicator period (`20` days in presented example config)
and adding this to the `train_period_days`. The units need to be in the base candle time frame:
`startup_candles` = ( 4 hours * 20 max period * 60 minutes/hour + 30 day train_period_days * 1440 minutes per day ) / 5 min (base time frame) = 9360.
!!! Note
In dry/live mode, this is all precomputed and handled automatically. Thus, `startup_candle` has no influence on dry/live mode.
!!! Note
Although fractional `backtest_period_days` is allowed, the user should be aware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, if the user wants to set a `--timerange` of 10 days, and asks for a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. Because of this, a true backtest of FreqAI adaptive training would take a *very* long time. The best way to fully test a model is to run it dry and let it constantly train. In this case, backtesting would take the exact same amount of time as a dry run.
### Defining model expirations
During dry/live mode, FreqAI trains each coin pair sequentially (on separate threads/GPU from the main Freqtrade bot). This means that there is always an age discrepancy between models. If a user is training on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old. This may be undesirable if the characteristic time scale (the trade duration target) for a strategy is less than 4 hours. The user can decide to only make trade entries if the model is less than
a certain number of hours old by setting the `expiration_hours` in the config file:
```json
"freqai": {
"expiration_hours": 0.5,
}
```
In the presented example config, the user will only allow predictions on models that are less than 1/2 hours old.
### Purging old model data
FreqAI stores new model files each time it retrains. These files become obsolete as new models are trained and FreqAI adapts to new market conditions. Users planning to leave FreqAI running for extended periods of time with high frequency retraining should enable `purge_old_models` in their config:
```json
"freqai": {
"purge_old_models": true,
}
```
This will automatically purge all models older than the two most recently trained ones.
### Returning additional info from training
The user may find that there are some important metrics that they'd like to return to the strategy at the end of each model training.
The user can include these metrics by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside their custom prediction model class. FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the return dataframe to the strategy.
The user can then use the value in the strategy with `dataframe['my_new_value']`. An example of how this is already used in FreqAI is
the `&*_mean` and `&*_std` values, which indicate the mean and standard deviation of the particular target (label) during the most recent training.
An example, where the user wants to use live metrics from the trade database, is shown below:
```json
"freqai": {
"extra_returns_per_train": {"total_profit": 4}
}
```
The user needs to set the standard dictionary in the config so that FreqAI can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, this is the value that will be returned.
### Setting up a follower
The user can define:
```json
"freqai": {
"follow_mode": true,
"identifier": "example"
}
```
to indicate to the bot that it should not train models, but instead should look for models trained by a leader with the same `identifier`. In this example, the user has a leader bot with the `identifier: "example"`. The leader bot is already running or launching simultaneously as the follower.
The follower will load models created by the leader and inference them to obtain predictions.
## Data manipulation techniques
### Feature normalization
The feature set created by the user is automatically normalized to the training data. This includes all test data and unseen prediction data (dry/live/backtest).
### Reducing data dimensionality with Principal Component Analysis
Users can reduce the dimensionality of their features by activating the `principal_component_analysis` in the config:
```json
"freqai": {
"feature_parameters" : {
"principal_component_analysis": true
}
}
```
This will perform PCA on the features and reduce the dimensionality of the data so that the explained variance of the data set is >= 0.999.
### Stratifying the data for training and testing the model
The user can stratify (group) the training/testing data using:
```json
"freqai": {
"feature_parameters" : {
"stratify_training_data": 3
}
}
```
This will split the data chronologically so that every Xth data point is used to test the model after training. In the
example above, the user is asking for every third data point in the dataframe to be used for
testing; the other points are used for training.
The test data is used to evaluate the performance of the model after training. If the test score is high, the model is able to capture the behavior of the data well. If the test score is low, either the model either does not capture the complexity of the data, the test data is significantly different from the train data, or a different model should be used.
### Controlling the model learning process
Model training parameters are unique to the machine learning library selected by the user. FreqAI allows the user to set any parameter for any library using the `model_training_parameters` dictionary in the user configuration file. The example configuration file (found in `config_examples/config_freqai.example.json`) show some of the example parameters associated with `Catboost` and `LightGBM`, but the user can add any parameters available in those libraries.
Data split parameters are defined in `data_split_parameters` which can be any parameters associated with `Sklearn`'s `train_test_split()` function.
FreqAI includes some additional parameters such as `weight_factor`, which allows the user to weight more recent data more strongly
than past data via an exponential function:
$$ W_i = \exp(\frac{-i}{\alpha*n}) $$
where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. Below is a figure showing the effect of different weight factors on the data points (candles) in a feature set.
![weight-factor](assets/freqai_weight-factor.jpg)
`train_test_split()` has a parameters called `shuffle` that allows the user to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data.
Finally, `label_period_candles` defines the offset (number of candles into the future) used for the `labels`. In the presented example config,
the user is asking for `labels` that are 24 candles in the future.
### Outlier removal
#### Removing outliers with the Dissimilarity Index
The user can tell FreqAI to remove outlier data points from the training/test data sets using a Dissimilarity Index by including the following statement in the config:
```json
"freqai": {
"feature_parameters" : {
"DI_threshold": 1
}
}
```
Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction made by the model. The DI allows predictions which are outliers (not existent in the model feature space) to be thrown out due to low levels of certainty.
To do so, FreqAI measures the distance between each training data point (feature vector), $X_{a}$, and all other training data points:
$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$
where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$ is the number of features, i.e., the length of the vector $X$. The characteristic distance, $\overline{d}$ for a set of training data points is simply the mean of the average distances:
$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$
$\overline{d}$ quantifies the spread of the training data, which is compared to the distance between a new prediction feature vectors, $X_k$ and all the training data:
$$ d_k = \arg \min d_{k,i} $$
which enables the estimation of the Dissimilarity Index as:
$$ DI_k = d_k/\overline{d} $$
The user can tweak the DI through the `DI_threshold` to increase or decrease the extrapolation of the trained model.
Below is a figure that describes the DI for a 3D data set.
![DI](assets/freqai_DI.jpg)
#### Removing outliers using a Support Vector Machine (SVM)
The user can tell FreqAI to remove outlier data points from the training/test data sets using a SVM by setting:
```json
"freqai": {
"feature_parameters" : {
"use_SVM_to_remove_outliers": true
}
}
```
FreqAI will train an SVM on the training data (or components of it if the user activated
`principal_component_analysis`) and remove any data point that the SVM deems to be beyond the feature space.
The parameter `shuffle` is by default set to `False` to ensure consistent results. If it is set to `True`, running the SVM multiple times on the same data set might result in different outcomes due to `max_iter` being to low for the algorithm to reach the demanded `tol`. Increasing `max_iter` solves this issue but causes the procedure to take longer time.
The parameter `nu`, *very* broadly, is the amount of data points that should be considered outliers.
#### Removing outliers with DBSCAN
The user can configure FreqAI to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config:
```json
"freqai": {
"feature_parameters" : {
"use_DBSCAN_to_remove_outliers": true
}
}
```
DBSCAN is an unsupervised machine learning algorithm that clusters data without needing to know how many clusters there should be.
Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters the data set by setting all data points that have $N-1$ other data points within a distance of $\varepsilon$ as *core points*. A data point that is within a distance of $\varepsilon$ from a *core point* but that does not have $N-1$ other data points within a distance of $\varepsilon$ from itself is considered an *edge point*. A cluster is then the collection of *core points* and *edge points*. Data points that have no other data points at a distance $<\varepsilon$ are considered outliers. The figure below shows a cluster with $N = 3$.
![dbscan](assets/freqai_dbscan.jpg)
FreqAI uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](#https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html)) with `min_samples` ($N$) taken as double the no. of user-defined features, and `eps` ($\varepsilon$) taken as the longest distance in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set.
## Additional information
### Common pitfalls
FreqAI cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically).
This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively,
it needs to download all the training data at the beginning of a dry/live instance. FreqAI stores and appends
new candles automatically for future retrains. This means that if new pairs arrive later in the dry run due to a volume pairlist, it will not have the data ready. However, FreqAI does work with the `ShufflePairlist` or a `VolumePairlist` which keeps the total pairlist constant (but reorders the pairs according to volume).
`FreqAI` cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically).
This is for performance reasons - `FreqAI` relies on making quick predictions/retrains. To do this effectively,
it needs to download all the training data at the beginning of a dry/live instance. `FreqAI` stores and appends
new candles automatically for future retrains. This means that if new pairs arrive later in the dry run due to a volume pairlist, it will not have the data ready. However, `FreqAI` does work with the `ShufflePairlist` or a `VolumePairlist` which keeps the total pairlist constant (but reorders the pairs according to volume).
## Credits
FreqAI was developed by a group of individuals who all contributed specific skillsets to the project.
`FreqAI` is developed by a group of individuals who all contribute specific skillsets to the project.
Conception and software development:
Robert Caulk @robcaulk
Theoretical brainstorming, data analysis:
Theoretical brainstorming and data analysis:
Elin Törnquist @th0rntwig
Code review, software architecture brainstorming:
Code review and software architecture brainstorming:
@xmatthias
Software development:
Wagner Costa @wagnercosta
Beta testing and bug reporting:
@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
Juha Nykänen @suikula, Wagner Costa @wagnercosta
Stefan Gehring @bloodhunter4rc, @longyu, Andrew Robert Lawless @paranoidandy, Pascal Schmidt @smidelis, Ryan McMullan @smarmau,
Juha Nykänen @suikula, Johan van der Vlugt @jooopiert, Richárd Józsa @richardjosza

9
docs/leverage.md
View File

@ -13,7 +13,7 @@
Please only use advanced trading modes when you know how freqtrade (and your strategy) works.
Also, never risk more than what you can afford to lose.
Please read the [strategy migration guide](strategy_migration.md#strategy-migration-between-v2-and-v3) to migrate your strategy from a freqtrade v2 strategy, to v3 strategy that can short and trade futures.
If you already have an existing strategy, please read the [strategy migration guide](strategy_migration.md#strategy-migration-between-v2-and-v3) to migrate your strategy from a freqtrade v2 strategy, to strategy of version 3 which can short and trade futures.
## Shorting
@ -62,6 +62,13 @@ You will also have to pick a "margin mode" (explanation below) - with freqtrade
"margin_mode": "isolated"
```
##### Pair namings
Freqtrade follows the [ccxt naming conventions for futures](https://docs.ccxt.com/en/latest/manual.html?#perpetual-swap-perpetual-future).
A futures pair will therefore have the naming of `base/quote:settle` (e.g. `ETH/USDT:USDT`).
Binance is currently still an exception to this naming scheme, where pairs are named `ETH/USDT` also for futures markets, but will be aligned as soon as CCXT is ready.
### Margin mode
On top of `trading_mode` - you will also have to configure your `margin_mode`.

163
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View File

@ -0,0 +1,163 @@
# Producer / Consumer mode
freqtrade provides a mechanism whereby an instance (also called `consumer`) may listen to messages from an upstream freqtrade instance (also called `producer`) using the message websocket. Mainly, `analyzed_df` and `whitelist` messages. This allows the reuse of computed indicators (and signals) for pairs in multiple bots without needing to compute them multiple times.
See [Message Websocket](rest-api.md#message-websocket) in the Rest API docs for setting up the `api_server` configuration for your message websocket (this will be your producer).
!!! Note
We strongly recommend to set `ws_token` to something random and known only to yourself to avoid unauthorized access to your bot.
## Configuration
Enable subscribing to an instance by adding the `external_message_consumer` section to the consumer's config file.
```json
{
//...
"external_message_consumer": {
"enabled": true,
"producers": [
{
"name": "default", // This can be any name you'd like, default is "default"
"host": "127.0.0.1", // The host from your producer's api_server config
"port": 8080, // The port from your producer's api_server config
"ws_token": "sercet_Ws_t0ken" // The ws_token from your producer's api_server config
}
],
// The following configurations are optional, and usually not required
// "wait_timeout": 300,
// "ping_timeout": 10,
// "sleep_time": 10,
// "remove_entry_exit_signals": false,
// "message_size_limit": 8
}
//...
}
```
| Parameter | Description |
|------------|-------------|
| `enabled` | **Required.** Enable consumer mode. If set to false, all other settings in this section are ignored.<br>*Defaults to `false`.*<br> **Datatype:** boolean .
| `producers` | **Required.** List of producers <br> **Datatype:** Array.
| `producers.name` | **Required.** Name of this producer. This name must be used in calls to `get_producer_pairs()` and `get_producer_df()` if more than one producer is used.<br> **Datatype:** string
| `producers.host` | **Required.** The hostname or IP address from your producer.<br> **Datatype:** string
| `producers.port` | **Required.** The port matching the above host.<br> **Datatype:** string
| `producers.ws_token` | **Required.** `ws_token` as configured on the producer.<br> **Datatype:** string
| | **Optional settings**
| `wait_timeout` | Timeout until we ping again if no message is received. <br>*Defaults to `300`.*<br> **Datatype:** Integer - in seconds.
| `wait_timeout` | Ping timeout <br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
| `sleep_time` | Sleep time before retrying to connect.<br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
| `remove_entry_exit_signals` | Remove signal columns from the dataframe (set them to 0) on dataframe receipt.<br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
| `message_size_limit` | Size limit per message<br>*Defaults to `8`.*<br> **Datatype:** Integer - Megabytes.
Instead of (or as well as) calculating indicators in `populate_indicators()` the follower instance listens on the connection to a producer instance's messages (or multiple producer instances in advanced configurations) and requests the producer's most recently analyzed dataframes for each pair in the active whitelist.
A consumer instance will then have a full copy of the analyzed dataframes without the need to calculate them itself.
## Examples
### Example - Producer Strategy
A simple strategy with multiple indicators. No special considerations are required in the strategy itself.
```py
class ProducerStrategy(IStrategy):
#...
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Calculate indicators in the standard freqtrade way which can then be broadcast to other instances
"""
dataframe['rsi'] = ta.RSI(dataframe)
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Populates the entry signal for the given dataframe
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) &
(dataframe['tema'] <= dataframe['bb_middleband']) &
(dataframe['tema'] > dataframe['tema'].shift(1)) &
(dataframe['volume'] > 0)
),
'enter_long'] = 1
return dataframe
```
!!! Tip "FreqAI"
You can use this to setup [FreqAI](freqai.md) on a powerful machine, while you run consumers on simple machines like raspberries, which can interpret the signals generated from the producer in different ways.
### Example - Consumer Strategy
A logically equivalent strategy which calculates no indicators itself, but will have the same analyzed dataframes available to make trading decisions based on the indicators calculated in the producer. In this example the consumer has the same entry criteria, however this is not necessary. The consumer may use different logic to enter/exit trades, and only use the indicators as specified.
```py
class ConsumerStrategy(IStrategy):
#...
process_only_new_candles = False # required for consumers
_columns_to_expect = ['rsi_default', 'tema_default', 'bb_middleband_default']
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Use the websocket api to get pre-populated indicators from another freqtrade instance.
Use `self.dp.get_producer_df(pair)` to get the dataframe
"""
pair = metadata['pair']
timeframe = self.timeframe
producer_pairs = self.dp.get_producer_pairs()
# You can specify which producer to get pairs from via:
# self.dp.get_producer_pairs("my_other_producer")
# This func returns the analyzed dataframe, and when it was analyzed
producer_dataframe, _ = self.dp.get_producer_df(pair)
# You can get other data if the producer makes it available:
# self.dp.get_producer_df(
# pair,
# timeframe="1h",
# candle_type=CandleType.SPOT,
# producer_name="my_other_producer"
# )
if not producer_dataframe.empty:
# If you plan on passing the producer's entry/exit signal directly,
# specify ffill=False or it will have unintended results
merged_dataframe = merge_informative_pair(dataframe, producer_dataframe,
timeframe, timeframe,
append_timeframe=False,
suffix="default")
return merged_dataframe
else:
dataframe[self._columns_to_expect] = 0
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Populates the entry signal for the given dataframe
"""
# Use the dataframe columns as if we calculated them ourselves
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['rsi_default'], self.buy_rsi.value)) &
(dataframe['tema_default'] <= dataframe['bb_middleband_default']) &
(dataframe['tema_default'] > dataframe['tema_default'].shift(1)) &
(dataframe['volume'] > 0)
),
'enter_long'] = 1
return dataframe
```
!!! Tip "Using upstream signals"
By setting `remove_entry_exit_signals=false`, you can also use the producer's signals directly. They should be available as `enter_long_default` (assuming `suffix="default"` was used) - and can be used as either signal directly, or as additional indicator.

2
docs/requirements-docs.txt
View File

@ -1,6 +1,6 @@
markdown==3.3.7
mkdocs==1.3.1
mkdocs-material==8.4.1
mkdocs-material==8.5.3
mdx_truly_sane_lists==1.3
pymdown-extensions==9.5
jinja2==3.1.2

71
docs/rest-api.md
View File

@ -31,7 +31,8 @@ Sample configuration:
"jwt_secret_key": "somethingrandom",
"CORS_origins": [],
"username": "Freqtrader",
"password": "SuperSecret1!"
"password": "SuperSecret1!",
"ws_token": "sercet_Ws_t0ken"
},
```
@ -93,7 +94,6 @@ Make sure that the following 2 lines are available in your docker-compose file:
!!! Danger "Security warning"
By using `8080:8080` in the docker port mapping, the API will be available to everyone connecting to the server under the correct port, so others may be able to control your bot.
## Rest API
### Consuming the API
@ -322,6 +322,73 @@ whitelist
```
### Message WebSocket
The API Server includes a websocket endpoint for subscribing to RPC messages from the freqtrade Bot.
This can be used to consume real-time data from your bot, such as entry/exit fill messages, whitelist changes, populated indicators for pairs, and more.
This is also used to setup [Producer/Consumer mode](producer-consumer.md) in Freqtrade.
Assuming your rest API is set to `127.0.0.1` on port `8080`, the endpoint is available at `http://localhost:8080/api/v1/message/ws`.
To access the websocket endpoint, the `ws_token` is required as a query parameter in the endpoint URL.
To generate a safe `ws_token` you can run the following code:
``` python
>>> import secrets
>>> secrets.token_urlsafe(25)
'hZ-y58LXyX_HZ8O1cJzVyN6ePWrLpNQv4Q'
```
You would then add that token under `ws_token` in your `api_server` config. Like so:
``` json
"api_server": {
"enabled": true,
"listen_ip_address": "127.0.0.1",
"listen_port": 8080,
"verbosity": "error",
"enable_openapi": false,
"jwt_secret_key": "somethingrandom",
"CORS_origins": [],
"username": "Freqtrader",
"password": "SuperSecret1!",
"ws_token": "hZ-y58LXyX_HZ8O1cJzVyN6ePWrLpNQv4Q" // <-----
},
```
You can now connect to the endpoint at `http://localhost:8080/api/v1/message/ws?token=hZ-y58LXyX_HZ8O1cJzVyN6ePWrLpNQv4Q`.
!!! Danger "Reuse of example tokens"
Please do not use the above example token. To make sure you are secure, generate a completely new token.
#### Using the WebSocket
Once connected to the WebSocket, the bot will broadcast RPC messages to anyone who is subscribed to them. To subscribe to a list of messages, you must send a JSON request through the WebSocket like the one below. The `data` key must be a list of message type strings.
``` json
{
"type": "subscribe",
"data": ["whitelist", "analyzed_df"] // A list of string message types
}
```
For a list of message types, please refer to the RPCMessageType enum in `freqtrade/enums/rpcmessagetype.py`
Now anytime those types of RPC messages are sent in the bot, you will receive them through the WebSocket as long as the connection is active. They typically take the same form as the request:
``` json
{
"type": "analyzed_df",
"data": {
"key": ["NEO/BTC", "5m", "spot"],
"df": {}, // The dataframe
"la": "2022-09-08 22:14:41.457786+00:00"
}
}
```
### OpenAPI interface
To enable the builtin openAPI interface (Swagger UI), specify `"enable_openapi": true` in the api_server configuration.

6
docs/strategy-advanced.md
View File

@ -106,6 +106,12 @@ def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_r
!!! Note
`enter_tag` is limited to 100 characters, remaining data will be truncated.
!!! Warning
There is only one `enter_tag` column, which is used for both long and short trades.
As a consequence, this column must be treated as "last write wins" (it's just a dataframe column after all).
In fancy situations, where multiple signals collide (or if signals are deactivated again based on different conditions), this can lead to odd results with the wrong tag applied to an entry signal.
These results are a consequence of the strategy overwriting prior tags - where the last tag will "stick" and will be the one freqtrade will use.
## Exit tag
Similar to [Buy Tagging](#buy-tag), you can also specify a sell tag.

7
docs/strategy-customization.md
View File

@ -166,7 +166,7 @@ Additional technical libraries can be installed as necessary, or custom indicato
Most indicators have an instable startup period, in which they are either not available (NaN), or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
To account for this, the strategy can be assigned the `startup_candle_count` attribute.
This should be set to the maximum number of candles that the strategy requires to calculate stable indicators.
This should be set to the maximum number of candles that the strategy requires to calculate stable indicators. In the case where a user includes higher timeframes with informative pairs, the `startup_candle_count` does not necessarily change. The value is the maximum period (in candles) that any of the informatives timeframes need to compute stable indicators.
In this example strategy, this should be set to 100 (`startup_candle_count = 100`), since the longest needed history is 100 candles.
@ -264,7 +264,8 @@ def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFram
### Exit signal rules
Edit the method `populate_exit_trend()` into your strategy file to update your exit strategy.
Please note that the exit-signal is only used if `use_exit_signal` is set to true in the configuration.
The exit-signal is only used for exits if `use_exit_signal` is set to true in the configuration.
`use_exit_signal` will not influence [signal collision rules](#colliding-signals) - which will still apply and can prevent entries.
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
@ -824,6 +825,8 @@ Options:
- Merge the dataframe without lookahead bias
- Forward-fill (optional)
For a full sample, please refer to the [complete data provider example](#complete-data-provider-sample) below.
All columns of the informative dataframe will be available on the returning dataframe in a renamed fashion:
!!! Example "Column renaming"

4
docs/strategy_migration.md
View File

@ -332,8 +332,8 @@ After:
``` python hl_lines="2 3"
order_time_in_force: Dict = {
"entry": "gtc",
"exit": "gtc",
"entry": "GTC",
"exit": "GTC",
}
```

7
docs/telegram-usage.md
View File

@ -82,6 +82,8 @@ Example configuration showing the different settings:
"warning": "on",
"startup": "off",
"entry": "silent",
"entry_fill": "on",
"entry_cancel": "silent",
"exit": {
"roi": "silent",
"emergency_exit": "on",
@ -90,11 +92,10 @@ Example configuration showing the different settings:
"trailing_stop_loss": "on",
"stop_loss": "on",
"stoploss_on_exchange": "on",
"custom_exit": "silent"
"custom_exit": "silent",
"partial_exit": "on"
},
"entry_cancel": "silent",
"exit_cancel": "on",
"entry_fill": "off",
"exit_fill": "off",
"protection_trigger": "off",
"protection_trigger_global": "on",

4
docs/utils.md
View File

@ -525,12 +525,14 @@ Requires a configuration with specified `pairlists` attribute.
Can be used to generate static pairlists to be used during backtesting / hyperopt.
```
usage: freqtrade test-pairlist [-h] [-v] [-c PATH]
usage: freqtrade test-pairlist [-h] [--userdir PATH] [-v] [-c PATH]
[--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]]
[-1] [--print-json] [--exchange EXCHANGE]
optional arguments:
-h, --help show this help message and exit
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
-c PATH, --config PATH
Specify configuration file (default:

