mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-10 10:21:59 +00:00
Merge branch 'develop' into freqai-pytorch-bugfixes
This commit is contained in:
yinon
commit
8ebfb731d8
4
.github/workflows/ci.yml
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.github/workflows/ci.yml
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|
|
@ -461,7 +461,7 @@ jobs:
|
|||
python setup.py sdist bdist_wheel
|
||||
|
||||
- name: Publish to PyPI (Test)
|
||||
uses: pypa/gh-action-pypi-publish@v1.8.7
|
||||
uses: pypa/gh-action-pypi-publish@v1.8.8
|
||||
if: (github.event_name == 'release')
|
||||
with:
|
||||
user: __token__
|
||||
|
|
@ -469,7 +469,7 @@ jobs:
|
|||
repository_url: https://test.pypi.org/legacy/
|
||||
|
||||
- name: Publish to PyPI
|
||||
uses: pypa/gh-action-pypi-publish@v1.8.7
|
||||
uses: pypa/gh-action-pypi-publish@v1.8.8
|
||||
if: (github.event_name == 'release')
|
||||
with:
|
||||
user: __token__
|
||||
|
|
|
|||
|
|
@ -13,12 +13,12 @@ repos:
|
|||
- id: mypy
|
||||
exclude: build_helpers
|
||||
additional_dependencies:
|
||||
- types-cachetools==5.3.0.5
|
||||
- types-cachetools==5.3.0.6
|
||||
- types-filelock==3.2.7
|
||||
- types-requests==2.31.0.1
|
||||
- types-tabulate==0.9.0.2
|
||||
- types-python-dateutil==2.8.19.13
|
||||
- SQLAlchemy==2.0.18
|
||||
- types-requests==2.31.0.2
|
||||
- types-tabulate==0.9.0.3
|
||||
- types-python-dateutil==2.8.19.14
|
||||
- SQLAlchemy==2.0.19
|
||||
# stages: [push]
|
||||
|
||||
- repo: https://github.com/pycqa/isort
|
||||
|
|
|
|||
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@ -1,21 +1,11 @@
|
|||
# Downloads don't work automatically, since the URL is regenerated via javascript.
|
||||
# Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
|
||||
# vendored Wheels compiled via https://github.com/xmatthias/ta-lib-python/tree/ta_bundled_040
|
||||
|
||||
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-0.4.26-cp38-cp38-win_amd64.whl
|
||||
}
|
||||
if ($pyv -eq '3.9') {
|
||||
pip install build_helpers\TA_Lib-0.4.26-cp39-cp39-win_amd64.whl
|
||||
}
|
||||
if ($pyv -eq '3.10') {
|
||||
pip install build_helpers\TA_Lib-0.4.26-cp310-cp310-win_amd64.whl
|
||||
}
|
||||
if ($pyv -eq '3.11') {
|
||||
pip install build_helpers\TA_Lib-0.4.26-cp311-cp311-win_amd64.whl
|
||||
}
|
||||
|
||||
pip install --find-links=build_helpers\ TA-Lib
|
||||
|
||||
pip install -r requirements-dev.txt
|
||||
pip install -e .
|
||||
|
|
|
|||
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|
|
@ -206,6 +206,6 @@
|
|||
"recursive_strategy_search": false,
|
||||
"add_config_files": [],
|
||||
"reduce_df_footprint": false,
|
||||
"dataformat_ohlcv": "json",
|
||||
"dataformat_trades": "jsongz"
|
||||
"dataformat_ohlcv": "feather",
|
||||
"dataformat_trades": "feather"
|
||||
}
|
||||
|
|
|
|||
|
|
@ -32,5 +32,5 @@ services:
|
|||
--logfile /freqtrade/user_data/logs/freqtrade.log
|
||||
--db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
|
||||
--config /freqtrade/user_data/config.json
|
||||
--freqai-model XGBoostClassifier
|
||||
--strategy SampleStrategy
|
||||
--freqaimodel XGBoostRegressor
|
||||
--strategy FreqaiExampleStrategy
|
||||
|
|
|
|||
|
|
@ -103,6 +103,22 @@ The indicators have to be present in your strategy's main DataFrame (either for
|
|||
timeframe or for informative timeframes) otherwise they will simply be ignored in the script
|
||||
output.
|
||||
|
||||
There are a range of candle and trade-related fields that are included in the analysis so are
|
||||
automatically accessible by including them on the indicator-list, and these include:
|
||||
|
||||
- **open_date :** trade open datetime
|
||||
- **close_date :** trade close datetime
|
||||
- **min_rate :** minimum price seen throughout the position
|
||||
- **max_rate :** maxiumum price seen throughout the position
|
||||
- **open :** signal candle open price
|
||||
- **close :** signal candle close price
|
||||
- **high :** signal candle high price
|
||||
- **low :** signal candle low price
|
||||
- **volume :** signal candle volumne
|
||||
- **profit_ratio :** trade profit ratio
|
||||
- **profit_abs :** absolute profit return of the trade
|
||||
|
||||
|
||||
### Filtering the trade output by date
|
||||
|
||||
To show only trades between dates within your backtested timerange, supply the usual `timerange` option in `YYYYMMDD-[YYYYMMDD]` format:
|
||||
|
|
|
|||
|
|
@ -305,7 +305,7 @@ A backtesting result will look like that:
|
|||
| Sharpe | 2.97 |
|
||||
| Calmar | 6.29 |
|
||||
| Profit factor | 1.11 |
|
||||
| Expectancy | -0.15 |
|
||||
| Expectancy (Ratio) | -0.15 (-0.05) |
|
||||
| Avg. stake amount | 0.001 BTC |
|
||||
| Total trade volume | 0.429 BTC |
|
||||
| | |
|
||||
|
|
@ -324,6 +324,7 @@ A backtesting result will look like that:
|
|||
| Days win/draw/lose | 12 / 82 / 25 |
|
||||
| Avg. Duration Winners | 4:23:00 |
|
||||
| Avg. Duration Loser | 6:55:00 |
|
||||
| Max Consecutive Wins / Loss | 3 / 4 |
|
||||
| Rejected Entry signals | 3089 |
|
||||
| Entry/Exit Timeouts | 0 / 0 |
|
||||
| Canceled Trade Entries | 34 |
|
||||
|
|
@ -409,7 +410,7 @@ It contains some useful key metrics about performance of your strategy on backte
|
|||
| Sharpe | 2.97 |
|
||||
| Calmar | 6.29 |
|
||||
| Profit factor | 1.11 |
|
||||
| Expectancy | -0.15 |
|
||||
| Expectancy (Ratio) | -0.15 (-0.05) |
|
||||
| Avg. stake amount | 0.001 BTC |
|
||||
| Total trade volume | 0.429 BTC |
|
||||
| | |
|
||||
|
|
@ -428,6 +429,7 @@ It contains some useful key metrics about performance of your strategy on backte
|
|||
| Days win/draw/lose | 12 / 82 / 25 |
|
||||
| Avg. Duration Winners | 4:23:00 |
|
||||
| Avg. Duration Loser | 6:55:00 |
|
||||
| Max Consecutive Wins / Loss | 3 / 4 |
|
||||
| Rejected Entry signals | 3089 |
|
||||
| Entry/Exit Timeouts | 0 / 0 |
|
||||
| Canceled Trade Entries | 34 |
|
||||
|
|
@ -467,6 +469,7 @@ It contains some useful key metrics about performance of your strategy on backte
|
|||
- `Best day` / `Worst day`: Best and worst day based on daily profit.
|
||||
- `Days win/draw/lose`: Winning / Losing days (draws are usually days without closed trade).
|
||||
- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
|
||||
- `Max Consecutive Wins / Loss`: Maximum consecutive wins/losses in a row.
|
||||
- `Rejected Entry signals`: Trade entry signals that could not be acted upon due to `max_open_trades` being reached.
|
||||
- `Entry/Exit Timeouts`: Entry/exit orders which did not fill (only applicable if custom pricing is used).
|
||||
- `Canceled Trade Entries`: Number of trades that have been canceled by user request via `adjust_entry_price`.
|
||||
|
|
@ -534,6 +537,7 @@ Since backtesting lacks some detailed information about what happens within a ca
|
|||
- ROI
|
||||
- exits are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the exit will be at 2%)
|
||||
- exits are never "below the candle", so a ROI of 2% may result in a exit at 2.4% if low was at 2.4% profit
|
||||
- ROI entries which came into effect on the triggering candle (e.g. `120: 0.02` for 1h candles, from `60: 0.05`) will use the candle's open as exit rate
|
||||
- Force-exits caused by `<N>=-1` ROI entries use low as exit value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
|
||||
- Stoploss exits happen exactly at stoploss price, even if low was lower, but the loss will be `2 * fees` higher than the stoploss price
|
||||
- Stoploss is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` exit reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes
|
||||
|
|
|
|||
|
|
@ -251,8 +251,8 @@ Mandatory parameters are marked as **Required**, which means that they are requi
|
|||
| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite:///tradesv3.dryrun.sqlite` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> **Datatype:** String, SQLAlchemy connect string
|
||||
| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
|
||||
| `add_config_files` | Additional config files. These files will be loaded and merged with the current config file. The files are resolved relative to the initial file.<br> *Defaults to `[]`*. <br> **Datatype:** List of strings
|
||||
| `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `json`*. <br> **Datatype:** String
|
||||
| `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
|
||||
| `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `feather`*. <br> **Datatype:** String
|
||||
| `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `feather`*. <br> **Datatype:** String
|
||||
| `reduce_df_footprint` | Recast all numeric columns to float32/int32, with the objective of reducing ram/disk usage (and decreasing train/inference timing in FreqAI). (Currently only affects FreqAI use-cases) <br> **Datatype:** Boolean. <br> Default: `False`.
|
||||
|
||||
### Parameters in the strategy
|
||||
|
|
|
|||
|
|
@ -27,11 +27,11 @@ usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
|
|||
[--exchange EXCHANGE]
|
||||
[-t TIMEFRAMES [TIMEFRAMES ...]] [--erase]
|
||||
[--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}]
|
||||
[--data-format-trades {json,jsongz,hdf5}]
|
||||
[--data-format-trades {json,jsongz,hdf5,feather}]
|
||||
[--trading-mode {spot,margin,futures}]
|
||||
[--prepend]
|
||||
|
||||
optional arguments:
|
||||
options:
|
||||
-h, --help show this help message and exit
|
||||
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
|
||||
Limit command to these pairs. Pairs are space-
|
||||
|
|
@ -48,8 +48,7 @@ optional arguments:
|
|||
--dl-trades Download trades instead of OHLCV data. The bot will
|
||||
resample trades to the desired timeframe as specified
|
||||
as --timeframes/-t.
|
||||
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
|
||||
config is provided.
|
||||
--exchange EXCHANGE Exchange name. Only valid if no config is provided.
|
||||
-t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
|
||||
Specify which tickers to download. Space-separated
|
||||
list. Default: `1m 5m`.
|
||||
|
|
@ -57,17 +56,18 @@ optional arguments:
|
|||
exchange/pairs/timeframes.
|
||||
--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}
|
||||
Storage format for downloaded candle (OHLCV) data.
|
||||
(default: `json`).
|
||||
--data-format-trades {json,jsongz,hdf5}
|
||||
(default: `feather`).