4
docs/windows_installation.md
View File

@ -23,7 +23,7 @@ git clone https://github.com/freqtrade/freqtrade.git
Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which need to be downloaded and installed using `pip install TA_Lib-0.4.24-cp38-cp38-win_amd64.whl` (make sure to use the version matching your python version).
As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which need to be downloaded and installed using `pip install TA_Lib-0.4.25-cp38-cp38-win_amd64.whl` (make sure to use the version matching your python version).
Freqtrade provides these dependencies for the latest 3 Python versions (3.8, 3.9 and 3.10) and for 64bit Windows.
Other versions must be downloaded from the above link.
@ -34,7 +34,7 @@ python -m venv .env
.env\Scripts\activate.ps1
# optionally install ta-lib from wheel
# Eventually adjust the below filename to match the downloaded wheel
pip install build_helpers/TA_Lib-0.4.19-cp38-cp38-win_amd64.whl
pip install --find-links build_helpers\ TA-Lib
pip install -r requirements.txt
pip install -e .
freqtrade

1
environment.yml
View File

@ -34,6 +34,7 @@ dependencies:
- schedule
- python-dateutil
- joblib
- pyarrow
# ============================

2
freqtrade/__init__.py
View File

@ -1,5 +1,5 @@
""" Freqtrade bot """
__version__ = '2022.8'
__version__ = '2022.9'
if 'dev' in __version__:
try:

8
freqtrade/commands/arguments.py
View File

@ -53,8 +53,8 @@ ARGS_LIST_PAIRS = ["exchange", "print_list", "list_pairs_print_json", "print_one
"print_csv", "base_currencies", "quote_currencies", "list_pairs_all",
"trading_mode"]
ARGS_TEST_PAIRLIST = ["verbosity", "config", "quote_currencies", "print_one_column",
"list_pairs_print_json", "exchange"]
ARGS_TEST_PAIRLIST = ["user_data_dir", "verbosity", "config", "quote_currencies",
"print_one_column", "list_pairs_print_json", "exchange"]
ARGS_CREATE_USERDIR = ["user_data_dir", "reset"]
@ -62,9 +62,9 @@ ARGS_BUILD_CONFIG = ["config"]
ARGS_BUILD_STRATEGY = ["user_data_dir", "strategy", "template"]
ARGS_CONVERT_DATA = ["pairs", "format_from", "format_to", "erase"]
ARGS_CONVERT_DATA = ["pairs", "format_from", "format_to", "erase", "exchange"]
ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes", "exchange", "trading_mode",
ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes", "trading_mode",
"candle_types"]
ARGS_CONVERT_TRADES = ["pairs", "timeframes", "exchange", "dataformat_ohlcv", "dataformat_trades"]

1
freqtrade/commands/build_config_commands.py
View File

@ -211,6 +211,7 @@ def ask_user_config() -> Dict[str, Any]:
)
# Force JWT token to be a random string
answers['api_server_jwt_key'] = secrets.token_hex()
answers['api_server_ws_token'] = secrets.token_urlsafe(25)
return answers

9
freqtrade/commands/cli_options.py
View File

@ -69,7 +69,7 @@ AVAILABLE_CLI_OPTIONS = {
metavar='PATH',
),
"datadir": Arg(
'-d', '--datadir',
'-d', '--datadir', '--data-dir',
help='Path to directory with historical backtesting data.',
metavar='PATH',
),
@ -393,7 +393,8 @@ AVAILABLE_CLI_OPTIONS = {
# Download data
"pairs_file": Arg(
'--pairs-file',
help='File containing a list of pairs to download.',
help='File containing a list of pairs. '
'Takes precedence over --pairs or pairs configured in the configuration.',
metavar='FILE',
),
"days": Arg(
@ -439,7 +440,7 @@ AVAILABLE_CLI_OPTIONS = {
"dataformat_trades": Arg(
'--data-format-trades',
help='Storage format for downloaded trades data. (default: `jsongz`).',
choices=constants.AVAILABLE_DATAHANDLERS,
choices=constants.AVAILABLE_DATAHANDLERS_TRADES,
),
"show_timerange": Arg(
'--show-timerange',
@ -455,8 +456,6 @@ AVAILABLE_CLI_OPTIONS = {
'-t', '--timeframes',
help='Specify which tickers to download. Space-separated list. '
'Default: `1m 5m`.',
choices=['1m', '3m', '5m', '15m', '30m', '1h', '2h', '4h',
'6h', '8h', '12h', '1d', '3d', '1w', '2w', '1M', '1y'],
default=['1m', '5m'],
nargs='+',
),

2
freqtrade/commands/db_commands.py
View File

@ -4,7 +4,7 @@ from typing import Any, Dict
from sqlalchemy import func
from freqtrade.configuration.config_setup import setup_utils_configuration
from freqtrade.enums.runmode import RunMode
from freqtrade.enums import RunMode
logger = logging.getLogger(__name__)

20
freqtrade/commands/deploy_commands.py
View File

@ -36,24 +36,24 @@ def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: st
"""
fallback = 'full'
indicators = render_template_with_fallback(
templatefile=f"subtemplates/indicators_{subtemplate}.j2",
templatefallbackfile=f"subtemplates/indicators_{fallback}.j2",
templatefile=f"strategy_subtemplates/indicators_{subtemplate}.j2",
templatefallbackfile=f"strategy_subtemplates/indicators_{fallback}.j2",
)
buy_trend = render_template_with_fallback(
templatefile=f"subtemplates/buy_trend_{subtemplate}.j2",
templatefallbackfile=f"subtemplates/buy_trend_{fallback}.j2",
templatefile=f"strategy_subtemplates/buy_trend_{subtemplate}.j2",
templatefallbackfile=f"strategy_subtemplates/buy_trend_{fallback}.j2",
)
sell_trend = render_template_with_fallback(
templatefile=f"subtemplates/sell_trend_{subtemplate}.j2",
templatefallbackfile=f"subtemplates/sell_trend_{fallback}.j2",
templatefile=f"strategy_subtemplates/sell_trend_{subtemplate}.j2",
templatefallbackfile=f"strategy_subtemplates/sell_trend_{fallback}.j2",
)
plot_config = render_template_with_fallback(
templatefile=f"subtemplates/plot_config_{subtemplate}.j2",
templatefallbackfile=f"subtemplates/plot_config_{fallback}.j2",
templatefile=f"strategy_subtemplates/plot_config_{subtemplate}.j2",
templatefallbackfile=f"strategy_subtemplates/plot_config_{fallback}.j2",
)
additional_methods = render_template_with_fallback(
templatefile=f"subtemplates/strategy_methods_{subtemplate}.j2",
templatefallbackfile="subtemplates/strategy_methods_empty.j2",
templatefile=f"strategy_subtemplates/strategy_methods_{subtemplate}.j2",
templatefallbackfile="strategy_subtemplates/strategy_methods_empty.j2",
)
strategy_text = render_template(templatefile='base_strategy.py.j2',

4
freqtrade/configuration/check_exchange.py
View File

@ -1,6 +1,6 @@
import logging
from typing import Any, Dict
from freqtrade.constants import Config
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import (available_exchanges, is_exchange_known_ccxt,
@ -10,7 +10,7 @@ from freqtrade.exchange import (available_exchanges, is_exchange_known_ccxt,
logger = logging.getLogger(__name__)
def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
def check_exchange(config: Config, check_for_bad: bool = True) -> bool:
"""
Check if the exchange name in the config file is supported by Freqtrade
:param check_for_bad: if True, check the exchange against the list of known 'bad'

28
freqtrade/configuration/config_validation.py
View File

@ -1,4 +1,5 @@
import logging
from collections import Counter
from copy import deepcopy
from typing import Any, Dict
@ -84,6 +85,8 @@ def validate_config_consistency(conf: Dict[str, Any], preliminary: bool = False)
_validate_protections(conf)
_validate_unlimited_amount(conf)
_validate_ask_orderbook(conf)
_validate_freqai_hyperopt(conf)
_validate_consumers(conf)
validate_migrated_strategy_settings(conf)
# validate configuration before returning
@ -323,6 +326,31 @@ def _validate_pricing_rules(conf: Dict[str, Any]) -> None:
del conf['ask_strategy']
def _validate_freqai_hyperopt(conf: Dict[str, Any]) -> None:
freqai_enabled = conf.get('freqai', {}).get('enabled', False)
analyze_per_epoch = conf.get('analyze_per_epoch', False)
if analyze_per_epoch and freqai_enabled:
raise OperationalException(
'Using analyze-per-epoch parameter is not supported with a FreqAI strategy.')
def _validate_consumers(conf: Dict[str, Any]) -> None:
emc_conf = conf.get('external_message_consumer', {})
if emc_conf.get('enabled', False):
if len(emc_conf.get('producers', [])) < 1:
raise OperationalException("You must specify at least 1 Producer to connect to.")
producer_names = [p['name'] for p in emc_conf.get('producers', [])]
duplicates = [item for item, count in Counter(producer_names).items() if count > 1]
if duplicates:
raise OperationalException(
f"Producer names must be unique. Duplicate: {', '.join(duplicates)}")
if conf.get('process_only_new_candles', True):
# Warning here or require it?
logger.warning("To receive best performance with external data, "
"please set `process_only_new_candles` to False")
def _strategy_settings(conf: Dict[str, Any]) -> None:
process_deprecated_setting(conf, None, 'use_sell_signal', None, 'use_exit_signal')

31
freqtrade/configuration/configuration.py
View File

@ -13,6 +13,7 @@ from freqtrade.configuration.deprecated_settings import process_temporary_deprec
from freqtrade.configuration.directory_operations import create_datadir, create_userdata_dir
from freqtrade.configuration.environment_vars import enironment_vars_to_dict
from freqtrade.configuration.load_config import load_file, load_from_files
from freqtrade.constants import Config
from freqtrade.enums import NON_UTIL_MODES, TRADING_MODES, CandleType, RunMode, TradingMode
from freqtrade.exceptions import OperationalException
from freqtrade.loggers import setup_logging
@ -30,10 +31,10 @@ class Configuration:
def __init__(self, args: Dict[str, Any], runmode: RunMode = None) -> None:
self.args = args
self.config: Optional[Dict[str, Any]] = None
self.config: Optional[Config] = None
self.runmode = runmode
def get_config(self) -> Dict[str, Any]:
def get_config(self) -> Config:
"""
Return the config. Use this method to get the bot config
:return: Dict: Bot config
@ -65,7 +66,7 @@ class Configuration:
:return: Configuration dictionary
"""
# Load all configs
config: Dict[str, Any] = load_from_files(self.args.get("config", []))
config: Config = load_from_files(self.args.get("config", []))
# Load environment variables
env_data = enironment_vars_to_dict()
@ -108,7 +109,7 @@ class Configuration:
return config
def _process_logging_options(self, config: Dict[str, Any]) -> None:
def _process_logging_options(self, config: Config) -> None:
"""
Extract information for sys.argv and load logging configuration:
the -v/--verbose, --logfile options
@ -121,7 +122,7 @@ class Configuration:
setup_logging(config)
def _process_trading_options(self, config: Dict[str, Any]) -> None:
def _process_trading_options(self, config: Config) -> None:
if config['runmode'] not in TRADING_MODES:
return
@ -137,7 +138,7 @@ class Configuration:
logger.info(f'Using DB: "{parse_db_uri_for_logging(config["db_url"])}"')
def _process_common_options(self, config: Dict[str, Any]) -> None:
def _process_common_options(self, config: Config) -> None:
# Set strategy if not specified in config and or if it's non default
if self.args.get('strategy') or not config.get('strategy'):
@ -161,7 +162,7 @@ class Configuration:
if 'sd_notify' in self.args and self.args['sd_notify']:
config['internals'].update({'sd_notify': True})
def _process_datadir_options(self, config: Dict[str, Any]) -> None:
def _process_datadir_options(self, config: Config) -> None:
"""
Extract information for sys.argv and load directory configurations
--user-data, --datadir
@ -195,7 +196,7 @@ class Configuration:
config['exportfilename'] = (config['user_data_dir']
/ 'backtest_results')
def _process_optimize_options(self, config: Dict[str, Any]) -> None:
def _process_optimize_options(self, config: Config) -> None:
# This will override the strategy configuration
self._args_to_config(config, argname='timeframe',
@ -380,7 +381,7 @@ class Configuration:
self._args_to_config(config, argname="hyperopt_ignore_missing_space",
logstring="Paramter --ignore-missing-space detected: {}")
def _process_plot_options(self, config: Dict[str, Any]) -> None:
def _process_plot_options(self, config: Config) -> None:
self._args_to_config(config, argname='pairs',
logstring='Using pairs {}')
@ -432,7 +433,7 @@ class Configuration:
self._args_to_config(config, argname='show_timerange',
logstring='Detected --show-timerange')
def _process_data_options(self, config: Dict[str, Any]) -> None:
def _process_data_options(self, config: Config) -> None:
self._args_to_config(config, argname='new_pairs_days',
logstring='Detected --new-pairs-days: {}')
self._args_to_config(config, argname='trading_mode',
@ -443,7 +444,7 @@ class Configuration:
self._args_to_config(config, argname='candle_types',
logstring='Detected --candle-types: {}')
def _process_analyze_options(self, config: Dict[str, Any]) -> None:
def _process_analyze_options(self, config: Config) -> None:
self._args_to_config(config, argname='analysis_groups',
logstring='Analysis reason groups: {}')
@ -456,7 +457,7 @@ class Configuration:
self._args_to_config(config, argname='indicator_list',
logstring='Analysis indicator list: {}')
def _process_runmode(self, config: Dict[str, Any]) -> None:
def _process_runmode(self, config: Config) -> None:
self._args_to_config(config, argname='dry_run',
logstring='Parameter --dry-run detected, '
@ -469,7 +470,7 @@ class Configuration:
config.update({'runmode': self.runmode})
def _process_freqai_options(self, config: Dict[str, Any]) -> None:
def _process_freqai_options(self, config: Config) -> None:
self._args_to_config(config, argname='freqaimodel',
logstring='Using freqaimodel class name: {}')
@ -479,7 +480,7 @@ class Configuration:
return
def _args_to_config(self, config: Dict[str, Any], argname: str,
def _args_to_config(self, config: Config, argname: str,
logstring: str, logfun: Optional[Callable] = None,
deprecated_msg: Optional[str] = None) -> None:
"""
@ -502,7 +503,7 @@ class Configuration:
if deprecated_msg:
warnings.warn(f"DEPRECATED: {deprecated_msg}", DeprecationWarning)
def _resolve_pairs_list(self, config: Dict[str, Any]) -> None:
def _resolve_pairs_list(self, config: Config) -> None:
"""
Helper for download script.
Takes first found:

11
freqtrade/configuration/deprecated_settings.py
View File

@ -3,15 +3,16 @@ Functions to handle deprecated settings
"""
import logging
from typing import Any, Dict, Optional
from typing import Optional
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
def check_conflicting_settings(config: Dict[str, Any],
def check_conflicting_settings(config: Config,
section_old: Optional[str], name_old: str,
section_new: Optional[str], name_new: str) -> None:
section_new_config = config.get(section_new, {}) if section_new else config
@ -28,7 +29,7 @@ def check_conflicting_settings(config: Dict[str, Any],
)
def process_removed_setting(config: Dict[str, Any],
def process_removed_setting(config: Config,
section1: str, name1: str,
section2: Optional[str], name2: str) -> None:
"""
@ -47,7 +48,7 @@ def process_removed_setting(config: Dict[str, Any],
)
def process_deprecated_setting(config: Dict[str, Any],
def process_deprecated_setting(config: Config,
section_old: Optional[str], name_old: str,
section_new: Optional[str], name_new: str
) -> None:
@ -69,7 +70,7 @@ def process_deprecated_setting(config: Dict[str, Any],
del section_old_config[name_old]
def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
def process_temporary_deprecated_settings(config: Config) -> None:
# Kept for future deprecated / moved settings
# check_conflicting_settings(config, 'ask_strategy', 'use_sell_signal',

6
freqtrade/configuration/directory_operations.py
View File

@ -1,16 +1,16 @@
import logging
import shutil
from pathlib import Path
from typing import Any, Dict, Optional
from typing import Optional
from freqtrade.constants import USER_DATA_FILES
from freqtrade.constants import USER_DATA_FILES, Config
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> Path:
def create_datadir(config: Config, datadir: Optional[str] = None) -> Path:
folder = Path(datadir) if datadir else Path(f"{config['user_data_dir']}/data")
if not datadir:

4
freqtrade/configuration/load_config.py
View File

@ -10,7 +10,7 @@ from typing import Any, Dict, List
import rapidjson
from freqtrade.constants import MINIMAL_CONFIG
from freqtrade.constants import MINIMAL_CONFIG, Config
from freqtrade.exceptions import OperationalException
from freqtrade.misc import deep_merge_dicts
@ -80,7 +80,7 @@ def load_from_files(files: List[str], base_path: Path = None, level: int = 0) ->
Recursively load configuration files if specified.
Sub-files are assumed to be relative to the initial config.
"""
config: Dict[str, Any] = {}
config: Config = {}
if level > 5:
raise OperationalException("Config loop detected.")

66
freqtrade/constants.py
View File

@ -3,7 +3,7 @@
"""
bot constants
"""
from typing import List, Literal, Tuple
from typing import Any, Dict, List, Literal, Tuple
from freqtrade.enums import CandleType
@ -23,7 +23,8 @@ REQUIRED_ORDERTIF = ['entry', 'exit']
REQUIRED_ORDERTYPES = ['entry', 'exit', 'stoploss', 'stoploss_on_exchange']
PRICING_SIDES = ['ask', 'bid', 'same', 'other']
ORDERTYPE_POSSIBILITIES = ['limit', 'market']
ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
_ORDERTIF_POSSIBILITIES = ['GTC', 'FOK', 'IOC', 'PO']
ORDERTIF_POSSIBILITIES = _ORDERTIF_POSSIBILITIES + [t.lower() for t in _ORDERTIF_POSSIBILITIES]
HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily',
'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily',
@ -35,7 +36,8 @@ AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter',
'ShuffleFilter', 'SpreadFilter', 'VolatilityFilter']
AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5']
AVAILABLE_DATAHANDLERS_TRADES = ['json', 'jsongz', 'hdf5']
AVAILABLE_DATAHANDLERS = AVAILABLE_DATAHANDLERS_TRADES + ['feather', 'parquet']
BACKTEST_BREAKDOWNS = ['day', 'week', 'month']
BACKTEST_CACHE_AGE = ['none', 'day', 'week', 'month']
BACKTEST_CACHE_DEFAULT = 'day'
@ -242,6 +244,7 @@ CONF_SCHEMA = {
'exchange': {'$ref': '#/definitions/exchange'},
'edge': {'$ref': '#/definitions/edge'},
'freqai': {'$ref': '#/definitions/freqai'},
'external_message_consumer': {'$ref': '#/definitions/external_message_consumer'},
'experimental': {
'type': 'object',
'properties': {
@ -288,11 +291,12 @@ CONF_SCHEMA = {
'warning': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'startup': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'entry': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'entry_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'entry_fill': {'type': 'string',
'entry_fill': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
},
'entry_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS, },
'exit': {
'type': ['string', 'object'],
'additionalProperties': {
@ -300,12 +304,12 @@ CONF_SCHEMA = {
'enum': TELEGRAM_SETTING_OPTIONS
}
},
'exit_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'exit_fill': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'on'
},
'exit_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'protection_trigger': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
@ -314,14 +318,17 @@ CONF_SCHEMA = {
'protection_trigger_global': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'on'
},
'show_candle': {
'type': 'string',
'enum': ['off', 'ohlc'],
'default': 'off'
},
'strategy_msg': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'on'
},
}
},
@ -399,6 +406,7 @@ CONF_SCHEMA = {
},
'username': {'type': 'string'},
'password': {'type': 'string'},
'ws_token': {'type': ['string', 'array'], 'items': {'type': 'string'}},
'jwt_secret_key': {'type': 'string'},
'CORS_origins': {'type': 'array', 'items': {'type': 'string'}},
'verbosity': {'type': 'string', 'enum': ['error', 'info']},
@ -427,7 +435,7 @@ CONF_SCHEMA = {
},
'dataformat_trades': {
'type': 'string',
'enum': AVAILABLE_DATAHANDLERS,
'enum': AVAILABLE_DATAHANDLERS_TRADES,
'default': 'jsongz'
},
'position_adjustment_enable': {'type': 'boolean'},
@ -483,6 +491,47 @@ CONF_SCHEMA = {
},
'required': ['process_throttle_secs', 'allowed_risk']
},
'external_message_consumer': {
'type': 'object',
'properties': {
'enabled': {'type': 'boolean', 'default': False},
'producers': {
'type': 'array',
'items': {
'type': 'object',
'properties': {
'name': {'type': 'string'},
'host': {'type': 'string'},
'port': {
'type': 'integer',
'default': 8080,
'minimum': 0,
'maximum': 65535
},
'ws_token': {'type': 'string'},
},
'required': ['name', 'host', 'ws_token']
}
},
'wait_timeout': {'type': 'integer', 'minimum': 0},
'sleep_time': {'type': 'integer', 'minimum': 0},
'ping_timeout': {'type': 'integer', 'minimum': 0},
'remove_entry_exit_signals': {'type': 'boolean', 'default': False},
'initial_candle_limit': {
'type': 'integer',
'minimum': 0,
'maximum': 1500,
'default': 1500
},
'message_size_limit': { # In megabytes
'type': 'integer',
'minimum': 1,
'maxmium': 20,
'default': 8,
}
},
'required': ['producers']
},
"freqai": {
"type": "object",
"properties": {
@ -503,6 +552,7 @@ CONF_SCHEMA = {
"weight_factor": {"type": "number", "default": 0},
"principal_component_analysis": {"type": "boolean", "default": False},
"use_SVM_to_remove_outliers": {"type": "boolean", "default": False},
"plot_feature_importances": {"type": "integer", "default": 0},
"svm_params": {"type": "object",
"properties": {
"shuffle": {"type": "boolean", "default": False},
@ -602,3 +652,5 @@ LongShort = Literal['long', 'short']
EntryExit = Literal['entry', 'exit']
BuySell = Literal['buy', 'sell']
MakerTaker = Literal['maker', 'taker']
Config = Dict[str, Any]

8
freqtrade/data/btanalysis.py
View File

@ -284,7 +284,7 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
df['enter_tag'] = df['buy_tag']
df = df.drop(['buy_tag'], axis=1)
if 'orders' not in df.columns:
df.loc[:, 'orders'] = None
df['orders'] = None
else:
# old format - only with lists.
@ -341,9 +341,9 @@ def trade_list_to_dataframe(trades: List[LocalTrade]) -> pd.DataFrame:
"""
df = pd.DataFrame.from_records([t.to_json(True) for t in trades], columns=BT_DATA_COLUMNS)
if len(df) > 0:
df.loc[:, 'close_date'] = pd.to_datetime(df['close_date'], utc=True)
df.loc[:, 'open_date'] = pd.to_datetime(df['open_date'], utc=True)
df.loc[:, 'close_rate'] = df['close_rate'].astype('float64')
df['close_date'] = pd.to_datetime(df['close_date'], utc=True)
df['open_date'] = pd.to_datetime(df['open_date'], utc=True)
df['close_rate'] = df['close_rate'].astype('float64')
return df

11
freqtrade/data/converter.py
View File

@ -5,12 +5,12 @@ import itertools
import logging
from datetime import datetime, timezone
from operator import itemgetter
from typing import Any, Dict, List
from typing import Dict, List
import pandas as pd
from pandas import DataFrame, to_datetime
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TradeList
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, Config, TradeList
from freqtrade.enums import CandleType
@ -237,7 +237,7 @@ def trades_to_ohlcv(trades: TradeList, timeframe: str) -> DataFrame:
return df_new.loc[:, DEFAULT_DATAFRAME_COLUMNS]
def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
def convert_trades_format(config: Config, convert_from: str, convert_to: str, erase: bool):
"""
Convert trades from one format to another format.
:param config: Config dictionary
@ -263,7 +263,7 @@ def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to:
def convert_ohlcv_format(
config: Dict[str, Any],
config: Config,
convert_from: str,
convert_to: str,
erase: bool,
@ -292,6 +292,7 @@ def convert_ohlcv_format(
timeframe,
candle_type=candle_type
))
config['pairs'] = sorted(set(config['pairs']))
logger.info(f"Converting candle (OHLCV) data for {config['pairs']}")
for timeframe in timeframes:
@ -302,7 +303,7 @@ def convert_ohlcv_format(
drop_incomplete=False,
startup_candles=0,
candle_type=candle_type)
logger.info(f"Converting {len(data)} {candle_type} candles for {pair}")
logger.info(f"Converting {len(data)} {timeframe} {candle_type} candles for {pair}")
if len(data) > 0:
trg.ohlcv_store(
pair=pair,

161
freqtrade/data/dataprovider.py
View File

@ -12,11 +12,12 @@ from typing import Any, Dict, List, Optional, Tuple
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import ListPairsWithTimeframes, PairWithTimeframe
from freqtrade.constants import Config, ListPairsWithTimeframes, PairWithTimeframe
from freqtrade.data.history import load_pair_history
from freqtrade.enums import CandleType, RunMode
from freqtrade.enums import CandleType, RPCMessageType, RunMode
from freqtrade.exceptions import ExchangeError, OperationalException
from freqtrade.exchange import Exchange, timeframe_to_seconds
from freqtrade.rpc import RPCManager
from freqtrade.util import PeriodicCache
@ -28,17 +29,33 @@ MAX_DATAFRAME_CANDLES = 1000
class DataProvider:
def __init__(self, config: dict, exchange: Optional[Exchange], pairlists=None) -> None:
def __init__(
self,
config: Config,
exchange: Optional[Exchange],
pairlists=None,
rpc: Optional[RPCManager] = None
) -> None:
self._config = config
self._exchange = exchange
self._pairlists = pairlists
self.__rpc = rpc
self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {}
self.__slice_index: Optional[int] = None
self.__cached_pairs_backtesting: Dict[PairWithTimeframe, DataFrame] = {}
self.__producer_pairs_df: Dict[str,
Dict[PairWithTimeframe, Tuple[DataFrame, datetime]]] = {}
self.__producer_pairs: Dict[str, List[str]] = {}
self._msg_queue: deque = deque()
self._default_candle_type = self._config.get('candle_type_def', CandleType.SPOT)
self._default_timeframe = self._config.get('timeframe', '1h')
self.__msg_cache = PeriodicCache(
maxsize=1000, ttl=timeframe_to_seconds(self._config.get('timeframe', '1h')))
maxsize=1000, ttl=timeframe_to_seconds(self._default_timeframe))
self.producers = self._config.get('external_message_consumer', {}).get('producers', [])
self.external_data_enabled = len(self.producers) > 0
def _set_dataframe_max_index(self, limit_index: int):
"""
@ -63,9 +80,110 @@ class DataProvider:
:param dataframe: analyzed dataframe
:param candle_type: Any of the enum CandleType (must match trading mode!)
"""
self.__cached_pairs[(pair, timeframe, candle_type)] = (
pair_key = (pair, timeframe, candle_type)
self.__cached_pairs[pair_key] = (
dataframe, datetime.now(timezone.utc))
# For multiple producers we will want to merge the pairlists instead of overwriting
def _set_producer_pairs(self, pairlist: List[str], producer_name: str = "default"):
"""
Set the pairs received to later be used.
:param pairlist: List of pairs
"""
self.__producer_pairs[producer_name] = pairlist
def get_producer_pairs(self, producer_name: str = "default") -> List[str]:
"""
Get the pairs cached from the producer
:returns: List of pairs
"""
return self.__producer_pairs.get(producer_name, []).copy()
def _emit_df(
self,
pair_key: PairWithTimeframe,
dataframe: DataFrame
) -> None:
"""
Send this dataframe as an ANALYZED_DF message to RPC
:param pair_key: PairWithTimeframe tuple
:param data: Tuple containing the DataFrame and the datetime it was cached
"""
if self.__rpc:
self.__rpc.send_msg(
{
'type': RPCMessageType.ANALYZED_DF,
'data': {
'key': pair_key,
'df': dataframe,
'la': datetime.now(timezone.utc)
}
}
)
def _add_external_df(
self,
pair: str,
dataframe: DataFrame,
last_analyzed: datetime,
timeframe: str,
candle_type: CandleType,
producer_name: str = "default"
) -> None:
"""
Add the pair data to this class from an external source.
:param pair: pair to get the data for
:param timeframe: Timeframe to get data for
:param candle_type: Any of the enum CandleType (must match trading mode!)
"""
pair_key = (pair, timeframe, candle_type)
if producer_name not in self.__producer_pairs_df:
self.__producer_pairs_df[producer_name] = {}
_last_analyzed = datetime.now(timezone.utc) if not last_analyzed else last_analyzed
self.__producer_pairs_df[producer_name][pair_key] = (dataframe, _last_analyzed)
logger.debug(f"External DataFrame for {pair_key} from {producer_name} added.")
def get_producer_df(
self,
pair: str,
timeframe: Optional[str] = None,
candle_type: Optional[CandleType] = None,
producer_name: str = "default"
) -> Tuple[DataFrame, datetime]:
"""
Get the pair data from producers.
:param pair: pair to get the data for
:param timeframe: Timeframe to get data for
:param candle_type: Any of the enum CandleType (must match trading mode!)
:returns: Tuple of the DataFrame and last analyzed timestamp
"""
_timeframe = self._default_timeframe if not timeframe else timeframe
_candle_type = self._default_candle_type if not candle_type else candle_type
pair_key = (pair, _timeframe, _candle_type)
# If we have no data from this Producer yet
if producer_name not in self.__producer_pairs_df:
# We don't have this data yet, return empty DataFrame and datetime (01-01-1970)
return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
# If we do have data from that Producer, but no data on this pair_key
if pair_key not in self.__producer_pairs_df[producer_name]:
# We don't have this data yet, return empty DataFrame and datetime (01-01-1970)
return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
# We have it, return this data
df, la = self.__producer_pairs_df[producer_name][pair_key]
return (df.copy(), la)
def add_pairlisthandler(self, pairlists) -> None:
"""
Allow adding pairlisthandler after initialization
@ -86,14 +204,16 @@ class DataProvider:
"""
_candle_type = CandleType.from_string(
candle_type) if candle_type != '' else self._config['candle_type_def']
saved_pair = (pair, str(timeframe), _candle_type)
saved_pair: PairWithTimeframe = (pair, str(timeframe), _candle_type)
if saved_pair not in self.__cached_pairs_backtesting:
timerange = TimeRange.parse_timerange(None if self._config.get(
'timerange') is None else str(self._config.get('timerange')))
# Move informative start time respecting startup_candle_count
timerange.subtract_start(
timeframe_to_seconds(str(timeframe)) * self._config.get('startup_candle_count', 0)
)
# It is not necessary to add the training candles, as they
# were already added at the beginning of the backtest.
startup_candles = self.get_required_startup(str(timeframe), False)
tf_seconds = timeframe_to_seconds(str(timeframe))
timerange.subtract_start(tf_seconds * startup_candles)
self.__cached_pairs_backtesting[saved_pair] = load_pair_history(
pair=pair,
timeframe=timeframe or self._config['timeframe'],
@ -105,6 +225,23 @@ class DataProvider:
)
return self.__cached_pairs_backtesting[saved_pair].copy()
def get_required_startup(self, timeframe: str, add_train_candles: bool = True) -> int:
freqai_config = self._config.get('freqai', {})
if not freqai_config.get('enabled', False):
return self._config.get('startup_candle_count', 0)
else:
startup_candles = self._config.get('startup_candle_count', 0)
indicator_periods = freqai_config['feature_parameters']['indicator_periods_candles']
# make sure the startupcandles is at least the set maximum indicator periods
self._config['startup_candle_count'] = max(startup_candles, max(indicator_periods))
tf_seconds = timeframe_to_seconds(timeframe)
train_candles = 0
if add_train_candles:
train_candles = freqai_config['train_period_days'] * 86400 / tf_seconds
total_candles = int(self._config['startup_candle_count'] + train_candles)
logger.info(f'Increasing startup_candle_count for freqai to {total_candles}')
return total_candles
def get_pair_dataframe(
self,
pair: str,
@ -181,7 +318,9 @@ class DataProvider:
Clear pair dataframe cache.
"""
self.__cached_pairs = {}
self.__cached_pairs_backtesting = {}
# Don't reset backtesting pairs -
# otherwise they're reloaded each time during hyperopt due to with analyze_per_epoch
# self.__cached_pairs_backtesting = {}
self.__slice_index = 0
# Exchange functions

130
freqtrade/data/history/featherdatahandler.py Normal file
View File

@ -0,0 +1,130 @@
import logging
from typing import Optional
from pandas import DataFrame, read_feather, to_datetime
from freqtrade.configuration import TimeRange
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
from freqtrade.enums import CandleType
from .idatahandler import IDataHandler
logger = logging.getLogger(__name__)
class FeatherDataHandler(IDataHandler):
_columns = DEFAULT_DATAFRAME_COLUMNS
def ohlcv_store(
self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType) -> None:
"""
Store data in json format "values".
format looks as follows:
[[<date>,<open>,<high>,<low>,<close>]]
:param pair: Pair - used to generate filename
:param timeframe: Timeframe - used to generate filename
:param data: Dataframe containing OHLCV data
:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: None
"""
filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
self.create_dir_if_needed(filename)
data.reset_index(drop=True).loc[:, self._columns].to_feather(
filename, compression_level=9, compression='lz4')
def _ohlcv_load(self, pair: str, timeframe: str,
timerange: Optional[TimeRange], candle_type: CandleType
) -> DataFrame:
"""
Internal method used to load data for one pair from disk.
Implements the loading and conversion to a Pandas dataframe.
Timerange trimming and dataframe validation happens outside of this method.
:param pair: Pair to load data
:param timeframe: Timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange.
Optionally implemented by subclasses to avoid loading
all data where possible.
:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: DataFrame with ohlcv data, or empty DataFrame
"""
filename = self._pair_data_filename(
self._datadir, pair, timeframe, candle_type=candle_type)
if not filename.exists():
# Fallback mode for 1M files
filename = self._pair_data_filename(
self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True)
if not filename.exists():
return DataFrame(columns=self._columns)
pairdata = read_feather(filename)
pairdata.columns = self._columns
pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
'low': 'float', 'close': 'float', 'volume': 'float'})
pairdata['date'] = to_datetime(pairdata['date'],
unit='ms',
utc=True,
infer_datetime_format=True)
return pairdata
def ohlcv_append(
self,
pair: str,
timeframe: str,
data: DataFrame,
candle_type: CandleType
) -> None:
"""
Append data to existing data structures
:param pair: Pair
:param timeframe: Timeframe this ohlcv data is for
:param data: Data to append.
:param candle_type: Any of the enum CandleType (must match trading mode!)
"""
raise NotImplementedError()
def trades_store(self, pair: str, data: TradeList) -> None:
"""
Store trades data (list of Dicts) to file
:param pair: Pair - used for filename
:param data: List of Lists containing trade data,
column sequence as in DEFAULT_TRADES_COLUMNS
"""
# filename = self._pair_trades_filename(self._datadir, pair)
raise NotImplementedError()
# array = pa.array(data)
# array
# feather.write_feather(data, filename)
def trades_append(self, pair: str, data: TradeList):
"""
Append data to existing files
:param pair: Pair - used for filename
:param data: List of Lists containing trade data,
column sequence as in DEFAULT_TRADES_COLUMNS
"""
raise NotImplementedError()
def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
"""
Load a pair from file, either .json.gz or .json
# TODO: respect timerange ...
:param pair: Load trades for this pair
:param timerange: Timerange to load trades for - currently not implemented
:return: List of trades
"""
raise NotImplementedError()
# filename = self._pair_trades_filename(self._datadir, pair)
# tradesdata = misc.file_load_json(filename)
# if not tradesdata:
# return []
# return tradesdata
@classmethod
def _get_file_extension(cls):
return "feather"

37
freqtrade/data/history/hdf5datahandler.py
View File

@ -1,7 +1,5 @@
import logging
import re
from pathlib import Path
from typing import List, Optional
from typing import Optional
import numpy as np
import pandas as pd
@ -20,26 +18,6 @@ class HDF5DataHandler(IDataHandler):
_columns = DEFAULT_DATAFRAME_COLUMNS
@classmethod
def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]:
"""
Returns a list of all pairs with ohlcv data available in this datadir
for the specified timeframe
:param datadir: Directory to search for ohlcv files
:param timeframe: Timeframe to search pairs for
:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: List of Pairs
"""
candle = ""
if candle_type != CandleType.SPOT:
datadir = datadir.joinpath('futures')
candle = f"-{candle_type}"
_tmp = [re.search(r'^(\S+)(?=\-' + timeframe + candle + '.h5)', p.name)
for p in datadir.glob(f"*{timeframe}{candle}.h5")]
# Check if regex found something and only return these results
return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
def ohlcv_store(
self, pair: str, timeframe: str, data: pd.DataFrame, candle_type: CandleType) -> None:
"""
@ -103,6 +81,7 @@ class HDF5DataHandler(IDataHandler):
raise ValueError("Wrong dataframe format")
pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
'low': 'float', 'close': 'float', 'volume': 'float'})
pairdata = pairdata.reset_index(drop=True)
return pairdata
def ohlcv_append(
@ -121,18 +100,6 @@ class HDF5DataHandler(IDataHandler):
"""
raise NotImplementedError()
@classmethod
def trades_get_pairs(cls, datadir: Path) -> List[str]:
"""
Returns a list of all pairs for which trade data is available in this
:param datadir: Directory to search for ohlcv files
:return: List of Pairs
"""
_tmp = [re.search(r'^(\S+)(?=\-trades.h5)', p.name)
for p in datadir.glob("*trades.h5")]
# Check if regex found something and only return these results to avoid exceptions.
return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
def trades_store(self, pair: str, data: TradeList) -> None:
"""
Store trades data (list of Dicts) to file

8
freqtrade/data/history/history_utils.py
View File

@ -228,9 +228,9 @@ def _download_pair_history(pair: str, *,
)
logger.debug("Current Start: %s",
f"{data.iloc[0]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
f"{data.iloc[0]['date']:DATETIME_PRINT_FORMAT}" if not data.empty else 'None')
logger.debug("Current End: %s",
f"{data.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
f"{data.iloc[-1]['date']:DATETIME_PRINT_FORMAT}" if not data.empty else 'None')
# Default since_ms to 30 days if nothing is given
new_data = exchange.get_historic_ohlcv(pair=pair,
@ -254,9 +254,9 @@ def _download_pair_history(pair: str, *,
fill_missing=False, drop_incomplete=False)
logger.debug("New Start: %s",
f"{data.iloc[0]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
f"{data.iloc[0]['date']:DATETIME_PRINT_FORMAT}" if not data.empty else 'None')
logger.debug("New End: %s",
f"{data.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')
f"{data.iloc[-1]['date']:DATETIME_PRINT_FORMAT}" if not data.empty else 'None')
data_handler.ohlcv_store(pair, timeframe, data=data, candle_type=candle_type)
return True

28
freqtrade/data/history/idatahandler.py
View File

@ -26,7 +26,7 @@ logger = logging.getLogger(__name__)
class IDataHandler(ABC):
_OHLCV_REGEX = r'^([a-zA-Z_-]+)\-(\d+[a-zA-Z]{1,2})\-?([a-zA-Z_]*)?(?=\.)'
_OHLCV_REGEX = r'^([a-zA-Z_\d-]+)\-(\d+[a-zA-Z]{1,2})\-?([a-zA-Z_]*)?(?=\.)'
def __init__(self, datadir: Path) -> None:
self._datadir = datadir
@ -61,7 +61,6 @@ class IDataHandler(ABC):
) for match in _tmp if match and len(match.groups()) > 1]
@classmethod
@abstractmethod
def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]:
"""
Returns a list of all pairs with ohlcv data available in this datadir
@ -71,6 +70,15 @@ class IDataHandler(ABC):
:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: List of Pairs
"""
candle = ""
if candle_type != CandleType.SPOT:
datadir = datadir.joinpath('futures')
candle = f"-{candle_type}"
ext = cls._get_file_extension()
_tmp = [re.search(r'^(\S+)(?=\-' + timeframe + candle + f'.{ext})', p.name)
for p in datadir.glob(f"*{timeframe}{candle}.{ext}")]
# Check if regex found something and only return these results
return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
@abstractmethod
def ohlcv_store(
@ -144,13 +152,17 @@ class IDataHandler(ABC):
"""
@classmethod
@abstractmethod
def trades_get_pairs(cls, datadir: Path) -> List[str]:
"""
Returns a list of all pairs for which trade data is available in this
:param datadir: Directory to search for ohlcv files
:return: List of Pairs
"""
_ext = cls._get_file_extension()
_tmp = [re.search(r'^(\S+)(?=\-trades.' + _ext + ')', p.name)
for p in datadir.glob(f"*trades.{_ext}")]
# Check if regex found something and only return these results to avoid exceptions.
return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
@abstractmethod
def trades_store(self, pair: str, data: TradeList) -> None:
@ -255,12 +267,12 @@ class IDataHandler(ABC):
Rebuild pair name from filename
Assumes a asset name of max. 7 length to also support BTC-PERP and BTC-PERP:USD names.
"""
res = re.sub(r'^(([A-Za-z]{1,10})|^([A-Za-z\-]{1,6}))(_)', r'\g<1>/', pair, 1)
res = re.sub(r'^(([A-Za-z\d]{1,10})|^([A-Za-z\-]{1,6}))(_)', r'\g<1>/', pair, 1)
res = re.sub('_', ':', res, 1)
return res
def ohlcv_load(self, pair, timeframe: str,
candle_type: CandleType,
candle_type: CandleType, *,
timerange: Optional[TimeRange] = None,
fill_missing: bool = True,
drop_incomplete: bool = True,
@ -363,6 +375,12 @@ def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
elif datatype == 'hdf5':
from .hdf5datahandler import HDF5DataHandler
return HDF5DataHandler
elif datatype == 'feather':
from .featherdatahandler import FeatherDataHandler
return FeatherDataHandler
elif datatype == 'parquet':
from .parquetdatahandler import ParquetDataHandler
return ParquetDataHandler
else:
raise ValueError(f"No datahandler for datatype {datatype} available.")

36
freqtrade/data/history/jsondatahandler.py
View File

@ -1,7 +1,5 @@
import logging
import re
from pathlib import Path
from typing import List, Optional
from typing import Optional
import numpy as np
from pandas import DataFrame, read_json, to_datetime
@ -23,26 +21,6 @@ class JsonDataHandler(IDataHandler):
_use_zip = False
_columns = DEFAULT_DATAFRAME_COLUMNS
@classmethod
def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]:
"""
Returns a list of all pairs with ohlcv data available in this datadir
for the specified timeframe
:param datadir: Directory to search for ohlcv files
:param timeframe: Timeframe to search pairs for
:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: List of Pairs
"""
candle = ""
if candle_type != CandleType.SPOT:
datadir = datadir.joinpath('futures')
candle = f"-{candle_type}"
_tmp = [re.search(r'^(\S+)(?=\-' + timeframe + candle + '.json)', p.name)
for p in datadir.glob(f"*{timeframe}{candle}.{cls._get_file_extension()}")]
# Check if regex found something and only return these results
return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
def ohlcv_store(
self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType) -> None:
"""
@ -119,18 +97,6 @@ class JsonDataHandler(IDataHandler):
"""
raise NotImplementedError()
@classmethod
def trades_get_pairs(cls, datadir: Path) -> List[str]:
"""
Returns a list of all pairs for which trade data is available in this
:param datadir: Directory to search for ohlcv files
:return: List of Pairs
"""
_tmp = [re.search(r'^(\S+)(?=\-trades.json)', p.name)
for p in datadir.glob(f"*trades.{cls._get_file_extension()}")]
# Check if regex found something and only return these results to avoid exceptions.
return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
def trades_store(self, pair: str, data: TradeList) -> None:
"""
Store trades data (list of Dicts) to file

129
freqtrade/data/history/parquetdatahandler.py Normal file
View File

@ -0,0 +1,129 @@
import logging
from typing import Optional
from pandas import DataFrame, read_parquet, to_datetime
from freqtrade.configuration import TimeRange
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
from freqtrade.enums import CandleType
from .idatahandler import IDataHandler
logger = logging.getLogger(__name__)
class ParquetDataHandler(IDataHandler):
_columns = DEFAULT_DATAFRAME_COLUMNS
def ohlcv_store(
self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType) -> None:
"""
Store data in json format "values".
format looks as follows:
[[<date>,<open>,<high>,<low>,<close>]]
:param pair: Pair - used to generate filename
:param timeframe: Timeframe - used to generate filename
:param data: Dataframe containing OHLCV data
:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: None
"""
filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
self.create_dir_if_needed(filename)
data.reset_index(drop=True).loc[:, self._columns].to_parquet(filename)
def _ohlcv_load(self, pair: str, timeframe: str,
timerange: Optional[TimeRange], candle_type: CandleType
) -> DataFrame:
"""
Internal method used to load data for one pair from disk.
Implements the loading and conversion to a Pandas dataframe.
Timerange trimming and dataframe validation happens outside of this method.
:param pair: Pair to load data
:param timeframe: Timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange.
Optionally implemented by subclasses to avoid loading
all data where possible.
:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: DataFrame with ohlcv data, or empty DataFrame
"""
filename = self._pair_data_filename(
self._datadir, pair, timeframe, candle_type=candle_type)
if not filename.exists():
# Fallback mode for 1M files
filename = self._pair_data_filename(
self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True)
if not filename.exists():
return DataFrame(columns=self._columns)
pairdata = read_parquet(filename)
pairdata.columns = self._columns
pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
'low': 'float', 'close': 'float', 'volume': 'float'})
pairdata['date'] = to_datetime(pairdata['date'],
unit='ms',
utc=True,
infer_datetime_format=True)
return pairdata
def ohlcv_append(
self,
pair: str,
timeframe: str,
data: DataFrame,
candle_type: CandleType
) -> None:
"""
Append data to existing data structures
:param pair: Pair
:param timeframe: Timeframe this ohlcv data is for
:param data: Data to append.
:param candle_type: Any of the enum CandleType (must match trading mode!)
"""
raise NotImplementedError()
def trades_store(self, pair: str, data: TradeList) -> None:
"""
Store trades data (list of Dicts) to file
:param pair: Pair - used for filename
:param data: List of Lists containing trade data,
column sequence as in DEFAULT_TRADES_COLUMNS
"""
# filename = self._pair_trades_filename(self._datadir, pair)
raise NotImplementedError()
# array = pa.array(data)
# array
# feather.write_feather(data, filename)
def trades_append(self, pair: str, data: TradeList):
"""
Append data to existing files
:param pair: Pair - used for filename
:param data: List of Lists containing trade data,
column sequence as in DEFAULT_TRADES_COLUMNS
"""
raise NotImplementedError()
def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
"""
Load a pair from file, either .json.gz or .json
# TODO: respect timerange ...
:param pair: Load trades for this pair
:param timerange: Timerange to load trades for - currently not implemented
:return: List of trades
"""
raise NotImplementedError()
# filename = self._pair_trades_filename(self._datadir, pair)
# tradesdata = misc.file_load_json(filename)
# if not tradesdata:
# return []
# return tradesdata
@classmethod
def _get_file_extension(cls):
return "parquet"