|
||||
--data-format-trades {json,jsongz,hdf5,feather}
|
||||
Storage format for downloaded trades data. (default:
|
||||
`jsongz`).
|
||||
`feather`).
|
||||
--trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
|
||||
Select Trading mode
|
||||
--prepend Allow data prepending. (Data-appending is disabled)
|
||||
|
||||
Common arguments:
|
||||
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
|
||||
--logfile FILE Log to the file specified. Special values are:
|
||||
--logfile FILE, --log-file FILE
|
||||
Log to the file specified. Special values are:
|
||||
'syslog', 'journald'. See the documentation for more
|
||||
details.
|
||||
-V, --version show program's version number and exit
|
||||
|
|
@ -157,7 +157,7 @@ Freqtrade currently supports the following data-formats:
|
|||
* `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 (OHLCV only)
|
||||
* `feather` - a dataformat based on Apache Arrow
|
||||
* `parquet` - columnar datastore (OHLCV only)
|
||||
|
||||
By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data.
|
||||
|
|
@ -255,7 +255,7 @@ usage: freqtrade convert-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
|
|||
[--trading-mode {spot,margin,futures}]
|
||||
[--candle-types {spot,futures,mark,index,premiumIndex,funding_rate} [{spot,futures,mark,index,premiumIndex,funding_rate} ...]]
|
||||
|
||||
optional arguments:
|
||||
options:
|
||||
-h, --help show this help message and exit
|
||||
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
|
||||
Limit command to these pairs. Pairs are space-
|
||||
|
|
@ -266,19 +266,20 @@ optional arguments:
|
|||
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.
|
||||
--exchange EXCHANGE Exchange name. Only valid if no config is provided.
|
||||
-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} ...]
|
||||
Select candle type to use
|
||||
Select candle type to convert. Defaults to all
|
||||
available types.
|
||||
|
||||
Common arguments:
|
||||
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
|
||||
--logfile FILE Log to the file specified. Special values are:
|
||||
--logfile FILE, --log-file FILE
|
||||
Log to the file specified. Special values are:
|
||||
'syslog', 'journald'. See the documentation for more
|
||||
details.
|
||||
-V, --version show program's version number and exit
|
||||
|
|
@ -291,7 +292,6 @@ Common arguments:
|
|||
Path to directory with historical backtesting data.
|
||||
--userdir PATH, --user-data-dir PATH
|
||||
Path to userdata directory.
|
||||
|
||||
```
|
||||
|
||||
### Example converting data
|
||||
|
|
@ -314,7 +314,7 @@ usage: freqtrade convert-trade-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
|
|||
{json,jsongz,hdf5,feather,parquet}
|
||||
[--erase] [--exchange EXCHANGE]
|
||||
|
||||
optional arguments:
|
||||
options:
|
||||
-h, --help show this help message and exit
|
||||
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
|
||||
Limit command to these pairs. Pairs are space-
|
||||
|
|
@ -325,12 +325,12 @@ optional arguments:
|
|||
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.
|
||||
--exchange EXCHANGE Exchange name. Only valid if no config is provided.
|
||||
|
||||
Common arguments:
|
||||
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
|
||||
--logfile FILE Log to the file specified. Special values are:
|
||||
--logfile FILE, --log-file FILE
|
||||
Log to the file specified. Special values are:
|
||||
'syslog', 'journald'. See the documentation for more
|
||||
details.
|
||||
-V, --version show program's version number and exit
|
||||
|
|
@ -367,9 +367,9 @@ usage: freqtrade trades-to-ohlcv [-h] [-v] [--logfile FILE] [-V] [-c PATH]
|
|||
[-t TIMEFRAMES [TIMEFRAMES ...]]
|
||||
[--exchange EXCHANGE]
|
||||
[--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}]
|
||||
[--data-format-trades {json,jsongz,hdf5}]
|
||||
[--data-format-trades {json,jsongz,hdf5,feather}]
|
||||
|
||||
optional arguments:
|
||||
options:
|
||||
-h, --help show this help message and exit
|
||||
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
|
||||
Limit command to these pairs. Pairs are space-
|
||||
|
|
@ -377,18 +377,18 @@ optional arguments:
|
|||
-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.
|
||||
--exchange EXCHANGE Exchange name. Only valid if no config is provided.
|
||||
--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}
|
||||
Storage format for downloaded candle (OHLCV) data.
|
||||
(default: `json`).
|
||||
--data-format-trades {json,jsongz,hdf5}
|
||||
(default: `feather`).
|
||||
--data-format-trades {json,jsongz,hdf5,feather}
|
||||
Storage format for downloaded trades data. (default:
|
||||
`jsongz`).
|
||||
`feather`).
|
||||
|
||||
Common arguments:
|
||||
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
|
||||
--logfile FILE Log to the file specified. Special values are:
|
||||
--logfile FILE, --log-file FILE
|
||||
Log to the file specified. Special values are:
|
||||
'syslog', 'journald'. See the documentation for more
|
||||
details.
|
||||
-V, --version show program's version number and exit
|
||||
|
|
@ -422,13 +422,12 @@ usage: freqtrade list-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
|
|||
[--trading-mode {spot,margin,futures}]
|
||||
[--show-timerange]
|
||||
|
||||
optional arguments:
|
||||
options:
|
||||
-h, --help show this help message and exit
|
||||
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
|
||||
config is provided.
|
||||
--exchange EXCHANGE Exchange name. Only valid if no config is provided.
|
||||
--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}
|
||||
Storage format for downloaded candle (OHLCV) data.
|
||||
(default: `json`).
|
||||
(default: `feather`).
|
||||
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
|
||||
Limit command to these pairs. Pairs are space-
|
||||
separated.
|
||||
|
|
@ -439,7 +438,8 @@ optional arguments:
|
|||
|
||||
Common arguments:
|
||||
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
|
||||
--logfile FILE Log to the file specified. Special values are:
|
||||
--logfile FILE, --log-file FILE
|
||||
Log to the file specified. Special values are:
|
||||
'syslog', 'journald'. See the documentation for more
|
||||
details.
|
||||
-V, --version show program's version number and exit
|
||||
|
|
@ -474,7 +474,7 @@ ETH/USDT 5m, 15m, 30m, 1h, 2h, 4h
|
|||
By default, `download-data` sub-command downloads Candles (OHLCV) data. Some exchanges also provide historic trade-data via their API.
|
||||
This data can be useful if you need many different timeframes, since it is only downloaded once, and then resampled locally to the desired timeframes.
|
||||
|
||||
Since this data is large by default, the files use gzip by default. They are stored in your data-directory with the naming convention of `<pair>-trades.json.gz` (`ETH_BTC-trades.json.gz`). Incremental mode is also supported, as for historic OHLCV data, so downloading the data once per week with `--days 8` will create an incremental data-repository.
|
||||
Since this data is large by default, the files use the feather fileformat by default. They are stored in your data-directory with the naming convention of `<pair>-trades.feather` (`ETH_BTC-trades.feather`). Incremental mode is also supported, as for historic OHLCV data, so downloading the data once per week with `--days 8` will create an incremental data-repository.
|
||||
|
||||
To use this mode, simply add `--dl-trades` to your call. This will swap the download method to download trades, and resamples the data locally.
|
||||
|
||||
|
|
|
|||
|
|
@ -259,10 +259,17 @@ The configuration parameter `exchange.unknown_fee_rate` can be used to specify t
|
|||
|
||||
Futures trading on bybit is currently supported for USDT markets, and will use isolated futures mode.
|
||||
Users with unified accounts (there's no way back) can create a Sub-account which will start as "non-unified", and can therefore use isolated futures.
|
||||
On startup, freqtrade will set the position mode to "One-way Mode" for the whole (sub)account. This avoids making this call over and over again (slowing down bot operations), but means that changes to this setting may result in exceptions and errors.
|
||||
On startup, freqtrade will set the position mode to "One-way Mode" for the whole (sub)account. This avoids making this call over and over again (slowing down bot operations), but means that changes to this setting may result in exceptions and errors
|
||||
|
||||
As bybit doesn't provide funding rate history, the dry-run calculation is used for live trades as well.
|
||||
|
||||
API Keys for live futures trading (Subaccount on non-unified) must have the following permissions:
|
||||
* Read-write
|
||||
* Contract - Orders
|
||||
* Contract - Positions
|
||||
|
||||
We do strongly recommend to limit all API keys to the IP you're going to use it from.
|
||||
|
||||
!!! Tip "Stoploss on Exchange"
|
||||
Bybit (futures only) 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, Bybit 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.
|
||||
|
|
|
|||
|
|
@ -261,7 +261,7 @@ class MyFreqaiModel(BaseRegressionModel):
|
|||
"""
|
||||
feature_pipeline = Pipeline([
|
||||
('qt', SKLearnWrapper(QuantileTransformer(output_distribution='normal'))),
|
||||
('di', ds.DissimilarityIndex(di_threshold=1)
|
||||
('di', ds.DissimilarityIndex(di_threshold=1))
|
||||
])
|
||||
|
||||
return feature_pipeline
|
||||
|
|
|
|||
|
|
@ -42,7 +42,6 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
|
|||
| `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).
|
||||
|
|
|
|||
|
|
@ -433,9 +433,14 @@ While this strategy is most likely too simple to provide consistent profit, it s
|
|||
`range` property may also be used with `DecimalParameter` and `CategoricalParameter`. `RealParameter` does not provide this property due to infinite search space.
|
||||
|
||||
??? Hint "Performance tip"
|
||||
During normal hyperopting, indicators are calculated once and supplied to each epoch, linearly increasing RAM usage as a factor of increasing cores. As this also has performance implications, hyperopt provides `--analyze-per-epoch` which will move the execution of `populate_indicators()` to the epoch process, calculating a single value per parameter per epoch instead of using the `.range` functionality. In this case, `.range` functionality will only return the actually used value. This will reduce RAM usage, but increase CPU usage. However, your hyperopting run will be less likely to fail due to Out Of Memory (OOM) issues.
|
||||
During normal hyperopting, indicators are calculated once and supplied to each epoch, linearly increasing RAM usage as a factor of increasing cores. As this also has performance implications, there are two alternatives to reduce RAM usage
|
||||
|
||||
In either case, you should try to use space ranges as small as possible this will improve CPU/RAM usage in both scenarios.
|
||||
* Move `ema_short` and `ema_long` calculations from `populate_indicators()` to `populate_entry_trend()`. Since `populate_entry_trend()` gonna be calculated every epochs, you don't need to use `.range` functionality.
|
||||
* hyperopt provides `--analyze-per-epoch` which will move the execution of `populate_indicators()` to the epoch process, calculating a single value per parameter per epoch instead of using the `.range` functionality. In this case, `.range` functionality will only return the actually used value.
|
||||
|
||||
These alternatives will reduce RAM usage, but increase CPU usage. However, your hyperopting run will be less likely to fail due to Out Of Memory (OOM) issues.
|
||||
|
||||
Whether you are using `.range` functionality or the alternatives above, you should try to use space ranges as small as possible since this will improve CPU/RAM usage.
|
||||
|
||||
|
||||
## Optimizing protections
|
||||
|
|
|
|||
|
|
@ -211,7 +211,7 @@ The user is responsible for providing a server or local file that returns a JSON
|
|||
```json
|
||||
{
|
||||
"pairs": ["XRP/USDT", "ETH/USDT", "LTC/USDT"],
|
||||
"refresh_period": 1800,
|
||||
"refresh_period": 1800
|
||||
}
|
||||
```
|
||||
|
||||
|
|
|
|||
11
docs/includes/showcase.md
Normal file
11
docs/includes/showcase.md
Normal file
|
|
@ -0,0 +1,11 @@
|
|||
This section will highlight a few projects from members of the community.