5
freqtrade/edge/edge_positioning.py
View File

@ -11,7 +11,7 @@ import utils_find_1st as utf1st
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT
from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT, Config
from freqtrade.data.history import get_timerange, load_data, refresh_data
from freqtrade.enums import CandleType, ExitType, RunMode
from freqtrade.exceptions import OperationalException
@ -42,10 +42,9 @@ class Edge:
Author: https://github.com/mishaker
"""
config: Dict = {}
_cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
def __init__(self, config: Dict[str, Any], exchange, strategy) -> None:
def __init__(self, config: Config, exchange, strategy) -> None:
self.config = config
self.exchange = exchange

2
freqtrade/enums/__init__.py
View File

@ -6,7 +6,7 @@ from freqtrade.enums.exittype import ExitType
from freqtrade.enums.hyperoptstate import HyperoptState
from freqtrade.enums.marginmode import MarginMode
from freqtrade.enums.ordertypevalue import OrderTypeValues
from freqtrade.enums.rpcmessagetype import RPCMessageType
from freqtrade.enums.rpcmessagetype import RPCMessageType, RPCRequestType
from freqtrade.enums.runmode import NON_UTIL_MODES, OPTIMIZE_MODES, TRADING_MODES, RunMode
from freqtrade.enums.signaltype import SignalDirection, SignalTagType, SignalType
from freqtrade.enums.state import State

13
freqtrade/enums/rpcmessagetype.py
View File

@ -1,7 +1,7 @@
from enum import Enum
class RPCMessageType(Enum):
class RPCMessageType(str, Enum):
STATUS = 'status'
WARNING = 'warning'
STARTUP = 'startup'
@ -19,8 +19,19 @@ class RPCMessageType(Enum):
STRATEGY_MSG = 'strategy_msg'
WHITELIST = 'whitelist'
ANALYZED_DF = 'analyzed_df'
def __repr__(self):
return self.value
def __str__(self):
return self.value
# Enum for parsing requests from ws consumers
class RPCRequestType(str, Enum):
SUBSCRIBE = 'subscribe'
WHITELIST = 'whitelist'
ANALYZED_DF = 'analyzed_df'

15
freqtrade/exchange/binance.py
View File

@ -1,5 +1,4 @@
""" Binance exchange subclass """
import json
import logging
from datetime import datetime
from pathlib import Path
@ -12,7 +11,7 @@ from freqtrade.enums import CandleType, MarginMode, TradingMode
from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import retrier
from freqtrade.misc import deep_merge_dicts
from freqtrade.misc import deep_merge_dicts, json_load
logger = logging.getLogger(__name__)
@ -23,8 +22,7 @@ class Binance(Exchange):
_ft_has: Dict = {
"stoploss_on_exchange": True,
"stoploss_order_types": {"limit": "stop_loss_limit"},
"order_time_in_force": ['gtc', 'fok', 'ioc'],
"time_in_force_parameter": "timeInForce",
"order_time_in_force": ['GTC', 'FOK', 'IOC'],
"ohlcv_candle_limit": 1000,
"trades_pagination": "id",
"trades_pagination_arg": "fromId",
@ -32,7 +30,7 @@ class Binance(Exchange):
"ccxt_futures_name": "future"
}
_ft_has_futures: Dict = {
"stoploss_order_types": {"limit": "stop"},
"stoploss_order_types": {"limit": "limit", "market": "market"},
"tickers_have_price": False,
}
@ -49,13 +47,12 @@ class Binance(Exchange):
Returns True if adjustment is necessary.
:param side: "buy" or "sell"
"""
ordertype = 'stop' if self.trading_mode == TradingMode.FUTURES else 'stop_loss_limit'
order_types = ('stop_loss_limit', 'stop', 'stop_market')
return (
order.get('stopPrice', None) is None
or (
order['type'] == ordertype
order['type'] in order_types
and (
(side == "sell" and stop_loss > float(order['stopPrice'])) or
(side == "buy" and stop_loss < float(order['stopPrice']))
@ -202,7 +199,7 @@ class Binance(Exchange):
Path(__file__).parent / 'binance_leverage_tiers.json'
)
with open(leverage_tiers_path) as json_file:
return json.load(json_file)
return json_load(json_file)
else:
try:
return self._api.fetch_leverage_tiers()

1118
freqtrade/exchange/binance_leverage_tiers.json
View File

File diff suppressed because it is too large Load Diff

82
freqtrade/exchange/exchange.py
View File

@ -21,7 +21,8 @@ from dateutil import parser
from pandas import DataFrame
from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BuySell,
EntryExit, ListPairsWithTimeframes, MakerTaker, PairWithTimeframe)
Config, EntryExit, ListPairsWithTimeframes, MakerTaker,
PairWithTimeframe)
from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
@ -62,7 +63,7 @@ class Exchange:
# or by specifying them in the configuration.
_ft_has_default: Dict = {
"stoploss_on_exchange": False,
"order_time_in_force": ["gtc"],
"order_time_in_force": ["GTC"],
"time_in_force_parameter": "timeInForce",
"ohlcv_params": {},
"ohlcv_candle_limit": 500,
@ -91,7 +92,7 @@ class Exchange:
# TradingMode.SPOT always supported and not required in this list
]
def __init__(self, config: Dict[str, Any], validate: bool = True,
def __init__(self, config: Config, validate: bool = True,
load_leverage_tiers: bool = False) -> None:
"""
Initializes this module with the given config,
@ -108,7 +109,7 @@ class Exchange:
self._loop_lock = Lock()
self.loop = asyncio.new_event_loop()
asyncio.set_event_loop(self.loop)
self._config: Dict = {}
self._config: Config = {}
self._config.update(config)
@ -205,7 +206,7 @@ class Exchange:
logger.debug("Exchange object destroyed, closing async loop")
if (self._api_async and inspect.iscoroutinefunction(self._api_async.close)
and self._api_async.session):
logger.info("Closing async ccxt session.")
logger.debug("Closing async ccxt session.")
self.loop.run_until_complete(self._api_async.close())
def validate_config(self, config):
@ -446,6 +447,15 @@ class Exchange:
contract_size = self.get_contract_size(pair)
return contracts_to_amount(num_contracts, contract_size)
def amount_to_contract_precision(self, pair: str, amount: float) -> float:
"""
Helper wrapper around amount_to_contract_precision
"""
contract_size = self.get_contract_size(pair)
return amount_to_contract_precision(amount, self.get_precision_amount(pair),
self.precisionMode, contract_size)
def set_sandbox(self, api: ccxt.Exchange, exchange_config: dict, name: str) -> None:
if exchange_config.get('sandbox'):
if api.urls.get('test'):
@ -611,7 +621,7 @@ class Exchange:
"""
Checks if order time in force configured in strategy/config are supported
"""
if any(v not in self._ft_has["order_time_in_force"]
if any(v.upper() not in self._ft_has["order_time_in_force"]
for k, v in order_time_in_force.items()):
raise OperationalException(
f'Time in force policies are not supported for {self.name} yet.')
@ -989,12 +999,12 @@ class Exchange:
ordertype: str,
leverage: float,
reduceOnly: bool,
time_in_force: str = 'gtc',
time_in_force: str = 'GTC',
) -> Dict:
params = self._params.copy()
if time_in_force != 'gtc' and ordertype != 'market':
if time_in_force != 'GTC' and ordertype != 'market':
param = self._ft_has.get('time_in_force_parameter', '')
params.update({param: time_in_force})
params.update({param: time_in_force.upper()})
if reduceOnly:
params.update({'reduceOnly': True})
return params
@ -1009,7 +1019,7 @@ class Exchange:
rate: float,
leverage: float,
reduceOnly: bool = False,
time_in_force: str = 'gtc',
time_in_force: str = 'GTC',
) -> Dict:
if self._config['dry_run']:
dry_order = self.create_dry_run_order(
@ -2295,7 +2305,7 @@ class Exchange:
updated = tiers.get('updated')
if updated:
updated_dt = parser.parse(updated)
if updated_dt < datetime.now(timezone.utc) - timedelta(days=1):
if updated_dt < datetime.now(timezone.utc) - timedelta(weeks=4):
logger.info("Cached leverage tiers are outdated. Will update.")
return None
return tiers['data']
@ -2432,36 +2442,6 @@ class Exchange:
"""
return 0.0
def get_liquidation_price(
self,
pair: str,
open_rate: float,
amount: float, # quote currency, includes leverage
stake_amount: float,
leverage: float,
is_short: bool
) -> Optional[float]:
if self.trading_mode in TradingMode.SPOT:
return None
elif (
self.trading_mode == TradingMode.FUTURES
):
isolated_liq = self.get_or_calculate_liquidation_price(
pair=pair,
open_rate=open_rate,
is_short=is_short,
amount=amount,
stake_amount=stake_amount,
wallet_balance=stake_amount, # In isolated mode, stake-amount = wallet size
mm_ex_1=0.0,
upnl_ex_1=0.0,
)
return isolated_liq
else:
raise OperationalException(
"Freqtrade currently only supports futures for leverage trading.")
def funding_fee_cutoff(self, open_date: datetime):
"""
:param open_date: The open date for a trade
@ -2530,8 +2510,13 @@ class Exchange:
cache=False,
drop_incomplete=False,
)
try:
# we can't assume we always get histories - for example during exchange downtimes
funding_rates = candle_histories[funding_comb]
mark_rates = candle_histories[mark_comb]
except KeyError:
raise ExchangeError("Could not find funding rates.") from None
funding_mark_rates = self.combine_funding_and_mark(
funding_rates=funding_rates, mark_rates=mark_rates)
@ -2611,6 +2596,8 @@ class Exchange:
:param is_short: trade direction
:param amount: Trade amount
:param open_date: Open date of the trade
:return: funding fee since open_date
:raies: ExchangeError if something goes wrong.
"""
if self.trading_mode == TradingMode.FUTURES:
if self._config['dry_run']:
@ -2622,7 +2609,7 @@ class Exchange:
else:
return 0.0
def get_or_calculate_liquidation_price(
def get_liquidation_price(
self,
pair: str,
# Dry-run
@ -2630,7 +2617,7 @@ class Exchange:
is_short: bool,
amount: float, # Absolute value of position size
stake_amount: float,
wallet_balance: float, # Or margin balance
wallet_balance: float,
mm_ex_1: float = 0.0, # (Binance) Cross only
upnl_ex_1: float = 0.0, # (Binance) Cross only
) -> Optional[float]:
@ -2641,8 +2628,9 @@ class Exchange:
return None
elif (self.trading_mode != TradingMode.FUTURES):
raise OperationalException(
f"{self.name} does not support {self.margin_mode.value} {self.trading_mode.value}")
f"{self.name} does not support {self.margin_mode} {self.trading_mode}")
isolated_liq = None
if self._config['dry_run'] or not self.exchange_has("fetchPositions"):
isolated_liq = self.dry_run_liquidation_price(
@ -2660,8 +2648,6 @@ class Exchange:
if len(positions) > 0:
pos = positions[0]
isolated_liq = pos['liquidationPrice']
else:
return None
if isolated_liq:
buffer_amount = abs(open_rate - isolated_liq) * self.liquidation_buffer
@ -2905,7 +2891,7 @@ def amount_to_contracts(amount: float, contract_size: Optional[float]) -> float:
:return: num-contracts
"""
if contract_size and contract_size != 1:
return amount / contract_size
return float(FtPrecise(amount) / FtPrecise(contract_size))
else:
return amount
@ -2919,7 +2905,7 @@ def contracts_to_amount(num_contracts: float, contract_size: Optional[float]) ->
"""
if contract_size and contract_size != 1:
return num_contracts * contract_size
return float(FtPrecise(num_contracts) * FtPrecise(contract_size))
else:
return num_contracts

1
freqtrade/exchange/ftx.py
View File

@ -19,6 +19,7 @@ logger = logging.getLogger(__name__)
class Ftx(Exchange):
_ft_has: Dict = {
"order_time_in_force": ['GTC', 'IOC', 'PO'],
"stoploss_on_exchange": True,
"ohlcv_candle_limit": 1500,
"ohlcv_require_since": True,

7
freqtrade/exchange/gateio.py
View File

@ -25,8 +25,7 @@ class Gateio(Exchange):
_ft_has: Dict = {
"ohlcv_candle_limit": 1000,
"time_in_force_parameter": "timeInForce",
"order_time_in_force": ['gtc', 'ioc'],
"order_time_in_force": ['GTC', 'IOC'],
"stoploss_order_types": {"limit": "limit"},
"stoploss_on_exchange": True,
}
@ -57,7 +56,7 @@ class Gateio(Exchange):
ordertype: str,
leverage: float,
reduceOnly: bool,
time_in_force: str = 'gtc',
time_in_force: str = 'GTC',
) -> Dict:
params = super()._get_params(
side=side,
@ -69,7 +68,7 @@ class Gateio(Exchange):
if ordertype == 'market' and self.trading_mode == TradingMode.FUTURES:
params['type'] = 'market'
param = self._ft_has.get('time_in_force_parameter', '')
params.update({param: 'ioc'})
params.update({param: 'IOC'})
return params
def get_trades_for_order(self, order_id: str, pair: str, since: datetime,

2
freqtrade/exchange/kraken.py
View File

@ -171,7 +171,7 @@ class Kraken(Exchange):
ordertype: str,
leverage: float,
reduceOnly: bool,
time_in_force: str = 'gtc'
time_in_force: str = 'GTC'
) -> Dict:
params = super()._get_params(
side=side,

3
freqtrade/exchange/kucoin.py
View File

@ -23,8 +23,7 @@ class Kucoin(Exchange):
"stoploss_order_types": {"limit": "limit", "market": "market"},
"l2_limit_range": [20, 100],
"l2_limit_range_required": False,
"order_time_in_force": ['gtc', 'fok', 'ioc'],
"time_in_force_parameter": "timeInForce",
"order_time_in_force": ['GTC', 'FOK', 'IOC'],
"ohlcv_candle_limit": 1500,
}

6
freqtrade/exchange/okx.py
View File

@ -4,8 +4,7 @@ from typing import Dict, List, Optional, Tuple
import ccxt
from freqtrade.constants import BuySell
from freqtrade.enums import MarginMode, TradingMode
from freqtrade.enums.candletype import CandleType
from freqtrade.enums import CandleType, MarginMode, TradingMode
from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
from freqtrade.exchange import Exchange, date_minus_candles
from freqtrade.exchange.common import retrier
@ -72,6 +71,7 @@ class Okx(Exchange):
try:
if self.trading_mode == TradingMode.FUTURES and not self._config['dry_run']:
accounts = self._api.fetch_accounts()
self._log_exchange_response('fetch_accounts', accounts)
if len(accounts) > 0:
self.net_only = accounts[0].get('info', {}).get('posMode') == 'net_mode'
except ccxt.DDoSProtection as e:
@ -98,7 +98,7 @@ class Okx(Exchange):
ordertype: str,
leverage: float,
reduceOnly: bool,
time_in_force: str = 'gtc',
time_in_force: str = 'GTC',
) -> Dict:
params = super()._get_params(
side=side,

44
freqtrade/freqai/prediction_models/BaseClassifierModel.py → freqtrade/freqai/base_models/BaseClassifierModel.py
View File

@ -1,4 +1,5 @@
import logging
from time import time
from typing import Any, Tuple
import numpy as np
@ -21,34 +22,36 @@ class BaseClassifierModel(IFreqaiModel):
"""
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any:
"""
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
for storing, saving, loading, and analyzing the data.
:param unfiltered_dataframe: Full dataframe for the current training period
:param unfiltered_df: Full dataframe for the current training period
:param metadata: pair metadata from strategy.
:return:
:model: Trained model which can be used to inference (self.predict)
"""
logger.info("-------------------- Starting training " f"{pair} --------------------")
logger.info(f"-------------------- Starting training {pair} --------------------")
start_time = time()
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe,
unfiltered_df,
dk.training_features_list,
dk.label_list,
training_filter=True,
)
start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
logger.info(f"-------------------- Training on data from {start_date} to "
f"{end_date} --------------------")
# split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get('fit_live_predictions', 0) or not self.live:
if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
dk.fit_labels()
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)
@ -57,36 +60,39 @@ class BaseClassifierModel(IFreqaiModel):
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
logger.info(f"Training model on {len(data_dictionary['train_features'])} data points")
model = self.fit(data_dictionary)
model = self.fit(data_dictionary, dk)
logger.info(f"--------------------done training {pair}--------------------")
end_time = time()
logger.info(f"-------------------- Done training {pair} "
f"({end_time - start_time:.2f} secs) --------------------")
return model
def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
:param: unfiltered_df: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (PCA and DI index)
"""
dk.find_features(unfiltered_dataframe)
filtered_dataframe, _ = dk.filter_features(
unfiltered_dataframe, dk.training_features_list, training_filter=False
dk.find_features(unfiltered_df)
filtered_df, _ = dk.filter_features(
unfiltered_df, dk.training_features_list, training_filter=False
)
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe
filtered_df = dk.normalize_data_from_metadata(filtered_df)
dk.data_dictionary["prediction_features"] = filtered_df
self.data_cleaning_predict(dk, filtered_dataframe)
self.data_cleaning_predict(dk, filtered_df)
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list)

44
freqtrade/freqai/prediction_models/BaseRegressionModel.py → freqtrade/freqai/base_models/BaseRegressionModel.py
View File

@ -1,4 +1,5 @@
import logging
from time import time
from typing import Any, Tuple
import numpy as np
@ -20,34 +21,36 @@ class BaseRegressionModel(IFreqaiModel):
"""
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any:
"""
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
for storing, saving, loading, and analyzing the data.
:param unfiltered_dataframe: Full dataframe for the current training period
:param unfiltered_df: Full dataframe for the current training period
:param metadata: pair metadata from strategy.
:return:
:model: Trained model which can be used to inference (self.predict)
"""
logger.info("-------------------- Starting training " f"{pair} --------------------")
logger.info(f"-------------------- Starting training {pair} --------------------")
start_time = time()
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe,
unfiltered_df,
dk.training_features_list,
dk.label_list,
training_filter=True,
)
start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
logger.info(f"-------------------- Training on data from {start_date} to "
f"{end_date} --------------------")
# split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get('fit_live_predictions', 0) or not self.live:
if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
dk.fit_labels()
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)
@ -56,37 +59,40 @@ class BaseRegressionModel(IFreqaiModel):
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
logger.info(f"Training model on {len(data_dictionary['train_features'])} data points")
model = self.fit(data_dictionary)
model = self.fit(data_dictionary, dk)
logger.info(f"--------------------done training {pair}--------------------")
end_time = time()
logger.info(f"-------------------- Done training {pair} "
f"({end_time - start_time:.2f} secs) --------------------")
return model
def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
:param: unfiltered_df: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (PCA and DI index)
"""
dk.find_features(unfiltered_dataframe)
filtered_dataframe, _ = dk.filter_features(
unfiltered_dataframe, dk.training_features_list, training_filter=False
dk.find_features(unfiltered_df)
filtered_df, _ = dk.filter_features(
unfiltered_df, dk.training_features_list, training_filter=False
)
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe
filtered_df = dk.normalize_data_from_metadata(filtered_df)
dk.data_dictionary["prediction_features"] = filtered_df
# optional additional data cleaning/analysis
self.data_cleaning_predict(dk, filtered_dataframe)
self.data_cleaning_predict(dk, filtered_df)
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list)

28
freqtrade/freqai/prediction_models/BaseTensorFlowModel.py → freqtrade/freqai/base_models/BaseTensorFlowModel.py
View File

@ -1,4 +1,5 @@
import logging
from time import time
from typing import Any
from pandas import DataFrame
@ -17,34 +18,36 @@ class BaseTensorFlowModel(IFreqaiModel):
"""
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any:
"""
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
for storing, saving, loading, and analyzing the data.
:param unfiltered_dataframe: Full dataframe for the current training period
:param unfiltered_df: Full dataframe for the current training period
:param metadata: pair metadata from strategy.
:return:
:model: Trained model which can be used to inference (self.predict)
"""
logger.info("-------------------- Starting training " f"{pair} --------------------")
logger.info(f"-------------------- Starting training {pair} --------------------")
start_time = time()
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe,
unfiltered_df,
dk.training_features_list,
dk.label_list,
training_filter=True,
)
start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
logger.info(f"-------------------- Training on data from {start_date} to "
f"{end_date} --------------------")
# split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get('fit_live_predictions', 0) or not self.live:
if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
dk.fit_labels()
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)
@ -53,12 +56,15 @@ class BaseTensorFlowModel(IFreqaiModel):
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
logger.info(f"Training model on {len(data_dictionary['train_features'])} data points")
model = self.fit(data_dictionary)
model = self.fit(data_dictionary, dk)
logger.info(f"--------------------done training {pair}--------------------")
end_time = time()
logger.info(f"-------------------- Done training {pair} "
f"({end_time - start_time:.2f} secs) --------------------")
return model

64
freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py Normal file
View File

@ -0,0 +1,64 @@
from joblib import Parallel
from sklearn.multioutput import MultiOutputRegressor, _fit_estimator
from sklearn.utils.fixes import delayed
from sklearn.utils.validation import has_fit_parameter
class FreqaiMultiOutputRegressor(MultiOutputRegressor):
def fit(self, X, y, sample_weight=None, fit_params=None):
"""Fit the model to data, separately for each output variable.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets. An indicator matrix turns on multilabel
estimation.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If `None`, then samples are equally weighted.
Only supported if the underlying regressor supports sample
weights.
fit_params : A list of dicts for the fit_params
Parameters passed to the ``estimator.fit`` method of each step.
Each dict may contain same or different values (e.g. different
eval_sets or init_models)
.. versionadded:: 0.23
Returns
-------
self : object
Returns a fitted instance.
"""
if not hasattr(self.estimator, "fit"):
raise ValueError("The base estimator should implement a fit method")
y = self._validate_data(X="no_validation", y=y, multi_output=True)
if y.ndim == 1:
raise ValueError(
"y must have at least two dimensions for "
"multi-output regression but has only one."
)
if sample_weight is not None and not has_fit_parameter(
self.estimator, "sample_weight"
):
raise ValueError("Underlying estimator does not support sample weights.")
if not fit_params:
fit_params = [None] * y.shape[1]
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_estimator)(
self.estimator, X, y[:, i], sample_weight, **fit_params[i]
)
for i in range(y.shape[1])
)
if hasattr(self.estimators_[0], "n_features_in_"):
self.n_features_in_ = self.estimators_[0].n_features_in_
if hasattr(self.estimators_[0], "feature_names_in_"):
self.feature_names_in_ = self.estimators_[0].feature_names_in_
return

65
freqtrade/freqai/data_drawer.py
View File

@ -16,6 +16,7 @@ from numpy.typing import NDArray
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import Config
from freqtrade.data.history import load_pair_history
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
@ -27,9 +28,7 @@ logger = logging.getLogger(__name__)
class pair_info(TypedDict):
model_filename: str
first: bool
trained_timestamp: int
priority: int
data_path: str
extras: dict
@ -58,7 +57,7 @@ class FreqaiDataDrawer:
Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
"""
def __init__(self, full_path: Path, config: dict, follow_mode: bool = False):
def __init__(self, full_path: Path, config: Config, follow_mode: bool = False):
self.config = config
self.freqai_info = config.get("freqai", {})
@ -76,6 +75,8 @@ class FreqaiDataDrawer:
self.full_path / f"follower_dictionary-{self.follower_name}.json"
)
self.historic_predictions_path = Path(self.full_path / "historic_predictions.pkl")
self.historic_predictions_bkp_path = Path(
self.full_path / "historic_predictions.backup.pkl")
self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
self.follow_mode = follow_mode
if follow_mode:
@ -89,7 +90,7 @@ class FreqaiDataDrawer:
self.old_DBSCAN_eps: Dict[str, float] = {}
self.empty_pair_dict: pair_info = {
"model_filename": "", "trained_timestamp": 0,
"priority": 1, "first": True, "data_path": "", "extras": {}}
"data_path": "", "extras": {}}
def load_drawer_from_disk(self):
"""
@ -118,6 +119,7 @@ class FreqaiDataDrawer:
"""
exists = self.historic_predictions_path.is_file()
if exists:
try:
with open(self.historic_predictions_path, "rb") as fp:
self.historic_predictions = cloudpickle.load(fp)
logger.info(
@ -125,6 +127,13 @@ class FreqaiDataDrawer:
"that statistics may be inaccurate if the bot has been offline for "
"an extended period of time."
)
except EOFError:
logger.warning(
'Historical prediction file was corrupted. Trying to load backup file.')
with open(self.historic_predictions_bkp_path, "rb") as fp:
self.historic_predictions = cloudpickle.load(fp)
logger.warning('FreqAI successfully loaded the backup historical predictions file.')
elif not self.follow_mode:
logger.info("Could not find existing historic_predictions, starting from scratch")
else:
@ -142,6 +151,9 @@ class FreqaiDataDrawer:
with open(self.historic_predictions_path, "wb") as fp:
cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
# create a backup
shutil.copy(self.historic_predictions_path, self.historic_predictions_bkp_path)
def save_drawer_to_disk(self):
"""
Save data drawer full of all pair model metadata in present model folder.
@ -203,7 +215,6 @@ class FreqaiDataDrawer:
self.pair_dict[pair] = self.empty_pair_dict.copy()
model_filename = ""
trained_timestamp = 0
self.pair_dict[pair]["priority"] = len(self.pair_dict)
if not data_path_set and self.follow_mode:
logger.warning(
@ -223,18 +234,9 @@ class FreqaiDataDrawer:
return
else:
self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy()
self.pair_dict[metadata["pair"]]["priority"] = len(self.pair_dict)
return
def pair_to_end_of_training_queue(self, pair: str) -> None:
# march all pairs up in the queue
with self.pair_dict_lock:
for p in self.pair_dict:
self.pair_dict[p]["priority"] -= 1
# send pair to end of queue
self.pair_dict[pair]["priority"] = len(self.pair_dict)
def set_initial_return_values(self, pair: str, pred_df: DataFrame) -> None:
"""
Set the initial return values to the historical predictions dataframe. This avoids needing
@ -311,6 +313,7 @@ class FreqaiDataDrawer:
"""
dk.find_features(dataframe)
dk.find_labels(dataframe)
full_labels = dk.label_list + dk.unique_class_list
@ -342,7 +345,7 @@ class FreqaiDataDrawer:
for dir in model_folders:
result = pattern.match(str(dir.name))
if result is None:
break
continue
coin = result.group(1)
timestamp = result.group(2)
@ -374,7 +377,27 @@ class FreqaiDataDrawer:
if self.config.get("freqai", {}).get("purge_old_models", False):
self.purge_old_models()
# Functions pulled back from FreqaiDataKitchen because they relied on DataDrawer
def save_metadata(self, dk: FreqaiDataKitchen) -> None:
"""
Saves only metadata for backtesting studies if user prefers
not to save model data. This saves tremendous amounts of space
for users generating huge studies.
This is only active when `save_backtest_models`: false (not default)
"""
if not dk.data_path.is_dir():
dk.data_path.mkdir(parents=True, exist_ok=True)
save_path = Path(dk.data_path)
dk.data["data_path"] = str(dk.data_path)
dk.data["model_filename"] = str(dk.model_filename)
dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
dk.data["label_list"] = dk.label_list
with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
return
def save_data(self, model: Any, coin: str, dk: FreqaiDataKitchen) -> None:
"""
@ -428,6 +451,16 @@ class FreqaiDataDrawer:
return
def load_metadata(self, dk: FreqaiDataKitchen) -> None:
"""
Load only metadata into datakitchen to increase performance during
presaved backtesting (prediction file loading).
"""
with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
dk.data = json.load(fp)
dk.training_features_list = dk.data["training_features_list"]
dk.label_list = dk.data["label_list"]
def load_data(self, coin: str, dk: FreqaiDataKitchen) -> Any:
"""
loads all data required to make a prediction on a sub-train time range

368
freqtrade/freqai/data_kitchen.py
View File

@ -1,7 +1,8 @@
import copy
import datetime
import logging
import shutil
from datetime import datetime, timezone
from math import cos, sin
from pathlib import Path
from typing import Any, Dict, List, Tuple
@ -9,6 +10,7 @@ import numpy as np
import numpy.typing as npt
import pandas as pd
from pandas import DataFrame
from scipy import stats
from sklearn import linear_model
from sklearn.cluster import DBSCAN
from sklearn.metrics.pairwise import pairwise_distances
@ -16,8 +18,7 @@ from sklearn.model_selection import train_test_split
from sklearn.neighbors import NearestNeighbors
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.strategy.interface import IStrategy
@ -57,7 +58,7 @@ class FreqaiDataKitchen:
def __init__(
self,
config: Dict[str, Any],
config: Config,
live: bool = False,
pair: str = "",
):
@ -71,6 +72,8 @@ class FreqaiDataKitchen:
self.label_list: List = []
self.training_features_list: List = []
self.model_filename: str = ""
self.backtesting_results_path = Path()
self.backtest_predictions_folder: str = "backtesting_predictions"
self.live = live
self.pair = pair
@ -168,13 +171,21 @@ class FreqaiDataKitchen:
train_labels = labels
train_weights = weights
# Simplest way to reverse the order of training and test data:
if self.freqai_config['feature_parameters'].get('reverse_train_test_order', False):
return self.build_data_dictionary(
train_features, test_features, train_labels, test_labels, train_weights, test_weights
test_features, train_features, test_labels,
train_labels, test_weights, train_weights
)
else:
return self.build_data_dictionary(
train_features, test_features, train_labels,
test_labels, train_weights, test_weights
)
def filter_features(
self,
unfiltered_dataframe: DataFrame,
unfiltered_df: DataFrame,
training_feature_list: List,
label_list: List = list(),
training_filter: bool = True,
@ -185,31 +196,35 @@ class FreqaiDataKitchen:
0s in the prediction dataset. However, prediction dataset do_predict will reflect any
row that had a NaN and will shield user from that prediction.
:params:
:unfiltered_dataframe: the full dataframe for the present training period
:unfiltered_df: the full dataframe for the present training period
:training_feature_list: list, the training feature list constructed by
self.build_feature_list() according to user specified parameters in the configuration file.
:labels: the labels for the dataset
:training_filter: boolean which lets the function know if it is training data or
prediction data to be filtered.
:returns:
:filtered_dataframe: dataframe cleaned of NaNs and only containing the user
:filtered_df: dataframe cleaned of NaNs and only containing the user
requested feature set.
:labels: labels cleaned of NaNs.
"""
filtered_dataframe = unfiltered_dataframe.filter(training_feature_list, axis=1)
filtered_dataframe = filtered_dataframe.replace([np.inf, -np.inf], np.nan)
filtered_df = unfiltered_df.filter(training_feature_list, axis=1)
filtered_df = filtered_df.replace([np.inf, -np.inf], np.nan)
drop_index = pd.isnull(filtered_dataframe).any(1) # get the rows that have NaNs,
drop_index = pd.isnull(filtered_df).any(1) # get the rows that have NaNs,
drop_index = drop_index.replace(True, 1).replace(False, 0) # pep8 requirement.
if (training_filter):
const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
if const_cols:
filtered_df = filtered_df.filter(filtered_df.columns.difference(const_cols))
logger.warning(f"Removed features {const_cols} with constant values.")
# we don't care about total row number (total no. datapoints) in training, we only care
# about removing any row with NaNs
# if labels has multiple columns (user wants to train multiple modelEs), we detect here
labels = unfiltered_dataframe.filter(label_list, axis=1)
labels = unfiltered_df.filter(label_list, axis=1)
drop_index_labels = pd.isnull(labels).any(1)
drop_index_labels = drop_index_labels.replace(True, 1).replace(False, 0)
dates = unfiltered_dataframe['date']
filtered_dataframe = filtered_dataframe[
dates = unfiltered_df['date']
filtered_df = filtered_df[
(drop_index == 0) & (drop_index_labels == 0)
] # dropping values
labels = labels[
@ -219,13 +234,13 @@ class FreqaiDataKitchen:
(drop_index == 0) & (drop_index_labels == 0)
]
logger.info(
f"dropped {len(unfiltered_dataframe) - len(filtered_dataframe)} training points"
f" due to NaNs in populated dataset {len(unfiltered_dataframe)}."
f"dropped {len(unfiltered_df) - len(filtered_df)} training points"
f" due to NaNs in populated dataset {len(unfiltered_df)}."
)
if (1 - len(filtered_dataframe) / len(unfiltered_dataframe)) > 0.1 and self.live:
worst_indicator = str(unfiltered_dataframe.count().idxmin())
if (1 - len(filtered_df) / len(unfiltered_df)) > 0.1 and self.live:
worst_indicator = str(unfiltered_df.count().idxmin())
logger.warning(
f" {(1 - len(filtered_dataframe)/len(unfiltered_dataframe)) * 100:.0f} percent "
f" {(1 - len(filtered_df)/len(unfiltered_df)) * 100:.0f} percent "
" of training data dropped due to NaNs, model may perform inconsistent "
f"with expectations. Verify {worst_indicator}"
)
@ -234,9 +249,9 @@ class FreqaiDataKitchen:
else:
# we are backtesting so we need to preserve row number to send back to strategy,
# so now we use do_predict to avoid any prediction based on a NaN
drop_index = pd.isnull(filtered_dataframe).any(1)
drop_index = pd.isnull(filtered_df).any(1)
self.data["filter_drop_index_prediction"] = drop_index
filtered_dataframe.fillna(0, inplace=True)
filtered_df.fillna(0, inplace=True)
# replacing all NaNs with zeros to avoid issues in 'prediction', but any prediction
# that was based on a single NaN is ultimately protected from buys with do_predict
drop_index = ~drop_index
@ -245,11 +260,11 @@ class FreqaiDataKitchen:
logger.info(
"dropped %s of %s prediction data points due to NaNs.",
len(self.do_predict) - self.do_predict.sum(),
len(filtered_dataframe),
len(filtered_df),
)
labels = []
return filtered_dataframe, labels
return filtered_df, labels
def build_data_dictionary(
self,
@ -281,6 +296,7 @@ class FreqaiDataKitchen:
:returns:
:data_dictionary: updated dictionary with standardized values.
"""
# standardize the data by training stats
train_max = data_dictionary["train_features"].max()
train_min = data_dictionary["train_features"].min()
@ -314,10 +330,24 @@ class FreqaiDataKitchen:
- 1
)
self.data[f"{item}_max"] = train_labels_max # .to_dict()
self.data[f"{item}_min"] = train_labels_min # .to_dict()
self.data[f"{item}_max"] = train_labels_max
self.data[f"{item}_min"] = train_labels_min
return data_dictionary
def normalize_single_dataframe(self, df: DataFrame) -> DataFrame:
train_max = df.max()
train_min = df.min()
df = (
2 * (df - train_min) / (train_max - train_min) - 1
)
for item in train_max.keys():
self.data[item + "_max"] = train_max[item]
self.data[item + "_min"] = train_min[item]
return df
def normalize_data_from_metadata(self, df: DataFrame) -> DataFrame:
"""
Normalize a set of data using the mean and standard deviation from
@ -337,7 +367,7 @@ class FreqaiDataKitchen:
def denormalize_labels_from_metadata(self, df: DataFrame) -> DataFrame:
"""
Normalize a set of data using the mean and standard deviation from
Denormalize a set of data using the mean and standard deviation from
the associated training data.
:param df: Dataframe of predictions to be denormalized
"""
@ -376,7 +406,7 @@ class FreqaiDataKitchen:
config_timerange = TimeRange.parse_timerange(self.config["timerange"])
if config_timerange.stopts == 0:
config_timerange.stopts = int(
datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
datetime.now(tz=timezone.utc).timestamp()
)
timerange_train = copy.deepcopy(full_timerange)
timerange_backtest = copy.deepcopy(full_timerange)
@ -393,8 +423,8 @@ class FreqaiDataKitchen:
timerange_train.stopts = timerange_train.startts + train_period_days
first = False
start = datetime.datetime.utcfromtimestamp(timerange_train.startts)
stop = datetime.datetime.utcfromtimestamp(timerange_train.stopts)
start = datetime.fromtimestamp(timerange_train.startts, tz=timezone.utc)
stop = datetime.fromtimestamp(timerange_train.stopts, tz=timezone.utc)
tr_training_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d"))
tr_training_list_timerange.append(copy.deepcopy(timerange_train))
@ -407,8 +437,8 @@ class FreqaiDataKitchen:
if timerange_backtest.stopts > config_timerange.stopts:
timerange_backtest.stopts = config_timerange.stopts
start = datetime.datetime.utcfromtimestamp(timerange_backtest.startts)
stop = datetime.datetime.utcfromtimestamp(timerange_backtest.stopts)
start = datetime.fromtimestamp(timerange_backtest.startts, tz=timezone.utc)
stop = datetime.fromtimestamp(timerange_backtest.stopts, tz=timezone.utc)
tr_backtesting_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d"))
tr_backtesting_list_timerange.append(copy.deepcopy(timerange_backtest))
@ -428,10 +458,11 @@ class FreqaiDataKitchen:
it is sliced down to just the present training period.
"""
start = datetime.datetime.fromtimestamp(timerange.startts, tz=datetime.timezone.utc)
stop = datetime.datetime.fromtimestamp(timerange.stopts, tz=datetime.timezone.utc)
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
df = df.loc[df["date"] >= start, :]
df = df.loc[df["date"] <= stop, :]
if not self.live:
df = df.loc[df["date"] < stop, :]
return df
@ -444,22 +475,23 @@ class FreqaiDataKitchen:
from sklearn.decomposition import PCA # avoid importing if we dont need it
n_components = self.data_dictionary["train_features"].shape[1]
pca = PCA(n_components=n_components)
pca = PCA(0.999)
pca = pca.fit(self.data_dictionary["train_features"])
n_keep_components = np.argmin(pca.explained_variance_ratio_.cumsum() < 0.999)
pca2 = PCA(n_components=n_keep_components)
n_keep_components = pca.n_components_
self.data["n_kept_components"] = n_keep_components
pca2 = pca2.fit(self.data_dictionary["train_features"])
n_components = self.data_dictionary["train_features"].shape[1]
logger.info("reduced feature dimension by %s", n_components - n_keep_components)
logger.info("explained variance %f", np.sum(pca2.explained_variance_ratio_))
train_components = pca2.transform(self.data_dictionary["train_features"])
logger.info("explained variance %f", np.sum(pca.explained_variance_ratio_))
train_components = pca.transform(self.data_dictionary["train_features"])
self.data_dictionary["train_features"] = pd.DataFrame(
data=train_components,
columns=["PC" + str(i) for i in range(0, n_keep_components)],
index=self.data_dictionary["train_features"].index,
)
# normalsing transformed training features
self.data_dictionary["train_features"] = self.normalize_single_dataframe(
self.data_dictionary["train_features"])
# keeping a copy of the non-transformed features so we can check for errors during
# model load from disk
@ -467,15 +499,18 @@ class FreqaiDataKitchen:
self.training_features_list = self.data_dictionary["train_features"].columns
if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
test_components = pca2.transform(self.data_dictionary["test_features"])
test_components = pca.transform(self.data_dictionary["test_features"])
self.data_dictionary["test_features"] = pd.DataFrame(
data=test_components,
columns=["PC" + str(i) for i in range(0, n_keep_components)],
index=self.data_dictionary["test_features"].index,
)
# normalise transformed test feature to transformed training features
self.data_dictionary["test_features"] = self.normalize_data_from_metadata(
self.data_dictionary["test_features"])
self.data["n_kept_components"] = n_keep_components
self.pca = pca2
self.pca = pca
logger.info(f"PCA reduced total features from {n_components} to {n_keep_components}")
@ -496,6 +531,9 @@ class FreqaiDataKitchen:
columns=["PC" + str(i) for i in range(0, self.data["n_kept_components"])],
index=filtered_dataframe.index,
)
# normalise transformed predictions to transformed training features
self.data_dictionary["prediction_features"] = self.normalize_data_from_metadata(
self.data_dictionary["prediction_features"])
def compute_distances(self) -> float:
"""
@ -521,7 +559,6 @@ class FreqaiDataKitchen:
"outlier_protection_percentage", 30)
outlier_pct = (dropped_pts.sum() / len(dropped_pts)) * 100
if outlier_pct >= outlier_protection_pct:
self.svm_model = None
return outlier_pct
else:
return 0.0
@ -571,6 +608,7 @@ class FreqaiDataKitchen:
f"SVM detected {outlier_pct:.2f}% of the points as outliers. "
f"Keeping original dataset."
)
self.svm_model = None
return
self.data_dictionary["train_features"] = self.data_dictionary["train_features"][
@ -622,9 +660,9 @@ class FreqaiDataKitchen:
is an outlier.
"""
from math import cos, sin
if predict:
if not self.data['DBSCAN_eps']:
return
train_ft_df = self.data_dictionary['train_features']
pred_ft_df = self.data_dictionary['prediction_features']
num_preds = len(pred_ft_df)
@ -694,6 +732,7 @@ class FreqaiDataKitchen:
f"DBSCAN detected {outlier_pct:.2f}% of the points as outliers. "
f"Keeping original dataset."
)
self.data['DBSCAN_eps'] = 0
return
self.data_dictionary['train_features'] = self.data_dictionary['train_features'][
@ -713,6 +752,126 @@ class FreqaiDataKitchen:
return
def compute_inlier_metric(self, set_='train') -> None:
"""
Compute inlier metric from backwards distance distributions.
This metric defines how well features from a timepoint fit
into previous timepoints.
"""
def normalise(dataframe: DataFrame, key: str) -> DataFrame:
if set_ == 'train':
min_value = dataframe.min()
max_value = dataframe.max()
self.data[f'{key}_min'] = min_value
self.data[f'{key}_max'] = max_value
else:
min_value = self.data[f'{key}_min']
max_value = self.data[f'{key}_max']
return (dataframe - min_value) / (max_value - min_value)
no_prev_pts = self.freqai_config["feature_parameters"]["inlier_metric_window"]
if set_ == 'train':
compute_df = copy.deepcopy(self.data_dictionary['train_features'])
elif set_ == 'test':
compute_df = copy.deepcopy(self.data_dictionary['test_features'])
else:
compute_df = copy.deepcopy(self.data_dictionary['prediction_features'])
compute_df_reindexed = compute_df.reindex(
index=np.flip(compute_df.index)
)
pairwise = pd.DataFrame(
np.triu(
pairwise_distances(compute_df_reindexed, n_jobs=self.thread_count)
),
columns=compute_df_reindexed.index,
index=compute_df_reindexed.index
)
pairwise = pairwise.round(5)
column_labels = [
'{}{}'.format('d', i) for i in range(1, no_prev_pts + 1)
]
distances = pd.DataFrame(
columns=column_labels, index=compute_df.index
)
for index in compute_df.index[no_prev_pts:]:
current_row = pairwise.loc[[index]]
current_row_no_zeros = current_row.loc[
:, (current_row != 0).any(axis=0)
]
distances.loc[[index]] = current_row_no_zeros.iloc[
:, :no_prev_pts
]
distances = distances.replace([np.inf, -np.inf], np.nan)
drop_index = pd.isnull(distances).any(1)
distances = distances[drop_index == 0]
inliers = pd.DataFrame(index=distances.index)
for key in distances.keys():
current_distances = distances[key].dropna()
current_distances = normalise(current_distances, key)
if set_ == 'train':
fit_params = stats.weibull_min.fit(current_distances)
self.data[f'{key}_fit_params'] = fit_params
else:
fit_params = self.data[f'{key}_fit_params']
quantiles = stats.weibull_min.cdf(current_distances, *fit_params)
df_inlier = pd.DataFrame(
{key: quantiles}, index=distances.index
)
inliers = pd.concat(
[inliers, df_inlier], axis=1
)
inlier_metric = pd.DataFrame(
data=inliers.sum(axis=1) / no_prev_pts,
columns=['%-inlier_metric'],
index=compute_df.index
)
inlier_metric = (2 * (inlier_metric - inlier_metric.min()) /
(inlier_metric.max() - inlier_metric.min()) - 1)
if set_ in ('train', 'test'):
inlier_metric = inlier_metric.iloc[no_prev_pts:]
compute_df = compute_df.iloc[no_prev_pts:]
self.remove_beginning_points_from_data_dict(set_, no_prev_pts)
self.data_dictionary[f'{set_}_features'] = pd.concat(
[compute_df, inlier_metric], axis=1)
else:
self.data_dictionary['prediction_features'] = pd.concat(
[compute_df, inlier_metric], axis=1)
self.data_dictionary['prediction_features'].fillna(0, inplace=True)
logger.info('Inlier metric computed and added to features.')
return None
def remove_beginning_points_from_data_dict(self, set_='train', no_prev_pts: int = 10):
features = self.data_dictionary[f'{set_}_features']
weights = self.data_dictionary[f'{set_}_weights']
labels = self.data_dictionary[f'{set_}_labels']
self.data_dictionary[f'{set_}_weights'] = weights[no_prev_pts:]
self.data_dictionary[f'{set_}_features'] = features.iloc[no_prev_pts:]
self.data_dictionary[f'{set_}_labels'] = labels.iloc[no_prev_pts:]
def add_noise_to_training_features(self) -> None:
"""
Add noise to train features to reduce the risk of overfitting.
"""
mu = 0 # no shift
sigma = self.freqai_config["feature_parameters"]["noise_standard_deviation"]
compute_df = self.data_dictionary['train_features']
noise = np.random.normal(mu, sigma, [compute_df.shape[0], compute_df.shape[1]])
self.data_dictionary['train_features'] += noise
return
def find_features(self, dataframe: DataFrame) -> None:
"""
Find features in the strategy provided dataframe
@ -722,11 +881,14 @@ class FreqaiDataKitchen:
"""
column_names = dataframe.columns
features = [c for c in column_names if "%" in c]
labels = [c for c in column_names if "&" in c]
if not features:
raise OperationalException("Could not find any features!")
self.training_features_list = features
def find_labels(self, dataframe: DataFrame) -> None:
column_names = dataframe.columns
labels = [c for c in column_names if "&" in c]
self.label_list = labels
def check_if_pred_in_training_spaces(self) -> None:
@ -751,18 +913,10 @@ class FreqaiDataKitchen:
0,
)
outlier_pct = self.get_outlier_percentage(1 - do_predict)
if outlier_pct:
logger.warning(
f"DI detected {outlier_pct:.2f}% of the points as outliers. "
f"Keeping original dataset."
)
return
if (len(do_predict) - do_predict.sum()) > 0:
logger.info(
f"DI tossed {len(do_predict) - do_predict.sum()} predictions for "
"being too far from training data"
"being too far from training data."
)
self.do_predict += do_predict
@ -777,9 +931,10 @@ class FreqaiDataKitchen:
weights = np.exp(-np.arange(num_weights) / (wfactor * num_weights))[::-1]
return weights
def append_predictions(self, predictions: DataFrame, do_predict: npt.ArrayLike) -> None:
def get_predictions_to_append(self, predictions: DataFrame,
do_predict: npt.ArrayLike) -> DataFrame:
"""
Append backtest prediction from current backtest period to all previous periods
Get backtest prediction from current backtest period
"""
append_df = DataFrame()
@ -794,13 +949,18 @@ class FreqaiDataKitchen:
if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0:
append_df["DI_values"] = self.DI_values
return append_df
def append_predictions(self, append_df: DataFrame) -> None:
"""
Append backtest prediction from current backtest period to all previous periods
"""
if self.full_df.empty:
self.full_df = append_df
else:
self.full_df = pd.concat([self.full_df, append_df], axis=0)
return
def fill_predictions(self, dataframe):
"""
Back fill values to before the backtesting range so that the dataframe matches size
@ -816,7 +976,6 @@ class FreqaiDataKitchen:
to_keep = [col for col in dataframe.columns if not col.startswith("&")]
self.return_dataframe = pd.concat([dataframe[to_keep], self.full_df], axis=1)
self.full_df = DataFrame()
return
@ -840,14 +999,14 @@ class FreqaiDataKitchen:
"Please indicate the end date of your desired backtesting. "
"timerange.")
# backtest_timerange.stopts = int(
# datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
# datetime.now(tz=timezone.utc).timestamp()
# )
backtest_timerange.startts = (
backtest_timerange.startts - backtest_period_days * SECONDS_IN_DAY
)
start = datetime.datetime.utcfromtimestamp(backtest_timerange.startts)
stop = datetime.datetime.utcfromtimestamp(backtest_timerange.stopts)
start = datetime.fromtimestamp(backtest_timerange.startts, tz=timezone.utc)
stop = datetime.fromtimestamp(backtest_timerange.stopts, tz=timezone.utc)
full_timerange = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")
self.full_path = Path(
@ -873,7 +1032,7 @@ class FreqaiDataKitchen:
:return:
bool = If the model is expired or not.
"""
time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
time = datetime.now(tz=timezone.utc).timestamp()
elapsed_time = (time - trained_timestamp) / 3600 # hours
max_time = self.freqai_config.get("expiration_hours", 0)
if max_time > 0:
@ -885,7 +1044,7 @@ class FreqaiDataKitchen:
self, trained_timestamp: int
) -> Tuple[bool, TimeRange, TimeRange]:
time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
time = datetime.now(tz=timezone.utc).timestamp()
trained_timerange = TimeRange()
data_load_timerange = TimeRange()
@ -900,9 +1059,7 @@ class FreqaiDataKitchen:
# We notice that users like to use exotic indicators where
# they do not know the required timeperiod. Here we include a factor
# of safety by multiplying the user considered "max" by 2.
max_period = self.freqai_config["feature_parameters"].get(
"indicator_max_period_candles", 20
) * 2
max_period = self.config.get('startup_candle_count', 20) * 2
additional_seconds = max_period * max_tf_seconds
if trained_timestamp != 0:
@ -948,31 +1105,6 @@ class FreqaiDataKitchen:
self.model_filename = f"cb_{coin.lower()}_{int(trained_timerange.stopts)}"
def download_all_data_for_training(self, timerange: TimeRange, dp: DataProvider) -> None:
"""
Called only once upon start of bot to download the necessary data for
populating indicators and training the model.
:param timerange: TimeRange = The full data timerange for populating the indicators
and training the model.
:param dp: DataProvider instance attached to the strategy
"""
new_pairs_days = int((timerange.stopts - timerange.startts) / SECONDS_IN_DAY)
if not dp._exchange:
# Not realistic - this is only called in live mode.
raise OperationalException("Dataprovider did not have an exchange attached.")
refresh_backtest_ohlcv_data(
dp._exchange,
pairs=self.all_pairs,
timeframes=self.freqai_config["feature_parameters"].get("include_timeframes"),
datadir=self.config["datadir"],
timerange=timerange,
new_pairs_days=new_pairs_days,
erase=False,
data_format=self.config.get("dataformat_ohlcv", "json"),
trading_mode=self.config.get("trading_mode", "spot"),
prepend=self.config.get("prepend_data", False),
)
def set_all_pairs(self) -> None:
self.all_pairs = copy.deepcopy(
@ -1077,7 +1209,8 @@ class FreqaiDataKitchen:
def get_unique_classes_from_labels(self, dataframe: DataFrame) -> None:
self.find_features(dataframe)
# self.find_features(dataframe)
self.find_labels(dataframe)
for key in self.label_list:
if dataframe[key].dtype == object:
@ -1086,3 +1219,48 @@ class FreqaiDataKitchen:
if self.unique_classes:
for label in self.unique_classes:
self.unique_class_list += list(self.unique_classes[label])
def save_backtesting_prediction(
self, append_df: DataFrame
) -> None:
"""
Save prediction dataframe from backtesting to h5 file format
:param append_df: dataframe for backtesting period
"""
full_predictions_folder = Path(self.full_path / self.backtest_predictions_folder)
if not full_predictions_folder.is_dir():
full_predictions_folder.mkdir(parents=True, exist_ok=True)
append_df.to_hdf(self.backtesting_results_path, key='append_df', mode='w')
def get_backtesting_prediction(
self
) -> DataFrame:
"""
Get prediction dataframe from h5 file format
"""
append_df = pd.read_hdf(self.backtesting_results_path)
return append_df
def check_if_backtest_prediction_exists(
self
) -> bool:
"""
Check if a backtesting prediction already exists
:param dk: FreqaiDataKitchen
:return:
:boolean: whether the prediction file exists or not.
"""
path_to_predictionfile = Path(self.full_path /
self.backtest_predictions_folder /
f"{self.model_filename}_prediction.h5")
self.backtesting_results_path = path_to_predictionfile
file_exists = path_to_predictionfile.is_file()
if file_exists:
logger.info(f"Found backtesting prediction file at {path_to_predictionfile}")
else:
logger.info(
f"Could not find backtesting prediction file at {path_to_predictionfile}"
)
return file_exists