|
||||
!!! Note
|
||||
The projects below are for the most part not maintained by the freqtrade , therefore use your own caution before using them.
|
||||
|
||||
- [Example freqtrade strategies](https://github.com/freqtrade/freqtrade-strategies/)
|
||||
- [FrequentHippo - Grafana dashboard with dry/live runs and backtests](http://frequenthippo.ddns.net:3000/) (by hippocritical).
|
||||
- [Online pairlist generator](https://remotepairlist.com/) (by Blood4rc).
|
||||
- [Freqtrade Backtesting Project](https://bt.robot.co.network/) (by Blood4rc).
|
||||
- [Freqtrade analysis notebook](https://github.com/froggleston/freqtrade_analysis_notebook) (by Froggleston).
|
||||
- [TUI for freqtrade](https://github.com/froggleston/freqtrade-frogtrade9000) (by Froggleston).
|
||||
- [Bot Academy](https://botacademy.ddns.net/) (by stash86) - Blog about crypto bot projects.
|
||||
|
|
@ -63,6 +63,10 @@ Exchanges confirmed working by the community:
|
|||
- [X] [Bitvavo](https://bitvavo.com/)
|
||||
- [X] [Kucoin](https://www.kucoin.com/)
|
||||
|
||||
## Community showcase
|
||||
|
||||
--8<-- "includes/showcase.md"
|
||||
|
||||
## Requirements
|
||||
|
||||
### Hardware requirements
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
markdown==3.3.7
|
||||
mkdocs==1.4.3
|
||||
mkdocs-material==9.1.18
|
||||
markdown==3.4.4
|
||||
mkdocs==1.5.2
|
||||
mkdocs-material==9.1.21
|
||||
mdx_truly_sane_lists==1.3
|
||||
pymdown-extensions==10.0.1
|
||||
pymdown-extensions==10.1
|
||||
jinja2==3.1.2
|
||||
|
|
|
|||
|
|
@ -750,7 +750,7 @@ class DigDeeperStrategy(IStrategy):
|
|||
# Hope you have a deep wallet!
|
||||
try:
|
||||
# This returns first order stake size
|
||||
stake_amount = filled_entries[0].cost
|
||||
stake_amount = filled_entries[0].stake_amount
|
||||
# This then calculates current safety order size
|
||||
stake_amount = stake_amount * (1 + (count_of_entries * 0.25))
|
||||
return stake_amount
|
||||
|
|
|
|||
|
|
@ -287,12 +287,17 @@ Return a summary of your profit/loss and performance.
|
|||
> **Best Performing:** `PAY/BTC: 50.23%`
|
||||
> **Trading volume:** `0.5 BTC`
|
||||
> **Profit factor:** `1.04`
|
||||
> **Win / Loss:** `102 / 36`
|
||||
> **Winrate:** `73.91%`
|
||||
> **Expectancy (Ratio):** `4.87 (1.66)`
|
||||
> **Max Drawdown:** `9.23% (0.01255 BTC)`
|
||||
|
||||
The relative profit of `1.2%` is the average profit per trade.
|
||||
The relative profit of `15.2 Σ%` is be based on the starting capital - so in this case, the starting capital was `0.00485701 * 1.152 = 0.00738 BTC`.
|
||||
Starting capital is either taken from the `available_capital` setting, or calculated by using current wallet size - profits.
|
||||
Profit Factor is calculated as gross profits / gross losses - and should serve as an overall metric for the strategy.
|
||||
Expectancy corresponds to the average return per currency unit at risk, i.e. the winrate and the risk-reward ratio (the average gain of winning trades compared to the average loss of losing trades).
|
||||
Expectancy Ratio is expected profit or loss of a subsequent trade based on the performance of all past trades.
|
||||
Max drawdown corresponds to the backtesting metric `Absolute Drawdown (Account)` - calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`.
|
||||
Bot started date will refer to the date the bot was first started. For older bots, this will default to the first trade's open date.
|
||||
|
||||
|
|
|
|||
|
|
@ -141,7 +141,8 @@ Most properties here can be None as they are dependant on the exchange response.
|
|||
`amount` | float | Amount in base currency
|
||||
`filled` | float | Filled amount (in base currency)
|
||||
`remaining` | float | Remaining amount
|
||||
`cost` | float | Cost of the order - usually average * filled
|
||||
`cost` | float | Cost of the order - usually average * filled (*Exchange dependant on futures, may contain the cost with or without leverage and may be in contracts.*)
|
||||
`stake_amount` | float | Stake amount used for this order. *Added in 2023.7.*
|
||||
`order_date` | datetime | Order creation date **use `order_date_utc` instead**
|
||||
`order_date_utc` | datetime | Order creation date (in UTC)
|
||||
`order_fill_date` | datetime | Order fill date **use `order_fill_utc` instead**
|
||||
|
|
|
|||
|
|
@ -80,12 +80,18 @@ When using the Form-Encoded or JSON-Encoded configuration you can configure any
|
|||
|
||||
The result would be a POST request with e.g. `Status: running` body and `Content-Type: text/plain` header.
|
||||
|
||||
Optional parameters are available to enable automatic retries for webhook messages. The `webhook.retries` parameter can be set for the maximum number of retries the webhook request should attempt if it is unsuccessful (i.e. HTTP response status is not 200). By default this is set to `0` which is disabled. An additional `webhook.retry_delay` parameter can be set to specify the time in seconds between retry attempts. By default this is set to `0.1` (i.e. 100ms). Note that increasing the number of retries or retry delay may slow down the trader if there are connectivity issues with the webhook. Example configuration for retries:
|
||||
## Additional configurations
|
||||
|
||||
The `webhook.retries` parameter can be set for the maximum number of retries the webhook request should attempt if it is unsuccessful (i.e. HTTP response status is not 200). By default this is set to `0` which is disabled. An additional `webhook.retry_delay` parameter can be set to specify the time in seconds between retry attempts. By default this is set to `0.1` (i.e. 100ms). Note that increasing the number of retries or retry delay may slow down the trader if there are connectivity issues with the webhook.
|
||||
You can also specify `webhook.timeout` - which defines how long the bot will wait until it assumes the other host as unresponsive (defaults to 10s).
|
||||
|
||||
Example configuration for retries:
|
||||
|
||||
```json
|
||||
"webhook": {
|
||||
"enabled": true,
|
||||
"url": "https://<YOURHOOKURL>",
|
||||
"timeout": 10,
|
||||
"retries": 3,
|
||||
"retry_delay": 0.2,
|
||||
"status": {
|
||||
|
|
@ -109,6 +115,8 @@ Custom messages can be sent to Webhook endpoints via the `self.dp.send_msg()` fu
|
|||
|
||||
Different payloads can be configured for different events. Not all fields are necessary, but you should configure at least one of the dicts, otherwise the webhook will never be called.
|
||||
|
||||
## Webhook Message types
|
||||
|
||||
### Entry
|
||||
|
||||
The fields in `webhook.entry` are filled when the bot executes a long/short. Parameters are filled using string.format.
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
""" Freqtrade bot """
|
||||
__version__ = '2023.7.dev'
|
||||
__version__ = '2023.8.dev'
|
||||
|
||||
if 'dev' in __version__:
|
||||
from pathlib import Path
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ from typing import Any, Dict, List
|
|||
|
||||
from questionary import Separator, prompt
|
||||
|
||||
from freqtrade.configuration.detect_environment import running_in_docker
|
||||
from freqtrade.configuration.directory_operations import chown_user_directory
|
||||
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
|
||||
from freqtrade.exceptions import OperationalException
|
||||
|
|
@ -179,7 +180,7 @@ def ask_user_config() -> Dict[str, Any]:
|
|||
"name": "api_server_listen_addr",
|
||||
"message": ("Insert Api server Listen Address (0.0.0.0 for docker, "
|
||||
"otherwise best left untouched)"),
|
||||
"default": "127.0.0.1",
|
||||
"default": "127.0.0.1" if not running_in_docker() else "0.0.0.0",
|
||||
"when": lambda x: x['api_server']
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -435,12 +435,12 @@ AVAILABLE_CLI_OPTIONS = {
|
|||
),
|
||||
"dataformat_ohlcv": Arg(
|
||||
'--data-format-ohlcv',
|
||||
help='Storage format for downloaded candle (OHLCV) data. (default: `json`).',
|
||||
help='Storage format for downloaded candle (OHLCV) data. (default: `feather`).',
|
||||
choices=constants.AVAILABLE_DATAHANDLERS,
|
||||
),
|
||||
"dataformat_trades": Arg(
|
||||
'--data-format-trades',
|
||||
help='Storage format for downloaded trades data. (default: `jsongz`).',
|
||||
help='Storage format for downloaded trades data. (default: `feather`).',
|
||||
choices=constants.AVAILABLE_DATAHANDLERS_TRADES,
|
||||
),
|
||||
"show_timerange": Arg(
|
||||
|
|
|
|||
|
|
@ -3,4 +3,5 @@
|
|||
from freqtrade.configuration.config_setup import setup_utils_configuration
|
||||
from freqtrade.configuration.config_validation import validate_config_consistency
|
||||
from freqtrade.configuration.configuration import Configuration
|
||||
from freqtrade.configuration.detect_environment import running_in_docker
|
||||
from freqtrade.configuration.timerange import TimeRange
|
||||
|
|
|
|||
8
freqtrade/configuration/detect_environment.py
Normal file
8
freqtrade/configuration/detect_environment.py
Normal file
|
|
@ -0,0 +1,8 @@
|
|||
import os
|
||||
|
||||
|
||||
def running_in_docker() -> bool:
|
||||
"""
|
||||
Check if we are running in a docker container
|
||||
"""
|
||||
return os.environ.get('FT_APP_ENV') == 'docker'
|
||||
|
|
@ -3,6 +3,7 @@ import shutil
|
|||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
from freqtrade.configuration.detect_environment import running_in_docker
|
||||
from freqtrade.constants import (USER_DATA_FILES, USERPATH_FREQAIMODELS, USERPATH_HYPEROPTS,
|
||||
USERPATH_NOTEBOOKS, USERPATH_STRATEGIES, Config)
|
||||
from freqtrade.exceptions import OperationalException
|
||||
|
|
@ -30,8 +31,7 @@ def chown_user_directory(directory: Path) -> None:
|
|||
Use Sudo to change permissions of the home-directory if necessary
|
||||
Only applies when running in docker!
|
||||
"""
|
||||
import os
|
||||
if os.environ.get('FT_APP_ENV') == 'docker':
|
||||
if running_in_docker():
|
||||
try:
|
||||
import subprocess
|
||||
subprocess.check_output(
|
||||
|
|
|
|||
|
|
@ -153,7 +153,7 @@ CONF_SCHEMA = {
|
|||
},
|
||||
},
|
||||
'amount_reserve_percent': {'type': 'number', 'minimum': 0.0, 'maximum': 0.5},
|
||||
'stoploss': {'type': 'number', 'maximum': 0, 'exclusiveMaximum': True, 'minimum': -1},
|
||||
'stoploss': {'type': 'number', 'maximum': 0, 'exclusiveMaximum': True},
|
||||
'trailing_stop': {'type': 'boolean'},
|
||||
'trailing_stop_positive': {'type': 'number', 'minimum': 0, 'maximum': 1},
|
||||
'trailing_stop_positive_offset': {'type': 'number', 'minimum': 0, 'maximum': 1},
|
||||
|
|
@ -446,12 +446,12 @@ CONF_SCHEMA = {
|
|||
'dataformat_ohlcv': {
|
||||
'type': 'string',
|
||||
'enum': AVAILABLE_DATAHANDLERS,
|
||||
'default': 'json'
|
||||
'default': 'feather'
|
||||
},
|
||||
'dataformat_trades': {
|
||||
'type': 'string',
|
||||
'enum': AVAILABLE_DATAHANDLERS_TRADES,
|
||||
'default': 'jsongz'
|
||||
'default': 'feather'
|
||||
},
|
||||
'position_adjustment_enable': {'type': 'boolean'},
|
||||
'max_entry_position_adjustment': {'type': ['integer', 'number'], 'minimum': -1},
|
||||
|
|
|
|||
|
|
@ -5,16 +5,17 @@ import logging
|
|||
from copy import copy
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Optional, Union
|
||||
from typing import Any, Dict, List, Literal, Optional, Union
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from freqtrade.constants import LAST_BT_RESULT_FN, IntOrInf
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.misc import json_load
|
||||
from freqtrade.misc import file_dump_json, json_load
|
||||
from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
|
||||
from freqtrade.persistence import LocalTrade, Trade, init_db
|
||||
from freqtrade.types import BacktestHistoryEntryType, BacktestResultType
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
|
@ -128,7 +129,7 @@ def load_backtest_metadata(filename: Union[Path, str]) -> Dict[str, Any]:
|
|||
raise OperationalException('Unexpected error while loading backtest metadata.') from e
|
||||
|
||||
|
||||
def load_backtest_stats(filename: Union[Path, str]) -> Dict[str, Any]:
|
||||
def load_backtest_stats(filename: Union[Path, str]) -> BacktestResultType:
|
||||
"""
|
||||
Load backtest statistics file.
|
||||
:param filename: pathlib.Path object, or string pointing to the file.
|
||||
|
|
@ -147,21 +148,21 @@ def load_backtest_stats(filename: Union[Path, str]) -> Dict[str, Any]:
|
|||
# Legacy list format does not contain metadata.
|
||||
if isinstance(data, dict):
|
||||
data['metadata'] = load_backtest_metadata(filename)
|
||||
|
||||
return data
|
||||
|
||||
|
||||
def load_and_merge_backtest_result(strategy_name: str, filename: Path, results: Dict[str, Any]):
|
||||
"""
|
||||
Load one strategy from multi-strategy result
|
||||
and merge it with results
|
||||
Load one strategy from multi-strategy result and merge it with results
|
||||
:param strategy_name: Name of the strategy contained in the result
|
||||
:param filename: Backtest-result-filename to load
|
||||
:param results: dict to merge the result to.
|
||||
"""
|
||||
bt_data = load_backtest_stats(filename)
|
||||
for k in ('metadata', 'strategy'):
|
||||
k: Literal['metadata', 'strategy']
|
||||
for k in ('metadata', 'strategy'): # type: ignore
|
||||
results[k][strategy_name] = bt_data[k][strategy_name]
|
||||
results['metadata'][strategy_name]['filename'] = filename.stem
|
||||
comparison = bt_data['strategy_comparison']
|
||||
for i in range(len(comparison)):
|
||||
if comparison[i]['key'] == strategy_name:
|
||||
|
|
@ -170,27 +171,67 @@ def load_and_merge_backtest_result(strategy_name: str, filename: Path, results:
|
|||
|
||||
|
||||
def _get_backtest_files(dirname: Path) -> List[Path]:
|
||||
# Weird glob expression here avoids including .meta.json files.
|
||||
return list(reversed(sorted(dirname.glob('backtest-result-*-[0-9][0-9].json'))))
|
||||
|
||||
|
||||
def get_backtest_resultlist(dirname: Path):
|
||||
def get_backtest_result(filename: Path) -> List[BacktestHistoryEntryType]:
|
||||
"""
|
||||
Get backtest result read from metadata file
|
||||
"""
|
||||
return [
|
||||
{
|
||||
'filename': filename.stem,
|
||||
'strategy': s,
|
||||
'notes': v.get('notes', ''),
|
||||
'run_id': v['run_id'],
|
||||
'backtest_start_time': v['backtest_start_time'],
|
||||
} for s, v in load_backtest_metadata(filename).items()
|
||||
]
|
||||
|
||||
|
||||
def get_backtest_resultlist(dirname: Path) -> List[BacktestHistoryEntryType]:
|
||||
"""
|
||||
Get list of backtest results read from metadata files
|
||||
"""
|
||||
results = []
|
||||
for filename in _get_backtest_files(dirname):
|
||||
metadata = load_backtest_metadata(filename)
|
||||
if not metadata:
|
||||
continue
|
||||
for s, v in metadata.items():
|
||||
results.append({
|
||||
'filename': filename.name,
|
||||
'strategy': s,
|
||||
'run_id': v['run_id'],
|
||||
'backtest_start_time': v['backtest_start_time'],
|
||||
return [
|
||||
{
|
||||
'filename': filename.stem,
|
||||
'strategy': s,
|
||||
'run_id': v['run_id'],
|
||||
'notes': v.get('notes', ''),
|
||||
'backtest_start_time': v['backtest_start_time'],
|
||||
}
|
||||
for filename in _get_backtest_files(dirname)
|
||||
for s, v in load_backtest_metadata(filename).items()
|
||||
if v
|
||||
]
|
||||
|
||||
})
|
||||
return results
|
||||
|
||||
def delete_backtest_result(file_abs: Path):
|
||||
"""
|
||||
Delete backtest result file and corresponding metadata file.
|
||||
"""
|
||||
# *.meta.json
|
||||
logger.info(f"Deleting backtest result file: {file_abs.name}")
|
||||
file_abs_meta = file_abs.with_suffix('.meta.json')
|
||||
file_abs.unlink()
|
||||
file_abs_meta.unlink()
|
||||
|
||||
|
||||
def update_backtest_metadata(filename: Path, strategy: str, content: Dict[str, Any]):
|
||||
"""
|
||||
Updates backtest metadata file with new content.
|
||||
:raises: ValueError if metadata file does not exist, or strategy is not in this file.
|
||||
"""
|
||||
metadata = load_backtest_metadata(filename)
|
||||
if not metadata:
|
||||
raise ValueError("File does not exist.")
|
||||
if strategy not in metadata:
|
||||
raise ValueError("Strategy not in metadata.")
|
||||
metadata[strategy].update(content)
|
||||
# Write data again.
|
||||
file_dump_json(get_backtest_metadata_filename(filename), metadata)
|
||||
|
||||
|
||||
def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, str],
|
||||
|
|
@ -211,7 +252,6 @@ def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, s
|
|||
'strategy_comparison': [],
|
||||
}
|
||||
|
||||
# Weird glob expression here avoids including .meta.json files.
|
||||
for filename in _get_backtest_files(dirname):
|
||||
metadata = load_backtest_metadata(filename)
|
||||
if not metadata:
|
||||
|
|
|
|||
|
|
@ -96,8 +96,14 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
|
|||
'volume': 'sum'
|
||||
}
|
||||
timeframe_minutes = timeframe_to_minutes(timeframe)
|
||||
resample_interval = f'{timeframe_minutes}min'
|
||||
if timeframe_minutes >= 43200 and timeframe_minutes < 525600:
|
||||
# Monthly candles need special treatment to stick to the 1st of the month
|
||||
resample_interval = f'{timeframe}S'
|
||||
elif timeframe_minutes > 43200:
|
||||
resample_interval = timeframe
|
||||
# Resample to create "NAN" values
|
||||
df = dataframe.resample(f'{timeframe_minutes}min', on='date').agg(ohlcv_dict)
|
||||
df = dataframe.resample(resample_interval, on='date').agg(ohlcv_dict)
|
||||
|
||||
# Forwardfill close for missing columns
|
||||
df['close'] = df['close'].fillna(method='ffill')
|
||||
|
|
@ -122,7 +128,7 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
|
|||
return df
|
||||
|
||||
|
||||
def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date',
|
||||
def trim_dataframe(df: DataFrame, timerange, *, df_date_col: str = 'date',
|
||||
startup_candles: int = 0) -> DataFrame:
|
||||
"""
|
||||
Trim dataframe based on given timerange
|
||||
|
|
|
|||
|
|
@ -310,7 +310,7 @@ class DataProvider:
|
|||
timeframe=timeframe or self._config['timeframe'],
|
||||
datadir=self._config['datadir'],
|
||||
timerange=timerange,
|
||||
data_format=self._config.get('dataformat_ohlcv', 'json'),
|
||||
data_format=self._config['dataformat_ohlcv'],
|
||||
candle_type=_candle_type,
|
||||
|
||||
)
|
||||
|
|
|
|||
|
|
@ -69,7 +69,7 @@ def load_data(datadir: Path,
|
|||
fill_up_missing: bool = True,
|
||||
startup_candles: int = 0,
|
||||
fail_without_data: bool = False,
|
||||
data_format: str = 'json',
|
||||
data_format: str = 'feather',
|
||||
candle_type: CandleType = CandleType.SPOT,
|
||||
user_futures_funding_rate: Optional[int] = None,
|
||||
) -> Dict[str, DataFrame]:
|
||||
|
|
@ -394,7 +394,7 @@ def _download_trades_history(exchange: Exchange,
|
|||
|
||||
def refresh_backtest_trades_data(exchange: Exchange, pairs: List[str], datadir: Path,
|
||||
timerange: TimeRange, new_pairs_days: int = 30,
|
||||
erase: bool = False, data_format: str = 'jsongz') -> List[str]:
|
||||
erase: bool = False, data_format: str = 'feather') -> List[str]:
|
||||
"""
|
||||
Refresh stored trades data for backtesting and hyperopt operations.
|
||||
Used by freqtrade download-data subcommand.