297
freqtrade/freqai/freqai_interface.py
View File

@ -1,13 +1,13 @@
# import contextlib
import datetime
import logging
import shutil
import threading
import time
from abc import ABC, abstractmethod
from collections import deque
from datetime import datetime, timezone
from pathlib import Path
from threading import Lock
from typing import Any, Dict, Tuple
from typing import Any, Dict, List, Tuple
import numpy as np
import pandas as pd
@ -15,11 +15,13 @@ from numpy.typing import NDArray
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import DATETIME_PRINT_FORMAT, Config
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.freqai.data_drawer import FreqaiDataDrawer
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.utils import plot_feature_importance
from freqtrade.strategy.interface import IStrategy
@ -27,13 +29,6 @@ pd.options.mode.chained_assignment = None
logger = logging.getLogger(__name__)
def threaded(fn):
def wrapper(*args, **kwargs):
threading.Thread(target=fn, args=args, kwargs=kwargs).start()
return wrapper
class IFreqaiModel(ABC):
"""
Class containing all tools for training and prediction in the strategy.
@ -57,7 +52,7 @@ class IFreqaiModel(ABC):
Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
"""
def __init__(self, config: Dict[str, Any]) -> None:
def __init__(self, config: Config) -> None:
self.config = config
self.assert_config(self.config)
@ -66,22 +61,28 @@ class IFreqaiModel(ABC):
"data_split_parameters", {})
self.model_training_parameters: Dict[str, Any] = config.get("freqai", {}).get(
"model_training_parameters", {})
self.feature_parameters = config.get("freqai", {}).get("feature_parameters")
self.retrain = False
self.first = True
self.set_full_path()
self.follow_mode: bool = self.freqai_info.get("follow_mode", False)
self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", True)
if self.save_backtest_models:
logger.info('Backtesting module configured to save all models.')
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
self.scanning = False
self.ft_params = self.freqai_info["feature_parameters"]
self.keras: bool = self.freqai_info.get("keras", False)
if self.keras and self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
self.freqai_info["feature_parameters"]["DI_threshold"] = 0
if self.keras and self.ft_params.get("DI_threshold", 0):
self.ft_params["DI_threshold"] = 0
logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
if self.ft_params.get("inlier_metric_window", 0):
self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
self.pair_it = 0
self.pair_it_train = 0
self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
self.train_queue = self._set_train_queue()
self.last_trade_database_summary: DataFrame = {}
self.current_trade_database_summary: DataFrame = {}
self.analysis_lock = Lock()
@ -90,8 +91,19 @@ class IFreqaiModel(ABC):
self.begin_time: float = 0
self.begin_time_train: float = 0
self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
self.continual_learning = self.freqai_info.get('continual_learning', False)
self.plot_features = self.ft_params.get("plot_feature_importances", 0)
def assert_config(self, config: Dict[str, Any]) -> None:
self._threads: List[threading.Thread] = []
self._stop_event = threading.Event()
def __getstate__(self):
"""
Return an empty state to be pickled in hyperopt
"""
return ({})
def assert_config(self, config: Config) -> None:
if not config.get("freqai", {}):
raise OperationalException("No freqai parameters found in configuration file.")
@ -124,7 +136,6 @@ class IFreqaiModel(ABC):
elif not self.follow_mode:
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
with self.analysis_lock:
dataframe = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
)
@ -145,22 +156,45 @@ class IFreqaiModel(ABC):
self.model = None
self.dk = None
@threaded
def start_scanning(self, strategy: IStrategy) -> None:
def shutdown(self):
"""
Cleans up threads on Shutdown, set stop event. Join threads to wait
for current training iteration.
"""
logger.info("Stopping FreqAI")
self._stop_event.set()
logger.info("Waiting on Training iteration")
for _thread in self._threads:
_thread.join()
def start_scanning(self, *args, **kwargs) -> None:
"""
Start `self._start_scanning` in a separate thread
"""
_thread = threading.Thread(target=self._start_scanning, args=args, kwargs=kwargs)
self._threads.append(_thread)
_thread.start()
def _start_scanning(self, strategy: IStrategy) -> None:
"""
Function designed to constantly scan pairs for retraining on a separate thread (intracandle)
to improve model youth. This function is agnostic to data preparation/collection/storage,
it simply trains on what ever data is available in the self.dd.
:param strategy: IStrategy = The user defined strategy class
"""
while 1:
while not self._stop_event.is_set():
time.sleep(1)
for pair in self.config.get("exchange", {}).get("pair_whitelist"):
pair = self.train_queue[0]
# ensure pair is avaialble in dp
if pair not in strategy.dp.current_whitelist():
self.train_queue.popleft()
logger.warning(f'{pair} not in current whitelist, removing from train queue.')
continue
(_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
if self.dd.pair_dict[pair]["priority"] != 1:
continue
dk = FreqaiDataKitchen(self.config, self.live, pair)
dk.set_paths(pair, trained_timestamp)
(
@ -172,11 +206,18 @@ class IFreqaiModel(ABC):
if retrain:
self.train_timer('start')
self.train_model_in_series(
try:
self.extract_data_and_train_model(
new_trained_timerange, pair, strategy, dk, data_load_timerange
)
except Exception as msg:
logger.warning(f'Training {pair} raised exception {msg}, skipping.')
self.train_timer('stop')
# only rotate the queue after the first has been trained.
self.train_queue.rotate(-1)
self.dd.save_historic_predictions_to_disk()
def start_backtesting(
@ -204,7 +245,8 @@ class IFreqaiModel(ABC):
# following tr_train. Both of these windows slide through the
# entire backtest
for tr_train, tr_backtest in zip(dk.training_timeranges, dk.backtesting_timeranges):
(_, _, _) = self.dd.get_pair_dict_info(metadata["pair"])
pair = metadata["pair"]
(_, _, _) = self.dd.get_pair_dict_info(pair)
train_it += 1
total_trains = len(dk.backtesting_timeranges)
self.training_timerange = tr_train
@ -212,40 +254,53 @@ class IFreqaiModel(ABC):
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
trained_timestamp = tr_train
tr_train_startts_str = datetime.datetime.utcfromtimestamp(tr_train.startts).strftime(
"%Y-%m-%d %H:%M:%S"
)
tr_train_stopts_str = datetime.datetime.utcfromtimestamp(tr_train.stopts).strftime(
"%Y-%m-%d %H:%M:%S"
)
tr_train_startts_str = datetime.fromtimestamp(
tr_train.startts,
tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
tr_train_stopts_str = datetime.fromtimestamp(
tr_train.stopts,
tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
logger.info(
f"Training {metadata['pair']}, {self.pair_it}/{self.total_pairs} pairs"
f"Training {pair}, {self.pair_it}/{self.total_pairs} pairs"
f" from {tr_train_startts_str} to {tr_train_stopts_str}, {train_it}/{total_trains} "
"trains"
)
trained_timestamp_int = int(trained_timestamp.stopts)
dk.data_path = Path(
dk.full_path
/
f"sub-train-{metadata['pair'].split('/')[0]}_{int(trained_timestamp.stopts)}"
dk.full_path / f"sub-train-{pair.split('/')[0]}_{trained_timestamp_int}"
)
if not self.model_exists(
metadata["pair"], dk, trained_timestamp=int(trained_timestamp.stopts)
):
dk.find_features(dataframe_train)
self.model = self.train(dataframe_train, metadata["pair"], dk)
self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = int(
trained_timestamp.stopts)
dk.set_new_model_names(metadata["pair"], trained_timestamp)
self.dd.save_data(self.model, metadata["pair"], dk)
else:
self.model = self.dd.load_data(metadata["pair"], dk)
dk.set_new_model_names(pair, trained_timestamp)
if dk.check_if_backtest_prediction_exists():
self.dd.load_metadata(dk)
self.check_if_feature_list_matches_strategy(dataframe_train, dk)
append_df = dk.get_backtesting_prediction()
dk.append_predictions(append_df)
else:
if not self.model_exists(dk):
dk.find_features(dataframe_train)
dk.find_labels(dataframe_train)
self.model = self.train(dataframe_train, pair, dk)
self.dd.pair_dict[pair]["trained_timestamp"] = int(
trained_timestamp.stopts)
if self.plot_features:
plot_feature_importance(self.model, pair, dk, self.plot_features)
if self.save_backtest_models:
logger.info('Saving backtest model to disk.')
self.dd.save_data(self.model, pair, dk)
else:
logger.info('Saving metadata to disk.')
self.dd.save_metadata(dk)
else:
self.model = self.dd.load_data(pair, dk)
# self.check_if_feature_list_matches_strategy(dataframe_train, dk)
pred_df, do_preds = self.predict(dataframe_backtest, dk)
dk.append_predictions(pred_df, do_preds)
append_df = dk.get_predictions_to_append(pred_df, do_preds)
dk.append_predictions(append_df)
dk.save_backtesting_prediction(append_df)
dk.fill_predictions(dataframe)
@ -290,14 +345,8 @@ class IFreqaiModel(ABC):
)
dk.set_paths(metadata["pair"], new_trained_timerange.stopts)
# download candle history if it is not already in memory
# load candle history into memory if it is not yet.
if not self.dd.historic_data:
logger.info(
"Downloading all training data for all pairs in whitelist and "
"corr_pairlist, this may take a while if you do not have the "
"data saved"
)
dk.download_all_data_for_training(data_load_timerange, strategy.dp)
self.dd.load_all_pair_histories(data_load_timerange, dk)
if not self.scanning:
@ -326,8 +375,7 @@ class IFreqaiModel(ABC):
self.dd.return_null_values_to_strategy(dataframe, dk)
return dk
# ensure user is feeding the correct indicators to the model
self.check_if_feature_list_matches_strategy(dataframe, dk)
dk.find_labels(dataframe)
self.build_strategy_return_arrays(dataframe, dk, metadata["pair"], trained_timestamp)
@ -385,36 +433,44 @@ class IFreqaiModel(ABC):
if "training_features_list_raw" in dk.data:
feature_list = dk.data["training_features_list_raw"]
else:
feature_list = dk.training_features_list
feature_list = dk.data['training_features_list']
if dk.training_features_list != feature_list:
raise OperationalException(
"Trying to access pretrained model with `identifier` "
"but found different features furnished by current strategy."
"Change `identifier` to train from scratch, or ensure the"
"strategy is furnishing the same features as the pretrained"
"model"
"model. In case of --strategy-list, please be aware that FreqAI "
"requires all strategies to maintain identical "
"populate_any_indicator() functions"
)
def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None:
"""
Base data cleaning method for train
Any function inside this method should drop training data points from the filtered_dataframe
based on user decided logic. See FreqaiDataKitchen::use_SVM_to_remove_outliers() for an
example of how outlier data points are dropped from the dataframe used for training.
Base data cleaning method for train.
Functions here improve/modify the input data by identifying outliers,
computing additional metrics, adding noise, reducing dimensionality etc.
"""
if self.freqai_info["feature_parameters"].get(
ft_params = self.freqai_info["feature_parameters"]
if ft_params.get('inlier_metric_window', 0):
dk.compute_inlier_metric(set_='train')
if self.freqai_info["data_split_parameters"]["test_size"] > 0:
dk.compute_inlier_metric(set_='test')
if ft_params.get(
"principal_component_analysis", False
):
dk.principal_component_analysis()
if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
if ft_params.get("use_SVM_to_remove_outliers", False):
dk.use_SVM_to_remove_outliers(predict=False)
if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
if ft_params.get("DI_threshold", 0):
dk.data["avg_mean_dist"] = dk.compute_distances()
if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
if ft_params.get("use_DBSCAN_to_remove_outliers", False):
if dk.pair in self.dd.old_DBSCAN_eps:
eps = self.dd.old_DBSCAN_eps[dk.pair]
else:
@ -422,39 +478,37 @@ class IFreqaiModel(ABC):
dk.use_DBSCAN_to_remove_outliers(predict=False, eps=eps)
self.dd.old_DBSCAN_eps[dk.pair] = dk.data['DBSCAN_eps']
if self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0):
dk.add_noise_to_training_features()
def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
"""
Base data cleaning method for predict.
These functions each modify dk.do_predict, which is a dataframe with equal length
to the number of candles coming from and returning to the strategy. Inside do_predict,
1 allows prediction and < 0 signals to the strategy that the model is not confident in
the prediction.
See FreqaiDataKitchen::remove_outliers() for an example
of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
for buy signals.
Functions here are complementary to the functions of data_cleaning_train.
"""
if self.freqai_info["feature_parameters"].get(
ft_params = self.freqai_info["feature_parameters"]
if ft_params.get('inlier_metric_window', 0):
dk.compute_inlier_metric(set_='predict')
if ft_params.get(
"principal_component_analysis", False
):
dk.pca_transform(dataframe)
dk.pca_transform(dk.data_dictionary['prediction_features'])
if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
if ft_params.get("use_SVM_to_remove_outliers", False):
dk.use_SVM_to_remove_outliers(predict=True)
if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
if ft_params.get("DI_threshold", 0):
dk.check_if_pred_in_training_spaces()
if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
if ft_params.get("use_DBSCAN_to_remove_outliers", False):
dk.use_DBSCAN_to_remove_outliers(predict=True)
def model_exists(
self,
pair: str,
dk: FreqaiDataKitchen,
trained_timestamp: int = None,
model_filename: str = "",
scanning: bool = False,
) -> bool:
# ensure user is feeding the correct indicators to the model
self.check_if_feature_list_matches_strategy(dk.data_dictionary['prediction_features'], dk)
def model_exists(self, dk: FreqaiDataKitchen) -> bool:
"""
Given a pair and path, check if a model already exists
:param pair: pair e.g. BTC/USD
@ -462,16 +516,11 @@ class IFreqaiModel(ABC):
:return:
:boolean: whether the model file exists or not.
"""
coin, _ = pair.split("/")
if not self.live:
dk.model_filename = model_filename = f"cb_{coin.lower()}_{trained_timestamp}"
path_to_modelfile = Path(dk.data_path / f"{model_filename}_model.joblib")
path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model.joblib")
file_exists = path_to_modelfile.is_file()
if file_exists and not scanning:
if file_exists:
logger.info("Found model at %s", dk.data_path / dk.model_filename)
elif not scanning:
else:
logger.info("Could not find model at %s", dk.data_path / dk.model_filename)
return file_exists
@ -485,7 +534,7 @@ class IFreqaiModel(ABC):
Path(self.full_path, Path(self.config["config_files"][0]).name),
)
def train_model_in_series(
def extract_data_and_train_model(
self,
new_trained_timerange: TimeRange,
pair: str,
@ -518,16 +567,17 @@ class IFreqaiModel(ABC):
# find the features indicated by strategy and store in datakitchen
dk.find_features(unfiltered_dataframe)
dk.find_labels(unfiltered_dataframe)
model = self.train(unfiltered_dataframe, pair, dk)
self.dd.pair_dict[pair]["trained_timestamp"] = new_trained_timerange.stopts
dk.set_new_model_names(pair, new_trained_timerange)
self.dd.pair_dict[pair]["first"] = False
if self.dd.pair_dict[pair]["priority"] == 1 and self.scanning:
self.dd.pair_to_end_of_training_queue(pair)
self.dd.save_data(model, pair, dk)
if self.plot_features:
plot_feature_importance(model, pair, dk, self.plot_features)
if self.freqai_info.get("purge_old_models", False):
self.dd.purge_old_models()
@ -577,7 +627,7 @@ class IFreqaiModel(ABC):
# # for keras type models, the conv_window needs to be prepended so
# # viewing is correct in frequi
if self.freqai_info.get('keras', False):
if self.freqai_info.get('keras', False) or self.ft_params.get('inlier_metric_window', 0):
n_lost_points = self.freqai_info.get('conv_width', 2)
zeros_df = DataFrame(np.zeros((n_lost_points, len(hist_preds_df.columns))),
columns=hist_preds_df.columns)
@ -619,8 +669,8 @@ class IFreqaiModel(ABC):
logger.info(
f'Total time spent inferencing pairlist {self.inference_time:.2f} seconds')
if self.inference_time > 0.25 * self.base_tf_seconds:
logger.warning('Inference took over 25/% of the candle time. Reduce pairlist to'
' avoid blinding open trades and degrading performance.')
logger.warning("Inference took over 25% of the candle time. Reduce pairlist to"
" avoid blinding open trades and degrading performance.")
self.pair_it = 0
self.inference_time = 0
return
@ -643,21 +693,56 @@ class IFreqaiModel(ABC):
self.train_time = 0
return
def get_init_model(self, pair: str) -> Any:
if pair not in self.dd.model_dictionary or not self.continual_learning:
init_model = None
else:
init_model = self.dd.model_dictionary[pair]
return init_model
def _set_train_queue(self):
"""
Sets train queue from existing train timestamps if they exist
otherwise it sets the train queue based on the provided whitelist.
"""
current_pairlist = self.config.get("exchange", {}).get("pair_whitelist")
if not self.dd.pair_dict:
logger.info('Set fresh train queue from whitelist. '
f'Queue: {current_pairlist}')
return deque(current_pairlist)
best_queue = deque()
pair_dict_sorted = sorted(self.dd.pair_dict.items(),
key=lambda k: k[1]['trained_timestamp'])
for pair in pair_dict_sorted:
if pair[0] in current_pairlist:
best_queue.append(pair[0])
for pair in current_pairlist:
if pair not in best_queue:
best_queue.appendleft(pair)
logger.info('Set existing queue from trained timestamps. '
f'Best approximation queue: {best_queue}')
return best_queue
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModel.py for an example.
@abstractmethod
def train(self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen) -> Any:
def train(self, unfiltered_df: DataFrame, pair: str,
dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
Filter the training data and train a model to it. Train makes heavy use of the datahandler
for storing, saving, loading, and analyzing the data.
:param unfiltered_dataframe: Full dataframe for the current training period
:param unfiltered_df: Full dataframe for the current training period
:param metadata: pair metadata from strategy.
:return: Trained model which can be used to inference (self.predict)
"""
@abstractmethod
def fit(self, data_dictionary: Dict[str, Any]) -> Any:
def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
Most regressors use the same function names and arguments e.g. user
can drop in LGBMRegressor in place of CatBoostRegressor and all data
@ -670,11 +755,11 @@ class IFreqaiModel(ABC):
@abstractmethod
def predict(
self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = True
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param unfiltered_dataframe: Full dataframe for the current backtest period.
:param unfiltered_df: Full dataframe for the current backtest period.
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
:param first: boolean = whether this is the first prediction or not.
:return:

9
freqtrade/freqai/prediction_models/CatboostClassifier.py
View File

@ -3,7 +3,8 @@ from typing import Any, Dict
from catboost import CatBoostClassifier, Pool
from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@ -16,7 +17,7 @@ class CatboostClassifier(BaseClassifierModel):
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
@ -36,6 +37,8 @@ class CatboostClassifier(BaseClassifierModel):
**self.model_training_parameters,
)
cbr.fit(train_data)
init_model = self.get_init_model(dk.pair)
cbr.fit(train_data, init_model=init_model)
return cbr

15
freqtrade/freqai/prediction_models/CatboostRegressor.py
View File

@ -1,10 +1,10 @@
import gc
import logging
from typing import Any, Dict
from catboost import CatBoostRegressor, Pool
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@ -17,7 +17,7 @@ class CatboostRegressor(BaseRegressionModel):
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold
@ -38,16 +38,13 @@ class CatboostRegressor(BaseRegressionModel):
weight=data_dictionary["test_weights"],
)
init_model = self.get_init_model(dk.pair)
model = CatBoostRegressor(
allow_writing_files=False,
**self.model_training_parameters,
)
model.fit(X=train_data, eval_set=test_data)
# some evidence that catboost pools have memory leaks:
# https://github.com/catboost/catboost/issues/1835
del train_data, test_data
gc.collect()
model.fit(X=train_data, eval_set=test_data, init_model=init_model)
return model

44
freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py
View File

@ -1,10 +1,11 @@
import logging
from typing import Any, Dict
from catboost import CatBoostRegressor # , Pool
from sklearn.multioutput import MultiOutputRegressor
from catboost import CatBoostRegressor, Pool
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@ -17,7 +18,7 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold
@ -31,14 +32,37 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
sample_weight = data_dictionary["train_weights"]
model = MultiOutputRegressor(estimator=cbr)
model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set)
eval_sets = [None] * y.shape[1]
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
train_score = model.score(X, y)
test_score = model.score(*eval_set)
logger.info(f"Train score {train_score}, Test score {test_score}")
eval_sets = [None] * data_dictionary['test_labels'].shape[1]
for i in range(data_dictionary['test_labels'].shape[1]):
eval_sets[i] = Pool(
data=data_dictionary["test_features"],
label=data_dictionary["test_labels"].iloc[:, i],
weight=data_dictionary["test_weights"],
)
init_model = self.get_init_model(dk.pair)
if init_model:
init_models = init_model.estimators_
else:
init_models = [None] * y.shape[1]
fit_params = []
for i in range(len(eval_sets)):
fit_params.append(
{'eval_set': eval_sets[i], 'init_model': init_models[i]})
model = FreqaiMultiOutputRegressor(estimator=cbr)
thread_training = self.freqai_info.get('multitarget_parallel_training', False)
if thread_training:
model.n_jobs = y.shape[1]
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
return model

9
freqtrade/freqai/prediction_models/LightGBMClassifier.py
View File

@ -3,7 +3,8 @@ from typing import Any, Dict
from lightgbm import LGBMClassifier
from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@ -16,7 +17,7 @@ class LightGBMClassifier(BaseClassifierModel):
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
@ -35,9 +36,11 @@ class LightGBMClassifier(BaseClassifierModel):
y = data_dictionary["train_labels"].to_numpy()[:, 0]
train_weights = data_dictionary["train_weights"]
init_model = self.get_init_model(dk.pair)
model = LGBMClassifier(**self.model_training_parameters)
model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
eval_sample_weight=[test_weights])
eval_sample_weight=[test_weights], init_model=init_model)
return model

9
freqtrade/freqai/prediction_models/LightGBMRegressor.py
View File

@ -3,7 +3,8 @@ from typing import Any, Dict
from lightgbm import LGBMRegressor
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@ -16,7 +17,7 @@ class LightGBMRegressor(BaseRegressionModel):
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
Most regressors use the same function names and arguments e.g. user
can drop in LGBMRegressor in place of CatBoostRegressor and all data
@ -35,9 +36,11 @@ class LightGBMRegressor(BaseRegressionModel):
y = data_dictionary["train_labels"]
train_weights = data_dictionary["train_weights"]
init_model = self.get_init_model(dk.pair)
model = LGBMRegressor(**self.model_training_parameters)
model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
eval_sample_weight=[eval_weights])
eval_sample_weight=[eval_weights], init_model=init_model)
return model

43
freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py
View File

@ -2,9 +2,10 @@ import logging
from typing import Any, Dict
from lightgbm import LGBMRegressor
from sklearn.multioutput import MultiOutputRegressor
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@ -17,7 +18,7 @@ class LightGBMRegressorMultiTarget(BaseRegressionModel):
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold
@ -28,12 +29,36 @@ class LightGBMRegressorMultiTarget(BaseRegressionModel):
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
sample_weight = data_dictionary["train_weights"]
model = MultiOutputRegressor(estimator=lgb)
model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set)
train_score = model.score(X, y)
test_score = model.score(*eval_set)
logger.info(f"Train score {train_score}, Test score {test_score}")
eval_weights = None
eval_sets = [None] * y.shape[1]
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
eval_weights = [data_dictionary["test_weights"]]
eval_sets = [(None, None)] * data_dictionary['test_labels'].shape[1] # type: ignore
for i in range(data_dictionary['test_labels'].shape[1]):
eval_sets[i] = ( # type: ignore
data_dictionary["test_features"],
data_dictionary["test_labels"].iloc[:, i]
)
init_model = self.get_init_model(dk.pair)
if init_model:
init_models = init_model.estimators_
else:
init_models = [None] * y.shape[1]
fit_params = []
for i in range(len(eval_sets)):
fit_params.append(
{'eval_set': eval_sets[i], 'eval_sample_weight': eval_weights,
'init_model': init_models[i]})
model = FreqaiMultiOutputRegressor(estimator=lgb)
thread_training = self.freqai_info.get('multitarget_parallel_training', False)
if thread_training:
model.n_jobs = y.shape[1]
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
return model

85
freqtrade/freqai/prediction_models/XGBoostClassifier.py Normal file
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@ -0,0 +1,85 @@
import logging
from typing import Any, Dict, Tuple
import numpy as np
import numpy.typing as npt
import pandas as pd
from pandas import DataFrame
from pandas.api.types import is_integer_dtype
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBClassifier
from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
class XGBoostClassifier(BaseClassifierModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
X = data_dictionary["train_features"].to_numpy()
y = data_dictionary["train_labels"].to_numpy()[:, 0]
le = LabelEncoder()
if not is_integer_dtype(y):
y = pd.Series(le.fit_transform(y), dtype="int64")
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
eval_set = None
else:
test_features = data_dictionary["test_features"].to_numpy()
test_labels = data_dictionary["test_labels"].to_numpy()[:, 0]
if not is_integer_dtype(test_labels):
test_labels = pd.Series(le.transform(test_labels), dtype="int64")
eval_set = [(test_features, test_labels)]
train_weights = data_dictionary["train_weights"]
init_model = self.get_init_model(dk.pair)
model = XGBClassifier(**self.model_training_parameters)
model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
xgb_model=init_model)
return model
def predict(
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_df: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (PCA and DI index)
"""
(pred_df, dk.do_predict) = super().predict(unfiltered_df, dk, **kwargs)
le = LabelEncoder()
label = dk.label_list[0]
labels_before = list(dk.data['labels_std'].keys())
labels_after = le.fit_transform(labels_before).tolist()
pred_df[label] = le.inverse_transform(pred_df[label])
pred_df = pred_df.rename(
columns={labels_after[i]: labels_before[i] for i in range(len(labels_before))})
return (pred_df, dk.do_predict)

45
freqtrade/freqai/prediction_models/XGBoostRegressor.py Normal file
View File

@ -0,0 +1,45 @@
import logging
from typing import Any, Dict
from xgboost import XGBRegressor
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
class XGBoostRegressor(BaseRegressionModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
eval_set = None
else:
eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
eval_weights = [data_dictionary['test_weights']]
sample_weight = data_dictionary["train_weights"]
xgb_model = self.get_init_model(dk.pair)
model = XGBRegressor(**self.model_training_parameters)
model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set,
sample_weight_eval_set=eval_weights, xgb_model=xgb_model)
return model

63
freqtrade/freqai/prediction_models/XGBoostRegressorMultiTarget.py Normal file
View File

@ -0,0 +1,63 @@
import logging
from typing import Any, Dict
from xgboost import XGBRegressor
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
class XGBoostRegressorMultiTarget(BaseRegressionModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
xgb = XGBRegressor(**self.model_training_parameters)
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
sample_weight = data_dictionary["train_weights"]
eval_weights = None
eval_sets = [None] * y.shape[1]
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
eval_weights = [data_dictionary["test_weights"]]
for i in range(data_dictionary['test_labels'].shape[1]):
eval_sets[i] = [( # type: ignore
data_dictionary["test_features"],
data_dictionary["test_labels"].iloc[:, i]
)]
init_model = self.get_init_model(dk.pair)
if init_model:
init_models = init_model.estimators_
else:
init_models = [None] * y.shape[1]
fit_params = []
for i in range(len(eval_sets)):
fit_params.append(
{'eval_set': eval_sets[i], 'sample_weight_eval_set': eval_weights,
'xgb_model': init_models[i]})
model = FreqaiMultiOutputRegressor(estimator=xgb)
thread_training = self.freqai_info.get('multitarget_parallel_training', False)
if thread_training:
model.n_jobs = y.shape[1]
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
return model

193
freqtrade/freqai/utils.py Normal file
View File

@ -0,0 +1,193 @@
import logging
from datetime import datetime, timezone
from typing import Any
import numpy as np
import pandas as pd
from freqtrade.configuration import TimeRange
from freqtrade.constants import Config
from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.exchange.exchange import market_is_active
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
logger = logging.getLogger(__name__)
def download_all_data_for_training(dp: DataProvider, config: Config) -> None:
"""
Called only once upon start of bot to download the necessary data for
populating indicators and training the model.
:param timerange: TimeRange = The full data timerange for populating the indicators
and training the model.
:param dp: DataProvider instance attached to the strategy
"""
if dp._exchange is None:
raise OperationalException('No exchange object found.')
markets = [p for p, m in dp._exchange.markets.items() if market_is_active(m)
or config.get('include_inactive')]
all_pairs = dynamic_expand_pairlist(config, markets)
timerange = get_required_data_timerange(config)
new_pairs_days = int((timerange.stopts - timerange.startts) / 86400)
refresh_backtest_ohlcv_data(
dp._exchange,
pairs=all_pairs,
timeframes=config["freqai"]["feature_parameters"].get("include_timeframes"),
datadir=config["datadir"],
timerange=timerange,
new_pairs_days=new_pairs_days,
erase=False,
data_format=config.get("dataformat_ohlcv", "json"),
trading_mode=config.get("trading_mode", "spot"),
prepend=config.get("prepend_data", False),
)
def get_required_data_timerange(config: Config) -> TimeRange:
"""
Used to compute the required data download time range
for auto data-download in FreqAI
"""
time = datetime.now(tz=timezone.utc).timestamp()
timeframes = config["freqai"]["feature_parameters"].get("include_timeframes")
max_tf_seconds = 0
for tf in timeframes:
secs = timeframe_to_seconds(tf)
if secs > max_tf_seconds:
max_tf_seconds = secs
startup_candles = config.get('startup_candle_count', 0)
indicator_periods = config["freqai"]["feature_parameters"]["indicator_periods_candles"]
# factor the max_period as a factor of safety.
max_period = int(max(startup_candles, max(indicator_periods)) * 1.5)
config['startup_candle_count'] = max_period
logger.info(f'FreqAI auto-downloader using {max_period} startup candles.')
additional_seconds = max_period * max_tf_seconds
startts = int(
time
- config["freqai"].get("train_period_days", 0) * 86400
- additional_seconds
)
stopts = int(time)
data_load_timerange = TimeRange('date', 'date', startts, stopts)
return data_load_timerange
# Keep below for when we wish to download heterogeneously lengthed data for FreqAI.
# def download_all_data_for_training(dp: DataProvider, config: Config) -> None:
# """
# Called only once upon start of bot to download the necessary data for
# populating indicators and training a FreqAI model.
# :param timerange: TimeRange = The full data timerange for populating the indicators
# and training the model.
# :param dp: DataProvider instance attached to the strategy
# """
# if dp._exchange is not None:
# markets = [p for p, m in dp._exchange.markets.items() if market_is_active(m)
# or config.get('include_inactive')]
# else:
# # This should not occur:
# raise OperationalException('No exchange object found.')
# all_pairs = dynamic_expand_pairlist(config, markets)
# if not dp._exchange:
# # Not realistic - this is only called in live mode.
# raise OperationalException("Dataprovider did not have an exchange attached.")
# time = datetime.now(tz=timezone.utc).timestamp()
# for tf in config["freqai"]["feature_parameters"].get("include_timeframes"):
# timerange = TimeRange()
# timerange.startts = int(time)
# timerange.stopts = int(time)
# startup_candles = dp.get_required_startup(str(tf))
# tf_seconds = timeframe_to_seconds(str(tf))
# timerange.subtract_start(tf_seconds * startup_candles)
# new_pairs_days = int((timerange.stopts - timerange.startts) / 86400)
# # FIXME: now that we are looping on `refresh_backtest_ohlcv_data`, the function
# # redownloads the funding rate for each pair.
# refresh_backtest_ohlcv_data(
# dp._exchange,
# pairs=all_pairs,
# timeframes=[tf],
# datadir=config["datadir"],
# timerange=timerange,
# new_pairs_days=new_pairs_days,
# erase=False,
# data_format=config.get("dataformat_ohlcv", "json"),
# trading_mode=config.get("trading_mode", "spot"),
# prepend=config.get("prepend_data", False),
# )
def plot_feature_importance(model: Any, pair: str, dk: FreqaiDataKitchen,
count_max: int = 25) -> None:
"""
Plot Best and worst features by importance for a single sub-train.
:param model: Any = A model which was `fit` using a common library
such as catboost or lightgbm
:param pair: str = pair e.g. BTC/USD
:param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
:param count_max: int = the amount of features to be loaded per column
"""
from freqtrade.plot.plotting import go, make_subplots, store_plot_file
# Extract feature importance from model
models = {}
if 'FreqaiMultiOutputRegressor' in str(model.__class__):
for estimator, label in zip(model.estimators_, dk.label_list):
models[label] = estimator
else:
models[dk.label_list[0]] = model
for label in models:
mdl = models[label]
if "catboost.core" in str(mdl.__class__):
feature_importance = mdl.get_feature_importance()
elif "lightgbm.sklearn" or "xgb" in str(mdl.__class__):
feature_importance = mdl.feature_importances_
else:
logger.info('Model type not support for generating feature importances.')
return
# Data preparation
fi_df = pd.DataFrame({
"feature_names": np.array(dk.data_dictionary['train_features'].columns),
"feature_importance": np.array(feature_importance)
})
fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1]
fi_df_worst = fi_df.nsmallest(count_max, "feature_importance")[::-1]
# Plotting
def add_feature_trace(fig, fi_df, col):
return fig.add_trace(
go.Bar(
x=fi_df["feature_importance"],
y=fi_df["feature_names"],
orientation='h', showlegend=False
), row=1, col=col
)
fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.5)
fig = add_feature_trace(fig, fi_df_top, 1)
fig = add_feature_trace(fig, fi_df_worst, 2)
fig.update_layout(title_text=f"Best and worst features by importance {pair}")
label = label.replace('&', '').replace('%', '') # escape two FreqAI specific characters
store_plot_file(fig, f"{dk.model_filename}-{label}.html", dk.data_path)

156
freqtrade/freqtradebot.py
View File

@ -11,9 +11,9 @@ from typing import Any, Dict, List, Optional, Tuple
from schedule import Scheduler
from freqtrade import __version__, constants
from freqtrade import constants
from freqtrade.configuration import validate_config_consistency
from freqtrade.constants import BuySell, LongShort
from freqtrade.constants import BuySell, Config, LongShort
from freqtrade.data.converter import order_book_to_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.edge import Edge
@ -29,6 +29,7 @@ from freqtrade.plugins.pairlistmanager import PairListManager
from freqtrade.plugins.protectionmanager import ProtectionManager
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.rpc import RPCManager
from freqtrade.rpc.external_message_consumer import ExternalMessageConsumer
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.util import FtPrecise
@ -44,7 +45,7 @@ class FreqtradeBot(LoggingMixin):
This is from here the bot start its logic.
"""
def __init__(self, config: Dict[str, Any]) -> None:
def __init__(self, config: Config) -> None:
"""
Init all variables and objects the bot needs to work
:param config: configuration dict, you can use Configuration.get_config()
@ -52,8 +53,6 @@ class FreqtradeBot(LoggingMixin):
"""
self.active_pair_whitelist: List[str] = []
logger.info('Starting freqtrade %s', __version__)
# Init bot state
self.state = State.STOPPED
@ -74,6 +73,8 @@ class FreqtradeBot(LoggingMixin):
PairLocks.timeframe = self.config['timeframe']
self.pairlists = PairListManager(self.exchange, self.config)
# RPC runs in separate threads, can start handling external commands just after
# initialization, even before Freqtradebot has a chance to start its throttling,
# so anything in the Freqtradebot instance should be ready (initialized), including
@ -81,9 +82,7 @@ class FreqtradeBot(LoggingMixin):
# Keep this at the end of this initialization method.
self.rpc: RPCManager = RPCManager(self)
self.pairlists = PairListManager(self.exchange, self.config)
self.dataprovider = DataProvider(self.config, self.exchange, self.pairlists)
self.dataprovider = DataProvider(self.config, self.exchange, self.pairlists, self.rpc)
# Attach Dataprovider to strategy instance
self.strategy.dp = self.dataprovider
@ -94,6 +93,10 @@ class FreqtradeBot(LoggingMixin):
self.edge = Edge(self.config, self.exchange, self.strategy) if \
self.config.get('edge', {}).get('enabled', False) else None
# Init ExternalMessageConsumer if enabled
self.emc = ExternalMessageConsumer(self.config, self.dataprovider) if \
self.config.get('external_message_consumer', {}).get('enabled', False) else None
self.active_pair_whitelist = self._refresh_active_whitelist()
# Set initial bot state from config
@ -142,13 +145,20 @@ class FreqtradeBot(LoggingMixin):
:return: None
"""
logger.info('Cleaning up modules ...')
try:
# Wrap db activities in shutdown to avoid problems if database is gone,
# and raises further exceptions.
if self.config['cancel_open_orders_on_exit']:
self.cancel_all_open_orders()
self.check_for_open_trades()
finally:
self.strategy.ft_bot_cleanup()
self.rpc.cleanup()
if self.emc:
self.emc.shutdown()
Trade.commit()
self.exchange.close()
@ -251,6 +261,7 @@ class FreqtradeBot(LoggingMixin):
pairs that have open trades.
"""
# Refresh whitelist
_prev_whitelist = self.pairlists.whitelist
self.pairlists.refresh_pairlist()
_whitelist = self.pairlists.whitelist
@ -263,6 +274,11 @@ class FreqtradeBot(LoggingMixin):
# Extend active-pair whitelist with pairs of open trades
# It ensures that candle (OHLCV) data are downloaded for open trades as well
_whitelist.extend([trade.pair for trade in trades if trade.pair not in _whitelist])
# Called last to include the included pairs
if _prev_whitelist != _whitelist:
self.rpc.send_msg({'type': RPCMessageType.WHITELIST, 'data': _whitelist})
return _whitelist
def get_free_open_trades(self) -> int:
@ -276,16 +292,17 @@ class FreqtradeBot(LoggingMixin):
def update_funding_fees(self):
if self.trading_mode == TradingMode.FUTURES:
trades = Trade.get_open_trades()
try:
for trade in trades:
funding_fees = self.exchange.get_funding_fees(
pair=trade.pair,
amount=trade.amount,
is_short=trade.is_short,
open_date=trade.open_date_utc
open_date=trade.date_last_filled_utc
)
trade.funding_fees = funding_fees
else:
return 0.0
except ExchangeError:
logger.warning("Could not update funding fees for open trades.")
def startup_backpopulate_precision(self):
@ -578,7 +595,9 @@ class FreqtradeBot(LoggingMixin):
if stake_amount is not None and stake_amount < 0.0:
# We should decrease our position
amount = abs(float(FtPrecise(stake_amount) / FtPrecise(current_exit_rate)))
amount = self.exchange.amount_to_contract_precision(
trade.pair,
abs(float(FtPrecise(stake_amount) / FtPrecise(current_exit_rate))))
if amount > trade.amount:
# This is currently ineffective as remaining would become < min tradable
# Fixing this would require checking for 0.0 there -
@ -587,9 +606,14 @@ class FreqtradeBot(LoggingMixin):
f"Adjusting amount to trade.amount as it is higher. {amount} > {trade.amount}")
amount = trade.amount
if amount == 0.0:
logger.info("Amount to exit is 0.0 due to exchange limits - not exiting.")
return
remaining = (trade.amount - amount) * current_exit_rate
if remaining < min_exit_stake:
logger.info(f'Remaining amount of {remaining} would be too small.')
logger.info(f"Remaining amount of {remaining} would be smaller "
f"than the minimum of {min_exit_stake}.")
return
self.execute_trade_exit(trade, current_exit_rate, exit_check=ExitCheckTuple(
@ -659,14 +683,12 @@ class FreqtradeBot(LoggingMixin):
if not stake_amount:
return False
if pos_adjust:
logger.info(f"Position adjust: about to create a new order for {pair} with stake: "
f"{stake_amount} for {trade}")
else:
logger.info(
msg = (f"Position adjust: about to create a new order for {pair} with stake: "
f"{stake_amount} for {trade}" if pos_adjust
else
f"{name} signal found: about create a new trade for {pair} with stake_amount: "
f"{stake_amount} ...")
logger.info(msg)
amount = (stake_amount / enter_limit_requested) * leverage
order_type = ordertype or self.strategy.order_types['entry']
@ -726,10 +748,16 @@ class FreqtradeBot(LoggingMixin):
fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
base_currency = self.exchange.get_pair_base_currency(pair)
open_date = datetime.now(timezone.utc)
funding_fees = self.exchange.get_funding_fees(
pair=pair, amount=amount, is_short=is_short, open_date=open_date)
# This is a new trade
if trade is None:
funding_fees = 0.0
try:
funding_fees = self.exchange.get_funding_fees(
pair=pair, amount=amount, is_short=is_short, open_date=open_date)
except ExchangeError:
logger.warning("Could not find funding fee.")
trade = Trade(
pair=pair,
base_currency=base_currency,
@ -906,7 +934,7 @@ class FreqtradeBot(LoggingMixin):
'stake_amount': trade.stake_amount,
'stake_currency': self.config['stake_currency'],
'fiat_currency': self.config.get('fiat_display_currency', None),
'amount': order.safe_amount_after_fee,
'amount': order.safe_amount_after_fee if fill else (order.amount or trade.amount),
'open_date': trade.open_date or datetime.utcnow(),
'current_rate': current_rate,
'sub_trade': sub_trade,
@ -1055,6 +1083,7 @@ class FreqtradeBot(LoggingMixin):
order_obj = Order.parse_from_ccxt_object(stoploss_order, trade.pair, 'stoploss')
trade.orders.append(order_obj)
trade.stoploss_order_id = str(stoploss_order['id'])
trade.stoploss_last_update = datetime.now(timezone.utc)
return True
except InsufficientFundsError as e:
logger.warning(f"Unable to place stoploss order {e}.")
@ -1128,10 +1157,9 @@ class FreqtradeBot(LoggingMixin):
if self.create_stoploss_order(trade=trade, stop_price=stop_price):
# The above will return False if the placement failed and the trade was force-sold.
# in which case the trade will be closed - which we must check below.
trade.stoploss_last_update = datetime.utcnow()
return False
# If stoploss order is canceled for some reason we add it
# If stoploss order is canceled for some reason we add it again
if (trade.is_open
and stoploss_order
and stoploss_order['status'] in ('canceled', 'cancelled')):
@ -1169,7 +1197,8 @@ class FreqtradeBot(LoggingMixin):
if self.exchange.stoploss_adjust(stoploss_norm, order, side=trade.exit_side):
# we check if the update is necessary
update_beat = self.strategy.order_types.get('stoploss_on_exchange_interval', 60)
if (datetime.utcnow() - trade.stoploss_last_update).total_seconds() >= update_beat:
upd_req = datetime.now(timezone.utc) - timedelta(seconds=update_beat)
if trade.stoploss_last_update_utc and upd_req >= trade.stoploss_last_update_utc:
# cancelling the current stoploss on exchange first
logger.info(f"Cancelling current stoploss on exchange for pair {trade.pair} "
f"(orderid:{order['id']}) in order to add another one ...")
@ -1480,12 +1509,16 @@ class FreqtradeBot(LoggingMixin):
:param exit_check: CheckTuple with signal and reason
:return: True if it succeeds False
"""
try:
trade.funding_fees = self.exchange.get_funding_fees(
pair=trade.pair,
amount=trade.amount,
is_short=trade.is_short,
open_date=trade.open_date_utc,
open_date=trade.date_last_filled_utc,
)
except ExchangeError:
logger.warning("Could not update funding fee.")
exit_type = 'exit'
exit_reason = exit_tag or exit_check.exit_reason
if exit_check.exit_type in (
@ -1577,14 +1610,14 @@ class FreqtradeBot(LoggingMixin):
# second condition is for mypy only; order will always be passed during sub trade
if sub_trade and order is not None:
amount = order.safe_filled if fill else order.amount
profit_rate = order.safe_price
order_rate: float = order.safe_price
profit = trade.calc_profit(rate=profit_rate, amount=amount, open_rate=trade.open_rate)
profit_ratio = trade.calc_profit_ratio(profit_rate, amount, trade.open_rate)
profit = trade.calc_profit(rate=order_rate, amount=amount, open_rate=trade.open_rate)
profit_ratio = trade.calc_profit_ratio(order_rate, amount, trade.open_rate)
else:
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit = trade.calc_profit(rate=profit_rate) + (0.0 if fill else trade.realized_profit)
profit_ratio = trade.calc_profit_ratio(profit_rate)
order_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit = trade.calc_profit(rate=order_rate) + (0.0 if fill else trade.realized_profit)
profit_ratio = trade.calc_profit_ratio(order_rate)
amount = trade.amount
gain = "profit" if profit_ratio > 0 else "loss"
@ -1597,11 +1630,12 @@ class FreqtradeBot(LoggingMixin):
'leverage': trade.leverage,
'direction': 'Short' if trade.is_short else 'Long',
'gain': gain,
'limit': profit_rate,
'limit': order_rate, # Deprecated
'order_rate': order_rate,
'order_type': order_type,
'amount': amount,
'open_rate': trade.open_rate,
'close_rate': profit_rate,
'close_rate': order_rate,
'current_rate': current_rate,
'profit_amount': profit,
'profit_ratio': profit_ratio,
@ -1732,12 +1766,12 @@ class FreqtradeBot(LoggingMixin):
# TODO: Margin will need to use interest_rate as well.
# interest_rate = self.exchange.get_interest_rate()
trade.set_liquidation_price(self.exchange.get_liquidation_price(
leverage=trade.leverage,
pair=trade.pair,
open_rate=trade.open_rate,
is_short=trade.is_short,
amount=trade.amount,
stake_amount=trade.stake_amount,
open_rate=trade.open_rate,
is_short=trade.is_short
wallet_balance=trade.stake_amount,
))
# Updating wallets when order is closed
@ -1778,7 +1812,7 @@ class FreqtradeBot(LoggingMixin):
self.rpc.send_msg(msg)
def apply_fee_conditional(self, trade: Trade, trade_base_currency: str,
amount: float, fee_abs: float) -> float:
amount: float, fee_abs: float, order_obj: Order) -> Optional[float]:
"""
Applies the fee to amount (either from Order or from Trades).
Can eat into dust if more than the required asset is available.
@ -1786,40 +1820,42 @@ class FreqtradeBot(LoggingMixin):
never in base currency.
"""
self.wallets.update()
if fee_abs != 0 and self.wallets.get_free(trade_base_currency) >= amount:
amount_ = amount
if order_obj.ft_order_side == trade.exit_side or order_obj.ft_order_side == 'stoploss':
# check against remaining amount!
amount_ = trade.amount - amount
if fee_abs != 0 and self.wallets.get_free(trade_base_currency) >= amount_:
# Eat into dust if we own more than base currency
logger.info(f"Fee amount for {trade} was in base currency - "
f"Eating Fee {fee_abs} into dust.")
elif fee_abs != 0:
real_amount = self.exchange.amount_to_precision(trade.pair, amount - fee_abs)
logger.info(f"Applying fee on amount for {trade} "
f"(from {amount} to {real_amount}).")
return real_amount
return amount
logger.info(f"Applying fee on amount for {trade}, fee={fee_abs}.")
return fee_abs
return None
def handle_order_fee(self, trade: Trade, order_obj: Order, order: Dict[str, Any]) -> None:
# Try update amount (binance-fix)
try:
new_amount = self.get_real_amount(trade, order, order_obj)
if not isclose(safe_value_fallback(order, 'filled', 'amount'), new_amount,
abs_tol=constants.MATH_CLOSE_PREC):
order_obj.ft_fee_base = trade.amount - new_amount
fee_abs = self.get_real_amount(trade, order, order_obj)
if fee_abs is not None:
order_obj.ft_fee_base = fee_abs
except DependencyException as exception:
logger.warning("Could not update trade amount: %s", exception)
def get_real_amount(self, trade: Trade, order: Dict, order_obj: Order) -> float:
def get_real_amount(self, trade: Trade, order: Dict, order_obj: Order) -> Optional[float]:
"""
Detect and update trade fee.
Calls trade.update_fee() upon correct detection.
Returns modified amount if the fee was taken from the destination currency.
Necessary for exchanges which charge fees in base currency (e.g. binance)
:return: identical (or new) amount for the trade
:return: Absolute fee to apply for this order or None
"""
# Init variables
order_amount = safe_value_fallback(order, 'filled', 'amount')
# Only run for closed orders
if trade.fee_updated(order.get('side', '')) or order['status'] == 'open':
return order_amount
return None
trade_base_currency = self.exchange.get_pair_base_currency(trade.pair)
# use fee from order-dict if possible
@ -1836,13 +1872,14 @@ class FreqtradeBot(LoggingMixin):
if trade_base_currency == fee_currency:
# Apply fee to amount
return self.apply_fee_conditional(trade, trade_base_currency,
amount=order_amount, fee_abs=fee_cost)
return order_amount
amount=order_amount, fee_abs=fee_cost,
order_obj=order_obj)
return None
return self.fee_detection_from_trades(
trade, order, order_obj, order_amount, order.get('trades', []))
def fee_detection_from_trades(self, trade: Trade, order: Dict, order_obj: Order,
order_amount: float, trades: List) -> float:
order_amount: float, trades: List) -> Optional[float]:
"""
fee-detection fallback to Trades.
Either uses provided trades list or the result of fetch_my_trades to get correct fee.
@ -1853,7 +1890,7 @@ class FreqtradeBot(LoggingMixin):
if len(trades) == 0:
logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade)
return order_amount
return None
fee_currency = None
amount = 0
fee_abs = 0.0
@ -1895,10 +1932,9 @@ class FreqtradeBot(LoggingMixin):
raise DependencyException("Half bought? Amounts don't match")
if fee_abs != 0:
return self.apply_fee_conditional(trade, trade_base_currency,
amount=amount, fee_abs=fee_abs)
else:
return amount
return self.apply_fee_conditional(
trade, trade_base_currency, amount=amount, fee_abs=fee_abs, order_obj=order_obj)
return None
def get_valid_price(self, custom_price: float, proposed_price: float) -> float:
"""