|
||||
|
|
@ -427,8 +427,8 @@ def convert_trades_to_ohlcv(
|
|||
datadir: Path,
|
||||
timerange: TimeRange,
|
||||
erase: bool = False,
|
||||
data_format_ohlcv: str = 'json',
|
||||
data_format_trades: str = 'jsongz',
|
||||
data_format_ohlcv: str = 'feather',
|
||||
data_format_trades: str = 'feather',
|
||||
candle_type: CandleType = CandleType.SPOT
|
||||
) -> None:
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -427,6 +427,6 @@ def get_datahandler(datadir: Path, data_format: Optional[str] = None,
|
|||
"""
|
||||
|
||||
if not data_handler:
|
||||
HandlerClass = get_datahandlerclass(data_format or 'json')
|
||||
HandlerClass = get_datahandlerclass(data_format or 'feather')
|
||||
data_handler = HandlerClass(datadir)
|
||||
return data_handler
|
||||
|
|
|
|||
|
|
@ -194,32 +194,35 @@ def calculate_cagr(days_passed: int, starting_balance: float, final_balance: flo
|
|||
return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1
|
||||
|
||||
|
||||
def calculate_expectancy(trades: pd.DataFrame) -> float:
|
||||
def calculate_expectancy(trades: pd.DataFrame) -> Tuple[float, float]:
|
||||
"""
|
||||
Calculate expectancy
|
||||
:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
|
||||
:return: expectancy
|
||||
:return: expectancy, expectancy_ratio
|
||||
"""
|
||||
if len(trades) == 0:
|
||||
return 0
|
||||
|
||||
expectancy = 1
|
||||
expectancy = 0
|
||||
expectancy_ratio = 100
|
||||
|
||||
profit_sum = trades.loc[trades['profit_abs'] > 0, 'profit_abs'].sum()
|
||||
loss_sum = abs(trades.loc[trades['profit_abs'] < 0, 'profit_abs'].sum())
|
||||
nb_win_trades = len(trades.loc[trades['profit_abs'] > 0])
|
||||
nb_loss_trades = len(trades.loc[trades['profit_abs'] < 0])
|
||||
if len(trades) > 0:
|
||||
winning_trades = trades.loc[trades['profit_abs'] > 0]
|
||||
losing_trades = trades.loc[trades['profit_abs'] < 0]
|
||||
profit_sum = winning_trades['profit_abs'].sum()
|
||||
loss_sum = abs(losing_trades['profit_abs'].sum())
|
||||
nb_win_trades = len(winning_trades)
|
||||
nb_loss_trades = len(losing_trades)
|
||||
|
||||
if (nb_win_trades > 0) and (nb_loss_trades > 0):
|
||||
average_win = profit_sum / nb_win_trades
|
||||
average_loss = loss_sum / nb_loss_trades
|
||||
risk_reward_ratio = average_win / average_loss
|
||||
winrate = nb_win_trades / len(trades)
|
||||
expectancy = ((1 + risk_reward_ratio) * winrate) - 1
|
||||
elif nb_win_trades == 0:
|
||||
expectancy = 0
|
||||
average_win = (profit_sum / nb_win_trades) if nb_win_trades > 0 else 0
|
||||
average_loss = (loss_sum / nb_loss_trades) if nb_loss_trades > 0 else 0
|
||||
winrate = (nb_win_trades / len(trades))
|
||||
loserate = (nb_loss_trades / len(trades))
|
||||
|
||||
return expectancy
|
||||
expectancy = (winrate * average_win) - (loserate * average_loss)
|
||||
if (average_loss > 0):
|
||||
risk_reward_ratio = average_win / average_loss
|
||||
expectancy_ratio = ((1 + risk_reward_ratio) * winrate) - 1
|
||||
|
||||
return expectancy, expectancy_ratio
|
||||
|
||||
|
||||
def calculate_sortino(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
|
||||
|
|
|
|||
|
|
@ -115,7 +115,7 @@ class Edge:
|
|||
exchange=self.exchange,
|
||||
timeframe=self.strategy.timeframe,
|
||||
timerange=timerange_startup,
|
||||
data_format=self.config.get('dataformat_ohlcv', 'json'),
|
||||
data_format=self.config['dataformat_ohlcv'],
|
||||
candle_type=self.config.get('candle_type_def', CandleType.SPOT),
|
||||
)
|
||||
# Download informative pairs too
|
||||
|
|
@ -132,7 +132,7 @@ class Edge:
|
|||
exchange=self.exchange,
|
||||
timeframe=timeframe,
|
||||
timerange=timerange_startup,
|
||||
data_format=self.config.get('dataformat_ohlcv', 'json'),
|
||||
data_format=self.config['dataformat_ohlcv'],
|
||||
candle_type=self.config.get('candle_type_def', CandleType.SPOT),
|
||||
)
|
||||
|
||||
|
|
@ -142,7 +142,7 @@ class Edge:
|
|||
timeframe=self.strategy.timeframe,
|
||||
timerange=self._timerange,
|
||||
startup_candles=self.strategy.startup_candle_count,
|
||||
data_format=self.config.get('dataformat_ohlcv', 'json'),
|
||||
data_format=self.config['dataformat_ohlcv'],
|
||||
candle_type=self.config.get('candle_type_def', CandleType.SPOT),
|
||||
)
|
||||
|
||||
|
|
@ -172,13 +172,7 @@ class Edge:
|
|||
pair_data = pair_data.sort_values(by=['date'])
|
||||
pair_data = pair_data.reset_index(drop=True)
|
||||
|
||||
df_analyzed = self.strategy.advise_exit(
|
||||
dataframe=self.strategy.advise_entry(
|
||||
dataframe=pair_data,
|
||||
metadata={'pair': pair}
|
||||
),
|
||||
metadata={'pair': pair}
|
||||
)[headers].copy()
|
||||
df_analyzed = self.strategy.ft_advise_signals(pair_data, {'pair': pair})[headers].copy()
|
||||
|
||||
trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)
|
||||
|
||||
|
|
|
|||
|
|
@ -34,6 +34,7 @@ class Binance(Exchange):
|
|||
"tickers_have_price": False,
|
||||
"floor_leverage": True,
|
||||
"stop_price_type_field": "workingType",
|
||||
"order_props_in_contracts": ['amount', 'cost', 'filled', 'remaining'],
|
||||
"stop_price_type_value_mapping": {
|
||||
PriceType.LAST: "CONTRACT_PRICE",
|
||||
PriceType.MARK: "MARK_PRICE",
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -80,9 +80,8 @@ class Exchange:
|
|||
"mark_ohlcv_price": "mark",
|
||||
"mark_ohlcv_timeframe": "8h",
|
||||
"ccxt_futures_name": "swap",
|
||||
"fee_cost_in_contracts": False, # Fee cost needs contract conversion
|
||||
"needs_trading_fees": False, # use fetch_trading_fees to cache fees
|
||||
"order_props_in_contracts": ['amount', 'cost', 'filled', 'remaining'],
|
||||
"order_props_in_contracts": ['amount', 'filled', 'remaining'],
|
||||
# Override createMarketBuyOrderRequiresPrice where ccxt has it wrong
|
||||
"marketOrderRequiresPrice": False,
|
||||
}
|
||||
|
|
@ -1859,9 +1858,6 @@ class Exchange:
|
|||
if fee_curr is None:
|
||||
return None
|
||||
fee_cost = float(fee['cost'])
|
||||
if self._ft_has['fee_cost_in_contracts']:
|
||||
# Convert cost via "contracts" conversion
|
||||
fee_cost = self._contracts_to_amount(symbol, fee['cost'])
|
||||
|
||||
# Calculate fee based on order details
|
||||
if fee_curr == self.get_pair_base_currency(symbol):
|
||||
|
|
|
|||
|
|
@ -33,8 +33,6 @@ class Gate(Exchange):
|
|||
_ft_has_futures: Dict = {
|
||||
"needs_trading_fees": True,
|
||||
"marketOrderRequiresPrice": False,
|
||||
"fee_cost_in_contracts": False, # Set explicitly to false for clarity
|
||||
"order_props_in_contracts": ['amount', 'filled', 'remaining'],
|
||||
"stop_price_type_field": "price_type",
|
||||
"stop_price_type_value_mapping": {
|
||||
PriceType.LAST: 0,
|
||||
|
|
|
|||
|
|
@ -32,7 +32,6 @@ class Okx(Exchange):
|
|||
}
|
||||
_ft_has_futures: Dict = {
|
||||
"tickers_have_quoteVolume": False,
|
||||
"fee_cost_in_contracts": True,
|
||||
"stop_price_type_field": "slTriggerPxType",
|
||||
"stop_price_type_value_mapping": {
|
||||
PriceType.LAST: "last",
|
||||
|
|
|
|||
|
|
@ -635,7 +635,7 @@ class FreqaiDataDrawer:
|
|||
timeframe=tf,
|
||||
pair=pair,
|
||||
timerange=timerange,
|
||||
data_format=self.config.get("dataformat_ohlcv", "json"),
|
||||
data_format=self.config.get("dataformat_ohlcv", "feather"),
|
||||
candle_type=self.config.get("candle_type_def", CandleType.SPOT),
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -86,8 +86,6 @@ class IFreqaiModel(ABC):
|
|||
logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
|
||||
|
||||
self.CONV_WIDTH = self.freqai_info.get('conv_width', 1)
|
||||
if self.ft_params.get("inlier_metric_window", 0):
|
||||
self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
|
||||
self.class_names: List[str] = [] # used in classification subclasses
|
||||
self.pair_it = 0
|
||||
self.pair_it_train = 0
|
||||
|
|
@ -676,15 +674,6 @@ class IFreqaiModel(ABC):
|
|||
hist_preds_df['close_price'] = strat_df['close']
|
||||
hist_preds_df['date_pred'] = strat_df['date']
|
||||
|
||||
# # for keras type models, the conv_window needs to be prepended so
|
||||
# # viewing is correct in frequi
|
||||
if 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)
|
||||
self.dd.historic_predictions[pair] = pd.concat(
|
||||
[zeros_df, hist_preds_df], axis=0, ignore_index=True)
|
||||
|
||||
def fit_live_predictions(self, dk: FreqaiDataKitchen, pair: str) -> None:
|
||||
"""
|
||||
Fit the labels with a gaussian distribution
|
||||
|
|
|
|||
|
|
@ -32,8 +32,8 @@ class LightGBMClassifier(BaseClassifierModel):
|
|||
eval_set = None
|
||||
test_weights = None
|
||||
else:
|
||||
eval_set = (data_dictionary["test_features"].to_numpy(),
|
||||
data_dictionary["test_labels"].to_numpy()[:, 0])
|
||||
eval_set = [(data_dictionary["test_features"].to_numpy(),
|
||||
data_dictionary["test_labels"].to_numpy()[:, 0])]
|
||||
test_weights = data_dictionary["test_weights"]
|
||||
X = data_dictionary["train_features"].to_numpy()
|
||||
y = data_dictionary["train_labels"].to_numpy()[:, 0]
|
||||
|
|
@ -42,7 +42,6 @@ class LightGBMClassifier(BaseClassifierModel):
|
|||
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], init_model=init_model)
|
||||
|
||||
|
|
|
|||
|
|
@ -32,7 +32,7 @@ class LightGBMRegressor(BaseRegressionModel):
|
|||
eval_set = None
|
||||
eval_weights = None
|
||||
else:
|
||||
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
|
||||
eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
|
||||
eval_weights = data_dictionary["test_weights"]
|
||||
X = data_dictionary["train_features"]
|
||||
y = data_dictionary["train_labels"]
|
||||
|
|
|
|||
|
|
@ -42,10 +42,10 @@ class LightGBMRegressorMultiTarget(BaseRegressionModel):
|
|||
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
|
||||
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:
|
||||
|
|
|
|||
|
|
@ -50,7 +50,7 @@ def download_all_data_for_training(dp: DataProvider, config: Config) -> None:
|
|||
timerange=timerange,
|
||||
new_pairs_days=new_pairs_days,
|
||||
erase=False,
|
||||
data_format=config.get("dataformat_ohlcv", "json"),
|
||||
data_format=config.get("dataformat_ohlcv", "feather"),
|
||||
trading_mode=config.get("trading_mode", "spot"),
|
||||
prepend=config.get("prepend_data", False),
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1383,7 +1383,10 @@ class FreqtradeBot(LoggingMixin):
|
|||
latest_candle_close_date = timeframe_to_next_date(self.strategy.timeframe,
|
||||
latest_candle_open_date)
|
||||
# Check if new candle
|
||||
if order_obj and latest_candle_close_date > order_obj.order_date_utc:
|
||||
if (
|
||||
order_obj and order_obj.side == trade.entry_side
|
||||
and latest_candle_close_date > order_obj.order_date_utc
|
||||
):
|
||||
# New candle
|
||||
proposed_rate = self.exchange.get_rate(
|
||||
trade.pair, side='entry', is_short=trade.is_short, refresh=True)
|
||||
|
|
@ -1939,6 +1942,7 @@ class FreqtradeBot(LoggingMixin):
|
|||
"""
|
||||
Applies the fee to amount (either from Order or from Trades).
|
||||
Can eat into dust if more than the required asset is available.
|
||||
In case of trade adjustment orders, trade.amount will not have been adjusted yet.
|
||||
Can't happen in Futures mode - where Fees are always in settlement currency,
|
||||
never in base currency.
|
||||
"""
|
||||
|
|
@ -1948,6 +1952,10 @@ class FreqtradeBot(LoggingMixin):
|
|||
# check against remaining amount!
|
||||
amount_ = trade.amount - amount
|
||||
|
||||
if trade.nr_of_successful_entries >= 1 and order_obj.ft_order_side == trade.entry_side:
|
||||
# In case of rebuy's, trade.amount doesn't contain the amount of the last entry.
|
||||
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 - "
|
||||
|
|
@ -1977,7 +1985,11 @@ class FreqtradeBot(LoggingMixin):
|
|||
# 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':
|
||||
if (
|
||||
trade.fee_updated(order.get('side', ''))
|
||||
or order['status'] == 'open'
|
||||
or order_obj.ft_fee_base
|
||||
):
|
||||
return None
|
||||
|
||||
trade_base_currency = self.exchange.get_pair_base_currency(trade.pair)
|
||||
|
|
|
|||
|
|
@ -116,6 +116,13 @@ def file_load_json(file: Path):
|
|||
return pairdata
|
||||
|
||||
|
||||
def is_file_in_dir(file: Path, directory: Path) -> bool:
|
||||
"""
|
||||
Helper function to check if file is in directory.
|
||||
"""
|
||||
return file.is_file() and file.parent.samefile(directory)
|
||||
|
||||
|
||||
def pair_to_filename(pair: str) -> str:
|
||||
for ch in ['/', ' ', '.', '@', '$', '+', ':']:
|
||||
pair = pair.replace(ch, '_')
|
||||
|
|
|
|||
|
|
@ -39,6 +39,7 @@ from freqtrade.plugins.protectionmanager import ProtectionManager
|
|||
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
|
||||
from freqtrade.strategy.interface import IStrategy
|
||||
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
|
||||
from freqtrade.types import BacktestResultType, get_BacktestResultType_default
|
||||
from freqtrade.util.binance_mig import migrate_binance_futures_data
|
||||
from freqtrade.wallets import Wallets
|
||||
|
||||
|
|
@ -77,7 +78,7 @@ class Backtesting:
|
|||
|
||||
LoggingMixin.show_output = False
|
||||
self.config = config
|
||||
self.results: Dict[str, Any] = {}
|
||||
self.results: BacktestResultType = get_BacktestResultType_default()
|
||||
self.trade_id_counter: int = 0
|
||||
self.order_id_counter: int = 0
|
||||
|
||||
|
|
@ -239,7 +240,7 @@ class Backtesting:
|
|||
timerange=self.timerange,
|
||||
startup_candles=self.config['startup_candle_count'],
|
||||
fail_without_data=True,
|
||||
data_format=self.config.get('dataformat_ohlcv', 'json'),
|
||||
data_format=self.config['dataformat_ohlcv'],
|
||||
candle_type=self.config.get('candle_type_def', CandleType.SPOT)
|
||||
)
|
||||
|
||||
|
|
@ -268,7 +269,7 @@ class Backtesting:
|
|||
timerange=self.timerange,
|
||||
startup_candles=0,
|
||||
fail_without_data=True,
|
||||
data_format=self.config.get('dataformat_ohlcv', 'json'),
|
||||
data_format=self.config['dataformat_ohlcv'],
|
||||
candle_type=self.config.get('candle_type_def', CandleType.SPOT)
|
||||
)
|
||||
else:
|
||||
|
|
@ -282,7 +283,7 @@ class Backtesting:
|
|||
timerange=self.timerange,
|
||||
startup_candles=0,
|
||||
fail_without_data=True,
|
||||
data_format=self.config.get('dataformat_ohlcv', 'json'),
|
||||
data_format=self.config['dataformat_ohlcv'],
|
||||
candle_type=CandleType.FUNDING_RATE
|
||||
)
|
||||
|
||||
|
|
@ -294,7 +295,7 @@ class Backtesting:
|
|||
timerange=self.timerange,
|
||||
startup_candles=0,
|
||||
fail_without_data=True,
|
||||
data_format=self.config.get('dataformat_ohlcv', 'json'),
|
||||
data_format=self.config['dataformat_ohlcv'],
|
||||
candle_type=CandleType.from_string(self.exchange.get_option("mark_ohlcv_price"))
|
||||
)
|
||||
# Combine data to avoid combining the data per trade.
|
||||
|
|
@ -367,11 +368,7 @@ class Backtesting:
|
|||
if not pair_data.empty:
|
||||
# Cleanup from prior runs
|
||||
pair_data.drop(HEADERS[5:] + ['buy', 'sell'], axis=1, errors='ignore')
|
||||
|
||||
df_analyzed = self.strategy.advise_exit(
|
||||
self.strategy.advise_entry(pair_data, {'pair': pair}),
|
||||
{'pair': pair}
|
||||
).copy()
|
||||
df_analyzed = self.strategy.ft_advise_signals(pair_data, {'pair': pair})
|
||||
# Trim startup period from analyzed dataframe
|
||||
df_analyzed = processed[pair] = pair_data = trim_dataframe(
|
||||
df_analyzed, self.timerange, startup_candles=self.required_startup)
|
||||
|
|
@ -679,6 +676,7 @@ class Backtesting:
|
|||
remaining=amount,
|
||||
cost=amount * close_rate,
|
||||
)
|
||||
order._trade_bt = trade
|
||||
trade.orders.append(order)
|
||||
return trade
|
||||
|
||||
|
|
@ -901,8 +899,9 @@ class Backtesting:
|
|||
amount=amount,
|
||||
filled=0,
|
||||
remaining=amount,
|
||||
cost=stake_amount + trade.fee_open,
|
||||
cost=amount * propose_rate + trade.fee_open,
|
||||
)
|
||||
order._trade_bt = trade
|
||||
trade.orders.append(order)
|
||||
if pos_adjust and self._get_order_filled(order.ft_price, row):
|
||||
order.close_bt_order(current_time, trade)
|
||||
|
|
@ -1275,6 +1274,7 @@ class Backtesting:
|
|||
preprocessed = self.strategy.advise_all_indicators(data)
|
||||
|
||||
# Trim startup period from analyzed dataframe
|
||||
# This only used to determine if trimming would result in an empty dataframe
|
||||
preprocessed_tmp = trim_dataframes(preprocessed, timerange, self.required_startup)
|
||||
|
||||
if not preprocessed_tmp:
|
||||
|
|
|
|||
|
|
@ -446,6 +446,8 @@ class Hyperopt:
|
|||
preprocessed = self.backtesting.strategy.advise_all_indicators(data)
|
||||
|
||||
# Trim startup period from analyzed dataframe to get correct dates for output.
|
||||
# This is only used to keep track of min/max date after trimming.
|
||||
# The result is NOT returned from this method, actual trimming happens in backtesting.
|
||||
trimmed = trim_dataframes(preprocessed, self.timerange, self.backtesting.required_startup)
|
||||
self.min_date, self.max_date = get_timerange(trimmed)
|
||||
if not self.market_change:
|
||||
|
|
|
|||
|
|
@ -432,12 +432,10 @@ class HyperoptTools:
|
|||
for i in range(len(trials)):
|
||||
if trials.loc[i]['is_profit']:
|
||||
for j in range(len(trials.loc[i]) - 3):
|
||||
trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
|
||||
str(trials.loc[i][j]), Fore.RESET)
|
||||
trials.iat[i, j] = f"{Fore.GREEN}{str(trials.loc[i][j])}{Fore.RESET}"
|
||||
if trials.loc[i]['is_best'] and highlight_best:
|
||||
for j in range(len(trials.loc[i]) - 3):
|
||||
trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
|
||||
str(trials.loc[i][j]), Style.RESET_ALL)
|
||||
trials.iat[i, j] = f"{Style.BRIGHT}{str(trials.loc[i][j])}{Style.RESET_ALL}"
|
||||
|
||||
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit', 'is_random'])
|
||||
if remove_header > 0:
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
# flake8: noqa: F401
|
||||
from freqtrade.optimize.optimize_reports.bt_output import (generate_edge_table,
|
||||
generate_wins_draws_losses,
|
||||
show_backtest_result,
|
||||
show_backtest_results,
|
||||
show_sorted_pairlist,
|
||||
|
|
@ -14,5 +15,4 @@ from freqtrade.optimize.optimize_reports.optimize_reports import (
|
|||
generate_all_periodic_breakdown_stats, generate_backtest_stats, generate_daily_stats,
|
||||
generate_exit_reason_stats, generate_pair_metrics, generate_periodic_breakdown_stats,
|
||||
generate_rejected_signals, generate_strategy_comparison, generate_strategy_stats,
|
||||
generate_tag_metrics, generate_trade_signal_candles, generate_trading_stats,
|
||||
generate_wins_draws_losses)
|
||||
generate_tag_metrics, generate_trade_signal_candles, generate_trading_stats)
|
||||
|
|
|
|||
|
|
@ -5,8 +5,8 @@ from tabulate import tabulate
|
|||
|
||||
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, Config
|
||||
from freqtrade.misc import decimals_per_coin, round_coin_value
|
||||
from freqtrade.optimize.optimize_reports.optimize_reports import (generate_periodic_breakdown_stats,
|
||||
generate_wins_draws_losses)
|
||||
from freqtrade.optimize.optimize_reports.optimize_reports import generate_periodic_breakdown_stats
|
||||
from freqtrade.types import BacktestResultType
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
|
@ -30,6 +30,16 @@ def _get_line_header(first_column: str, stake_currency: str,
|
|||
'Win Draw Loss Win%']
|
||||
|
||||
|
||||
def generate_wins_draws_losses(wins, draws, losses):
|
||||
if wins > 0 and losses == 0:
|
||||
wl_ratio = '100'
|
||||
elif wins == 0:
|
||||
wl_ratio = '0'
|
||||
else:
|
||||
wl_ratio = f'{100.0 / (wins + draws + losses) * wins:.1f}' if losses > 0 else '100'
|
||||
return f'{wins:>4} {draws:>4} {losses:>4} {wl_ratio:>4}'
|
||||
|
||||
|
||||
def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: str) -> str:
|
||||
"""
|
||||
Generates and returns a text table for the given backtest data and the results dataframe
|
||||
|
|
@ -233,8 +243,9 @@ def text_table_add_metrics(strat_results: Dict) -> str:
|
|||
('Calmar', f"{strat_results['calmar']:.2f}" if 'calmar' in strat_results else 'N/A'),
|
||||
('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor'
|
||||
in strat_results else 'N/A'),
|
||||
('Expectancy', f"{strat_results['expectancy']:.2f}" if 'expectancy'
|
||||
in strat_results else 'N/A'),
|
||||
('Expectancy (Ratio)', (
|
||||
f"{strat_results['expectancy']:.2f} ({strat_results['expectancy_ratio']:.2f})" if
|
||||
'expectancy_ratio' in strat_results else 'N/A')),
|
||||
('Trades per day', strat_results['trades_per_day']),
|
||||
('Avg. daily profit %',
|
||||
f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),
|
||||
|
|
@ -260,6 +271,9 @@ def text_table_add_metrics(strat_results: Dict) -> str:
|
|||
f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
|
||||
('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
|
||||
('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
|
||||
('Max Consecutive Wins / Loss',
|
||||
f"{strat_results['max_consecutive_wins']} / {strat_results['max_consecutive_losses']}"
|
||||
if 'max_consecutive_losses' in strat_results else 'N/A'),
|
||||
('Rejected Entry signals', strat_results.get('rejected_signals', 'N/A')),
|
||||
('Entry/Exit Timeouts',
|
||||
f"{strat_results.get('timedout_entry_orders', 'N/A')} / "
|
||||
|
|
@ -350,7 +364,7 @@ def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency:
|
|||
print()
|
||||
|
||||
|
||||
def show_backtest_results(config: Config, backtest_stats: Dict):
|
||||
def show_backtest_results(config: Config, backtest_stats: BacktestResultType):
|
||||
stake_currency = config['stake_currency']
|
||||
|
||||
for strategy, results in backtest_stats['strategy'].items():
|
||||
|
|
@ -370,7 +384,7 @@ def show_backtest_results(config: Config, backtest_stats: Dict):
|
|||
print('\nFor more details, please look at the detail tables above')
|
||||
|
||||
|
||||
def show_sorted_pairlist(config: Config, backtest_stats: Dict):
|
||||
def show_sorted_pairlist(config: Config, backtest_stats: BacktestResultType):
|
||||
if config.get('backtest_show_pair_list', False):
|
||||
for strategy, results in backtest_stats['strategy'].items():
|
||||
print(f"Pairs for Strategy {strategy}: \n[")
|
||||
|
|
|
|||
|
|
@ -2,18 +2,17 @@ import logging
|
|||
from pathlib import Path
|
||||
from typing import Dict
|
||||
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.constants import LAST_BT_RESULT_FN
|
||||
from freqtrade.misc import file_dump_joblib, file_dump_json
|
||||
from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
|
||||
from freqtrade.types import BacktestResultType
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def store_backtest_stats(
|
||||
recordfilename: Path, stats: Dict[str, DataFrame], dtappendix: str) -> None:
|
||||
recordfilename: Path, stats: BacktestResultType, dtappendix: str) -> Path:
|
||||
"""
|
||||
Stores backtest results
|
||||
:param recordfilename: Path object, which can either be a filename or a directory.