4
freqtrade/loggers.py
View File

@ -2,8 +2,8 @@ import logging
import sys
from logging import Formatter
from logging.handlers import BufferingHandler, RotatingFileHandler, SysLogHandler
from typing import Any, Dict
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
@ -73,7 +73,7 @@ def setup_logging_pre() -> None:
)
def setup_logging(config: Dict[str, Any]) -> None:
def setup_logging(config: Config) -> None:
"""
Process -v/--verbose, --logfile options
"""

2
freqtrade/main.py
View File

@ -12,6 +12,7 @@ from typing import Any, List
if sys.version_info < (3, 8): # pragma: no cover
sys.exit("Freqtrade requires Python version >= 3.8")
from freqtrade import __version__
from freqtrade.commands import Arguments
from freqtrade.exceptions import FreqtradeException, OperationalException
from freqtrade.loggers import setup_logging_pre
@ -34,6 +35,7 @@ def main(sysargv: List[str] = None) -> None:
# Call subcommand.
if 'func' in args:
logger.info(f'freqtrade {__version__}')
return_code = args['func'](args)
else:
# No subcommand was issued.

40
freqtrade/misc.py
View File

@ -10,9 +10,11 @@ from typing import Any, Iterator, List
from typing.io import IO
from urllib.parse import urlparse
import pandas
import rapidjson
from freqtrade.constants import DECIMAL_PER_COIN_FALLBACK, DECIMALS_PER_COIN
from freqtrade.enums import SignalTagType, SignalType
logger = logging.getLogger(__name__)
@ -249,3 +251,41 @@ def parse_db_uri_for_logging(uri: str):
return uri
pwd = parsed_db_uri.netloc.split(':')[1].split('@')[0]
return parsed_db_uri.geturl().replace(f':{pwd}@', ':*****@')
def dataframe_to_json(dataframe: pandas.DataFrame) -> str:
"""
Serialize a DataFrame for transmission over the wire using JSON
:param dataframe: A pandas DataFrame
:returns: A JSON string of the pandas DataFrame
"""
return dataframe.to_json(orient='split')
def json_to_dataframe(data: str) -> pandas.DataFrame:
"""
Deserialize JSON into a DataFrame
:param data: A JSON string
:returns: A pandas DataFrame from the JSON string
"""
dataframe = pandas.read_json(data, orient='split')
if 'date' in dataframe.columns:
dataframe['date'] = pandas.to_datetime(dataframe['date'], unit='ms', utc=True)
return dataframe
def remove_entry_exit_signals(dataframe: pandas.DataFrame):
"""
Remove Entry and Exit signals from a DataFrame
:param dataframe: The DataFrame to remove signals from
"""
dataframe[SignalType.ENTER_LONG.value] = 0
dataframe[SignalType.EXIT_LONG.value] = 0
dataframe[SignalType.ENTER_SHORT.value] = 0
dataframe[SignalType.EXIT_SHORT.value] = 0
dataframe[SignalTagType.ENTER_TAG.value] = None
dataframe[SignalTagType.EXIT_TAG.value] = None
return dataframe

62
freqtrade/optimize/backtesting.py
View File

@ -15,7 +15,7 @@ from pandas import DataFrame
from freqtrade import constants
from freqtrade.configuration import TimeRange, validate_config_consistency
from freqtrade.constants import DATETIME_PRINT_FORMAT, LongShort
from freqtrade.constants import DATETIME_PRINT_FORMAT, Config, LongShort
from freqtrade.data import history
from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe
from freqtrade.data.converter import trim_dataframe, trim_dataframes
@ -70,7 +70,7 @@ class Backtesting:
backtesting.start()
"""
def __init__(self, config: Dict[str, Any]) -> None:
def __init__(self, config: Config) -> None:
LoggingMixin.show_output = False
self.config = config
@ -91,8 +91,8 @@ class Backtesting:
if self.config.get('strategy_list'):
if self.config.get('freqai', {}).get('enabled', False):
raise OperationalException(
"You can't use strategy_list and freqai at the same time.")
logger.warning("Using --strategy-list with FreqAI REQUIRES all strategies "
"to have identical populate_any_indicators.")
for strat in list(self.config['strategy_list']):
stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
@ -113,7 +113,7 @@ class Backtesting:
self.pairlists = PairListManager(self.exchange, self.config)
if 'VolumePairList' in self.pairlists.name_list:
raise OperationalException("VolumePairList not allowed for backtesting. "
"Please use StaticPairlist instead.")
"Please use StaticPairList instead.")
if 'PerformanceFilter' in self.pairlists.name_list:
raise OperationalException("PerformanceFilter not allowed for backtesting.")
@ -139,9 +139,14 @@ class Backtesting:
# Get maximum required startup period
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
if self.config.get('freqai', {}).get('enabled', False):
# For FreqAI, increase the required_startup to includes the training data
self.required_startup = self.dataprovider.get_required_startup(self.timeframe)
# Add maximum startup candle count to configuration for informative pairs support
self.config['startup_candle_count'] = self.required_startup
self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
self.trading_mode: TradingMode = config.get('trading_mode', TradingMode.SPOT)
# strategies which define "can_short=True" will fail to load in Spot mode.
@ -149,9 +154,6 @@ class Backtesting:
self.init_backtest()
def __del__(self):
self.cleanup()
@staticmethod
def cleanup():
LoggingMixin.show_output = True
@ -212,21 +214,12 @@ class Backtesting:
"""
self.progress.init_step(BacktestState.DATALOAD, 1)
if self.config.get('freqai', {}).get('enabled', False):
startup_candles = int(self.config.get('freqai', {}).get('startup_candles', 0))
if not startup_candles:
raise OperationalException('FreqAI backtesting module requires user set '
'startup_candles in config.')
self.required_startup += int(self.config.get('freqai', {}).get('startup_candles', 0))
logger.info(f'Increasing startup_candle_count for freqai to {self.required_startup}')
self.config['startup_candle_count'] = self.required_startup
data = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
timeframe=self.timeframe,
timerange=self.timerange,
startup_candles=self.required_startup,
startup_candles=self.config['startup_candle_count'],
fail_without_data=True,
data_format=self.config.get('dataformat_ohlcv', 'json'),
candle_type=self.config.get('candle_type_def', CandleType.SPOT)
@ -377,10 +370,10 @@ class Backtesting:
for col in HEADERS[5:]:
tag_col = col in ('enter_tag', 'exit_tag')
if col in df_analyzed.columns:
df_analyzed.loc[:, col] = df_analyzed.loc[:, col].replace(
df_analyzed[col] = df_analyzed.loc[:, col].replace(
[nan], [0 if not tag_col else None]).shift(1)
elif not df_analyzed.empty:
df_analyzed.loc[:, col] = 0 if not tag_col else None
df_analyzed[col] = 0 if not tag_col else None
df_analyzed = df_analyzed.drop(df_analyzed.head(1).index)
@ -546,7 +539,11 @@ class Backtesting:
return pos_trade
if stake_amount is not None and stake_amount < 0.0:
amount = abs(stake_amount) / current_rate
amount = amount_to_contract_precision(
abs(stake_amount) / current_rate, trade.amount_precision,
self.precision_mode, trade.contract_size)
if amount == 0.0:
return trade
if amount > trade.amount:
# This is currently ineffective as remaining would become < min tradable
amount = trade.amount
@ -695,7 +692,7 @@ class Backtesting:
self.futures_data[trade.pair],
amount=trade.amount,
is_short=trade.is_short,
open_date=trade.open_date_utc,
open_date=trade.date_last_filled_utc,
close_date=exit_candle_time,
)
@ -817,14 +814,6 @@ class Backtesting:
return trade
time_in_force = self.strategy.order_time_in_force['entry']
if not pos_adjust:
# Confirm trade entry:
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=stake_amount, rate=propose_rate,
time_in_force=time_in_force, current_time=current_time,
entry_tag=entry_tag, side=direction):
return trade
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
self.order_id_counter += 1
base_currency = self.exchange.get_pair_base_currency(pair)
@ -839,6 +828,15 @@ class Backtesting:
# Backcalculate actual stake amount.
stake_amount = amount * propose_rate / leverage
if not pos_adjust:
# Confirm trade entry:
if not strategy_safe_wrapper(
self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=amount, rate=propose_rate,
time_in_force=time_in_force, current_time=current_time,
entry_tag=entry_tag, side=direction):
return trade
is_short = (direction == 'short')
# Necessary for Margin trading. Disabled until support is enabled.
# interest_rate = self.exchange.get_interest_rate()
@ -881,7 +879,7 @@ class Backtesting:
open_rate=propose_rate,
amount=amount,
stake_amount=trade.stake_amount,
leverage=leverage,
wallet_balance=trade.stake_amount,
is_short=is_short,
))

4
freqtrade/optimize/edge_cli.py
View File

@ -4,10 +4,10 @@
This module contains the edge backtesting interface
"""
import logging
from typing import Any, Dict
from freqtrade import constants
from freqtrade.configuration import TimeRange, validate_config_consistency
from freqtrade.constants import Config
from freqtrade.data.dataprovider import DataProvider
from freqtrade.edge import Edge
from freqtrade.optimize.optimize_reports import generate_edge_table
@ -26,7 +26,7 @@ class EdgeCli:
edge.start()
"""
def __init__(self, config: Dict[str, Any]) -> None:
def __init__(self, config: Config) -> None:
self.config = config
# Ensure using dry-run

132
freqtrade/optimize/hyperopt.py
View File

@ -21,7 +21,7 @@ from joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_
from joblib.externals import cloudpickle
from pandas import DataFrame
from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN
from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN, Config
from freqtrade.data.converter import trim_dataframes
from freqtrade.data.history import get_timerange
from freqtrade.enums import HyperoptState
@ -61,12 +61,12 @@ class Hyperopt:
"""
Hyperopt class, this class contains all the logic to run a hyperopt simulation
To run a backtest:
To start a hyperopt run:
hyperopt = Hyperopt(config)
hyperopt.start()
"""
def __init__(self, config: Dict[str, Any]) -> None:
def __init__(self, config: Config) -> None:
self.buy_space: List[Dimension] = []
self.sell_space: List[Dimension] = []
self.protection_space: List[Dimension] = []
@ -132,7 +132,7 @@ class Hyperopt:
self.print_json = self.config.get('print_json', False)
@staticmethod
def get_lock_filename(config: Dict[str, Any]) -> str:
def get_lock_filename(config: Config) -> str:
return str(config['user_data_dir'] / 'hyperopt.lock')
@ -290,7 +290,7 @@ class Hyperopt:
# noinspection PyProtectedMember
attr.value = params_dict[attr_name]
def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
def generate_optimizer(self, raw_params: List[Any]) -> Dict[str, Any]:
"""
Used Optimize function.
Called once per epoch to optimize whatever is configured.
@ -410,9 +410,11 @@ class Hyperopt:
model_queue_size=SKOPT_MODEL_QUEUE_SIZE,
)
def run_optimizer_parallel(self, parallel, asked, i) -> List:
def run_optimizer_parallel(
self, parallel: Parallel, asked: List[List]) -> List[Dict[str, Any]]:
""" Start optimizer in a parallel way """
return parallel(delayed(
wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
wrap_non_picklable_objects(self.generate_optimizer))(v) for v in asked)
def _set_random_state(self, random_state: Optional[int]) -> int:
return random_state or random.randint(1, 2**16 - 1)
@ -421,9 +423,10 @@ class Hyperopt:
preprocessed = self.backtesting.strategy.advise_all_indicators(data)
# Trim startup period from analyzed dataframe to get correct dates for output.
processed = trim_dataframes(preprocessed, self.timerange, self.backtesting.required_startup)
self.min_date, self.max_date = get_timerange(processed)
return processed
trimmed = trim_dataframes(preprocessed, self.timerange, self.backtesting.required_startup)
self.min_date, self.max_date = get_timerange(trimmed)
# Real trimming will happen as part of backtesting.
return preprocessed
def prepare_hyperopt_data(self) -> None:
HyperoptStateContainer.set_state(HyperoptState.DATALOAD)
@ -490,6 +493,53 @@ class Hyperopt:
else:
return self.opt.ask(n_points=n_points), [False for _ in range(n_points)]
def get_progressbar_widgets(self):
if self.print_colorized:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
fill_wrap=Fore.GREEN + '{}' + Fore.RESET,
marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL,
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
else:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
return widgets
def evaluate_result(self, val: Dict[str, Any], current: int, is_random: bool):
"""
Evaluate results returned from generate_optimizer
"""
val['current_epoch'] = current
val['is_initial_point'] = current <= INITIAL_POINTS
logger.debug("Optimizer epoch evaluated: %s", val)
is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
# This value is assigned here and not in the optimization method
# to keep proper order in the list of results. That's because
# evaluations can take different time. Here they are aligned in the
# order they will be shown to the user.
val['is_best'] = is_best
val['is_random'] = is_random
self.print_results(val)
if is_best:
self.current_best_loss = val['loss']
self.current_best_epoch = val
self._save_result(val)
def start(self) -> None:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state'))
logger.info(f"Using optimizer random state: {self.random_state}")
@ -525,64 +575,40 @@ class Hyperopt:
logger.info(f'Effective number of parallel workers used: {jobs}')
# Define progressbar
if self.print_colorized:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
fill_wrap=Fore.GREEN + '{}' + Fore.RESET,
marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL,
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
else:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
widgets = self.get_progressbar_widgets()
with progressbar.ProgressBar(
max_value=self.total_epochs, redirect_stdout=False, redirect_stderr=False,
widgets=widgets
) as pbar:
EVALS = ceil(self.total_epochs / jobs)
for i in range(EVALS):
start = 0
if self.analyze_per_epoch:
# First analysis not in parallel mode when using --analyze-per-epoch.
# This allows dataprovider to load it's informative cache.
asked, is_random = self.get_asked_points(n_points=1)
f_val0 = self.generate_optimizer(asked[0])
self.opt.tell(asked, [f_val0['loss']])
self.evaluate_result(f_val0, 1, is_random[0])
pbar.update(1)
start += 1
evals = ceil((self.total_epochs - start) / jobs)
for i in range(evals):
# Correct the number of epochs to be processed for the last
# iteration (should not exceed self.total_epochs in total)
n_rest = (i + 1) * jobs - self.total_epochs
n_rest = (i + 1) * jobs - (self.total_epochs - start)
current_jobs = jobs - n_rest if n_rest > 0 else jobs
asked, is_random = self.get_asked_points(n_points=current_jobs)
f_val = self.run_optimizer_parallel(parallel, asked, i)
f_val = self.run_optimizer_parallel(parallel, asked)
self.opt.tell(asked, [v['loss'] for v in f_val])
# Calculate progressbar outputs
for j, val in enumerate(f_val):
# Use human-friendly indexes here (starting from 1)
current = i * jobs + j + 1
val['current_epoch'] = current
val['is_initial_point'] = current <= INITIAL_POINTS
current = i * jobs + j + 1 + start
logger.debug(f"Optimizer epoch evaluated: {val}")
is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
# This value is assigned here and not in the optimization method
# to keep proper order in the list of results. That's because
# evaluations can take different time. Here they are aligned in the
# order they will be shown to the user.
val['is_best'] = is_best
val['is_random'] = is_random[j]
self.print_results(val)
if is_best:
self.current_best_loss = val['loss']
self.current_best_epoch = val
self._save_result(val)
self.evaluate_result(val, current, is_random[j])
pbar.update(current)

3
freqtrade/optimize/hyperopt_interface.py
View File

@ -10,6 +10,7 @@ from typing import Dict, List, Union
from sklearn.base import RegressorMixin
from skopt.space import Categorical, Dimension, Integer
from freqtrade.constants import Config
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import round_dict
from freqtrade.optimize.space import SKDecimal
@ -32,7 +33,7 @@ class IHyperOpt(ABC):
timeframe: str
strategy: IStrategy
def __init__(self, config: dict) -> None:
def __init__(self, config: Config) -> None:
self.config = config
# Assign timeframe to be used in hyperopt

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