|
||||
|
|
@ -31,13 +30,19 @@ def store_backtest_stats(
|
|||
|
||||
# Store metadata separately.
|
||||
file_dump_json(get_backtest_metadata_filename(filename), stats['metadata'])
|
||||
del stats['metadata']
|
||||
# Don't mutate the original stats dict.
|
||||
stats_copy = {
|
||||
'strategy': stats['strategy'],
|
||||
'strategy_comparison': stats['strategy_comparison'],
|
||||
}
|
||||
|
||||
file_dump_json(filename, stats)
|
||||
file_dump_json(filename, stats_copy)
|
||||
|
||||
latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
|
||||
file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
|
||||
|
||||
return filename
|
||||
|
||||
|
||||
def _store_backtest_analysis_data(
|
||||
recordfilename: Path, data: Dict[str, Dict],
|
||||
|
|
|
|||
|
|
@ -1,15 +1,17 @@
|
|||
import logging
|
||||
from copy import deepcopy
|
||||
from datetime import datetime, timedelta, timezone
|
||||
from typing import Any, Dict, List, Union
|
||||
from typing import Any, Dict, List, Tuple, Union
|
||||
|
||||
from pandas import DataFrame, concat, to_datetime
|
||||
import numpy as np
|
||||
from pandas import DataFrame, Series, concat, to_datetime
|
||||
|
||||
from freqtrade.constants import BACKTEST_BREAKDOWNS, DATETIME_PRINT_FORMAT, IntOrInf
|
||||
from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
|
||||
calculate_expectancy, calculate_market_change,
|
||||
calculate_max_drawdown, calculate_sharpe, calculate_sortino)
|
||||
from freqtrade.misc import decimals_per_coin, round_coin_value
|
||||
from freqtrade.types import BacktestResultType
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
|
@ -57,16 +59,6 @@ def generate_rejected_signals(preprocessed_df: Dict[str, DataFrame],
|
|||
return rejected_candles_only
|
||||
|
||||
|
||||
def generate_wins_draws_losses(wins, draws, losses):
|
||||
if wins > 0 and losses == 0:
|
||||
wl_ratio = '100'
|
||||
elif wins == 0:
|
||||
wl_ratio = '0'
|
||||
else:
|
||||
wl_ratio = f'{100.0 / (wins + draws + losses) * wins:.1f}' if losses > 0 else '100'
|
||||
return f'{wins:>4} {draws:>4} {losses:>4} {wl_ratio:>4}'
|
||||
|
||||
|
||||
def _generate_result_line(result: DataFrame, starting_balance: int, first_column: str) -> Dict:
|
||||
"""
|
||||
Generate one result dict, with "first_column" as key.
|
||||
|
|
@ -97,6 +89,7 @@ def _generate_result_line(result: DataFrame, starting_balance: int, first_column
|
|||
'wins': len(result[result['profit_abs'] > 0]),
|
||||
'draws': len(result[result['profit_abs'] == 0]),
|
||||
'losses': len(result[result['profit_abs'] < 0]),
|
||||
'winrate': len(result[result['profit_abs'] > 0]) / len(result) if len(result) else 0.0,
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -184,6 +177,7 @@ def generate_exit_reason_stats(max_open_trades: IntOrInf, results: DataFrame) ->
|
|||
'wins': len(result[result['profit_abs'] > 0]),
|
||||
'draws': len(result[result['profit_abs'] == 0]),
|
||||
'losses': len(result[result['profit_abs'] < 0]),
|
||||
'winrate': len(result[result['profit_abs'] > 0]) / count if count else 0.0,
|
||||
'profit_mean': profit_mean,
|
||||
'profit_mean_pct': round(profit_mean * 100, 2),
|
||||
'profit_sum': profit_sum,
|
||||
|
|
@ -238,6 +232,7 @@ def generate_periodic_breakdown_stats(trade_list: List, period: str) -> List[Dic
|
|||
wins = sum(day['profit_abs'] > 0)
|
||||
draws = sum(day['profit_abs'] == 0)
|
||||
loses = sum(day['profit_abs'] < 0)
|
||||
trades = (wins + draws + loses)
|
||||
stats.append(
|
||||
{
|
||||
'date': name.strftime('%d/%m/%Y'),
|
||||
|
|
@ -245,7 +240,8 @@ def generate_periodic_breakdown_stats(trade_list: List, period: str) -> List[Dic
|
|||
'profit_abs': profit_abs,
|
||||
'wins': wins,
|
||||
'draws': draws,
|
||||
'loses': loses
|
||||
'loses': loses,
|
||||
'winrate': wins / trades if trades else 0.0,
|
||||
}
|
||||
)
|
||||
return stats
|
||||
|
|
@ -258,6 +254,23 @@ def generate_all_periodic_breakdown_stats(trade_list: List) -> Dict[str, List]:
|
|||
return result
|
||||
|
||||
|
||||
def calc_streak(dataframe: DataFrame) -> Tuple[int, int]:
|
||||
"""
|
||||
Calculate consecutive win and loss streaks
|
||||
:param dataframe: Dataframe containing the trades dataframe, with profit_ratio column
|
||||
:return: Tuple containing consecutive wins and losses
|
||||
"""
|
||||
|
||||
df = Series(np.where(dataframe['profit_ratio'] > 0, 'win', 'loss')).to_frame('result')
|
||||
df['streaks'] = df['result'].ne(df['result'].shift()).cumsum().rename('streaks')
|
||||
df['counter'] = df['streaks'].groupby(df['streaks']).cumcount() + 1
|
||||
res = df.groupby(df['result']).max()
|
||||
#
|
||||
cons_wins = int(res.loc['win', 'counter']) if 'win' in res.index else 0
|
||||
cons_losses = int(res.loc['loss', 'counter']) if 'loss' in res.index else 0
|
||||
return cons_wins, cons_losses
|
||||
|
||||
|
||||
def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
|
||||
""" Generate overall trade statistics """
|
||||
if len(results) == 0:
|
||||
|
|
@ -265,9 +278,12 @@ def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
|
|||
'wins': 0,
|
||||
'losses': 0,
|
||||
'draws': 0,
|
||||
'winrate': 0,
|
||||
'holding_avg': timedelta(),
|
||||
'winner_holding_avg': timedelta(),
|
||||
'loser_holding_avg': timedelta(),
|
||||
'max_consecutive_wins': 0,
|
||||
'max_consecutive_losses': 0,
|
||||
}
|
||||
|
||||
winning_trades = results.loc[results['profit_ratio'] > 0]
|
||||
|
|
@ -280,17 +296,21 @@ def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
|
|||
if not winning_trades.empty else timedelta())
|
||||
loser_holding_avg = (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
|
||||
if not losing_trades.empty else timedelta())
|
||||
winstreak, loss_streak = calc_streak(results)
|
||||
|
||||
return {
|
||||
'wins': len(winning_trades),
|
||||
'losses': len(losing_trades),
|
||||
'draws': len(draw_trades),
|
||||
'winrate': len(winning_trades) / len(results) if len(results) else 0.0,
|
||||
'holding_avg': holding_avg,
|
||||
'holding_avg_s': holding_avg.total_seconds(),
|
||||
'winner_holding_avg': winner_holding_avg,
|
||||
'winner_holding_avg_s': winner_holding_avg.total_seconds(),
|
||||
'loser_holding_avg': loser_holding_avg,
|
||||
'loser_holding_avg_s': loser_holding_avg.total_seconds(),
|
||||
'max_consecutive_wins': winstreak,
|
||||
'max_consecutive_losses': loss_streak,
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -383,6 +403,7 @@ def generate_strategy_stats(pairlist: List[str],
|
|||
losing_profit = results.loc[results['profit_abs'] < 0, 'profit_abs'].sum()
|
||||
profit_factor = winning_profit / abs(losing_profit) if losing_profit else 0.0
|
||||
|
||||
expectancy, expectancy_ratio = calculate_expectancy(results)
|
||||
backtest_days = (max_date - min_date).days or 1
|
||||
strat_stats = {
|
||||
'trades': results.to_dict(orient='records'),
|
||||
|
|
@ -408,7 +429,8 @@ def generate_strategy_stats(pairlist: List[str],
|
|||
'profit_total_long_abs': results.loc[~results['is_short'], 'profit_abs'].sum(),
|
||||
'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(),
|
||||
'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']),
|
||||
'expectancy': calculate_expectancy(results),
|
||||
'expectancy': expectancy,
|
||||
'expectancy_ratio': expectancy_ratio,
|
||||
'sortino': calculate_sortino(results, min_date, max_date, start_balance),
|
||||
'sharpe': calculate_sharpe(results, min_date, max_date, start_balance),
|
||||
'calmar': calculate_calmar(results, min_date, max_date, start_balance),
|
||||
|
|
@ -514,7 +536,7 @@ def generate_strategy_stats(pairlist: List[str],
|
|||
def generate_backtest_stats(btdata: Dict[str, DataFrame],
|
||||
all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
|
||||
min_date: datetime, max_date: datetime
|
||||
) -> Dict[str, Any]:
|
||||
) -> BacktestResultType:
|
||||
"""
|
||||
:param btdata: Backtest data
|
||||
:param all_results: backtest result - dictionary in the form:
|
||||
|
|
@ -523,7 +545,7 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
|
|||
:param max_date: Backtest end date
|
||||
:return: Dictionary containing results per strategy and a strategy summary.
|
||||
"""
|
||||
result: Dict[str, Any] = {
|
||||
result: BacktestResultType = {
|
||||
'metadata': {},
|
||||
'strategy': {},
|
||||
'strategy_comparison': [],
|
||||
|
|
|
|||
|
|
@ -38,6 +38,7 @@ class Order(ModelBase):
|
|||
Mirrors CCXT Order structure
|
||||
"""
|
||||
__tablename__ = 'orders'
|
||||
__allow_unmapped__ = True
|
||||
session: ClassVar[SessionType]
|
||||
|
||||
# Uniqueness should be ensured over pair, order_id
|
||||
|
|
@ -47,7 +48,8 @@ class Order(ModelBase):
|
|||
id: Mapped[int] = mapped_column(Integer, primary_key=True)
|
||||
ft_trade_id: Mapped[int] = mapped_column(Integer, ForeignKey('trades.id'), index=True)
|
||||
|
||||
trade: Mapped["Trade"] = relationship("Trade", back_populates="orders")
|
||||
_trade_live: Mapped["Trade"] = relationship("Trade", back_populates="orders")
|
||||
_trade_bt: "LocalTrade" = None # type: ignore
|
||||
|
||||
# order_side can only be 'buy', 'sell' or 'stoploss'
|
||||
ft_order_side: Mapped[str] = mapped_column(String(25), nullable=False)
|
||||
|
|
@ -119,6 +121,15 @@ class Order(ModelBase):
|
|||
def safe_amount_after_fee(self) -> float:
|
||||
return self.safe_filled - self.safe_fee_base
|
||||
|
||||
@property
|
||||
def trade(self) -> "LocalTrade":
|
||||
return self._trade_bt or self._trade_live
|
||||
|
||||
@property
|
||||
def stake_amount(self) -> float:
|
||||
""" Amount in stake currency used for this order"""
|
||||
return self.safe_amount * self.safe_price / self.trade.leverage
|
||||
|
||||
def __repr__(self):
|
||||
|
||||
return (f"Order(id={self.id}, trade={self.ft_trade_id}, order_id={self.order_id}, "
|
||||
|
|
@ -1299,9 +1310,12 @@ class Trade(ModelBase, LocalTrade):
|
|||
Float(), nullable=True, default=None) # type: ignore
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
from_json = kwargs.pop('__FROM_JSON', None)
|
||||
super().__init__(**kwargs)
|
||||
self.realized_profit = 0
|
||||
self.recalc_open_trade_value()
|
||||
if not from_json:
|
||||
# Skip recalculation when loading from json
|
||||
self.realized_profit = 0
|
||||
self.recalc_open_trade_value()
|
||||
|
||||
@validates('enter_tag', 'exit_reason')
|
||||
def validate_string_len(self, key, value):
|
||||
|
|
@ -1655,6 +1669,7 @@ class Trade(ModelBase, LocalTrade):
|
|||
import rapidjson
|
||||
data = rapidjson.loads(json_str)
|
||||
trade = cls(
|
||||
__FROM_JSON=True,
|
||||
id=data["trade_id"],
|
||||
pair=data["pair"],
|
||||
base_currency=data["base_currency"],
|
||||
|
|
|
|||
|
|
@ -55,7 +55,7 @@ def init_plotscript(config, markets: List, startup_candles: int = 0):
|
|||
timeframe=config['timeframe'],
|
||||
timerange=timerange,
|
||||
startup_candles=startup_candles,
|
||||
data_format=config.get('dataformat_ohlcv', 'json'),
|
||||
data_format=config['dataformat_ohlcv'],
|
||||
candle_type=config.get('candle_type_def', CandleType.SPOT)
|
||||
)
|
||||
|
||||
|
|
@ -84,7 +84,7 @@ def init_plotscript(config, markets: List, startup_candles: int = 0):
|
|||
except ValueError as e:
|
||||
raise OperationalException(e) from e
|
||||
if not trades.empty:
|
||||
trades = trim_dataframe(trades, timerange, 'open_date')
|
||||
trades = trim_dataframe(trades, timerange, df_date_col='open_date')
|
||||
|
||||
return {"ohlcv": data,
|
||||
"trades": trades,
|
||||
|
|
|
|||
|
|
@ -3,15 +3,16 @@ Remote PairList provider
|
|||
|
||||
Provides pair list fetched from a remote source
|
||||
"""
|
||||
import json
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Tuple
|
||||
|
||||
import rapidjson
|
||||
import requests
|
||||
from cachetools import TTLCache
|
||||
|
||||
from freqtrade import __version__
|
||||
from freqtrade.configuration.load_config import CONFIG_PARSE_MODE
|
||||
from freqtrade.constants import Config
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange.types import Tickers
|
||||
|
|
@ -236,7 +237,7 @@ class RemotePairList(IPairList):
|
|||
if file_path.exists():
|
||||
with file_path.open() as json_file:
|
||||
# Load the JSON data into a dictionary
|
||||
jsonparse = json.load(json_file)
|
||||
jsonparse = rapidjson.load(json_file, parse_mode=CONFIG_PARSE_MODE)
|
||||
|
||||
try:
|
||||
pairlist = self.process_json(jsonparse)
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ import asyncio
|
|||
import logging
|
||||
from copy import deepcopy
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List
|
||||
|
||||
from fastapi import APIRouter, BackgroundTasks, Depends
|
||||
|
|
@ -9,16 +10,19 @@ from fastapi.exceptions import HTTPException
|
|||
|
||||
from freqtrade.configuration.config_validation import validate_config_consistency
|
||||
from freqtrade.constants import Config
|
||||
from freqtrade.data.btanalysis import get_backtest_resultlist, load_and_merge_backtest_result
|
||||
from freqtrade.data.btanalysis import (delete_backtest_result, get_backtest_result,
|
||||
get_backtest_resultlist, load_and_merge_backtest_result,
|
||||
update_backtest_metadata)
|
||||
from freqtrade.enums import BacktestState
|
||||
from freqtrade.exceptions import DependencyException, OperationalException
|
||||
from freqtrade.exchange.common import remove_exchange_credentials
|
||||
from freqtrade.misc import deep_merge_dicts
|
||||
from freqtrade.rpc.api_server.api_schemas import (BacktestHistoryEntry, BacktestRequest,
|
||||
BacktestResponse)
|
||||
from freqtrade.misc import deep_merge_dicts, is_file_in_dir
|
||||
from freqtrade.rpc.api_server.api_schemas import (BacktestHistoryEntry, BacktestMetadataUpdate,
|
||||
BacktestRequest, BacktestResponse)
|
||||
from freqtrade.rpc.api_server.deps import get_config
|
||||
from freqtrade.rpc.api_server.webserver_bgwork import ApiBG
|
||||
from freqtrade.rpc.rpc import RPCException
|
||||
from freqtrade.types import get_BacktestResultType_default
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
|
@ -67,14 +71,15 @@ def __run_backtest_bg(btconfig: Config):
|
|||
|
||||
ApiBG.bt['bt'].enable_protections = btconfig.get('enable_protections', False)
|
||||
ApiBG.bt['bt'].strategylist = [strat]
|
||||
ApiBG.bt['bt'].results = {}
|
||||
ApiBG.bt['bt'].results = get_BacktestResultType_default()
|
||||
ApiBG.bt['bt'].load_prior_backtest()
|
||||
|
||||
ApiBG.bt['bt'].abort = False
|
||||
strategy_name = strat.get_strategy_name()
|
||||
if (ApiBG.bt['bt'].results and
|
||||
strat.get_strategy_name() in ApiBG.bt['bt'].results['strategy']):
|
||||
strategy_name in ApiBG.bt['bt'].results['strategy']):
|
||||
# When previous result hash matches - reuse that result and skip backtesting.
|
||||
logger.info(f'Reusing result of previous backtest for {strat.get_strategy_name()}')
|
||||
logger.info(f'Reusing result of previous backtest for {strategy_name}')
|
||||
else:
|
||||
min_date, max_date = ApiBG.bt['bt'].backtest_one_strategy(
|
||||
strat, ApiBG.bt['data'], ApiBG.bt['timerange'])
|
||||
|
|
@ -84,10 +89,12 @@ def __run_backtest_bg(btconfig: Config):
|
|||
min_date=min_date, max_date=max_date)
|
||||
|
||||
if btconfig.get('export', 'none') == 'trades':
|
||||
store_backtest_stats(
|
||||
fn = store_backtest_stats(
|
||||
btconfig['exportfilename'], ApiBG.bt['bt'].results,
|
||||
datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
|
||||
)
|
||||
ApiBG.bt['bt'].results['metadata'][strategy_name]['filename'] = str(fn.name)
|
||||
ApiBG.bt['bt'].results['metadata'][strategy_name]['strategy'] = strategy_name
|
||||
|
||||
logger.info("Backtest finished.")
|
||||
|
||||
|
|
@ -245,13 +252,16 @@ def api_backtest_history(config=Depends(get_config)):
|
|||
tags=['webserver', 'backtest'])
|
||||
def api_backtest_history_result(filename: str, strategy: str, config=Depends(get_config)):
|
||||
# Get backtest result history, read from metadata files
|
||||
fn = config['user_data_dir'] / 'backtest_results' / filename
|
||||
bt_results_base: Path = config['user_data_dir'] / 'backtest_results'
|
||||
fn = (bt_results_base / filename).with_suffix('.json')
|
||||
|
||||
results: Dict[str, Any] = {
|
||||
'metadata': {},
|
||||
'strategy': {},
|
||||
'strategy_comparison': [],
|
||||
}
|
||||
|
||||
if not is_file_in_dir(fn, bt_results_base):
|
||||
raise HTTPException(status_code=404, detail="File not found.")
|
||||
load_and_merge_backtest_result(strategy, fn, results)
|
||||
return {
|
||||
"status": "ended",
|
||||
|
|
@ -261,3 +271,38 @@ def api_backtest_history_result(filename: str, strategy: str, config=Depends(get
|
|||
"status_msg": "Historic result",
|
||||
"backtest_result": results,
|
||||
}
|
||||
|
||||
|
||||
@router.delete('/backtest/history/{file}', response_model=List[BacktestHistoryEntry],
|
||||
tags=['webserver', 'backtest'])
|
||||
def api_delete_backtest_history_entry(file: str, config=Depends(get_config)):
|
||||
# Get backtest result history, read from metadata files
|
||||
bt_results_base: Path = config['user_data_dir'] / 'backtest_results'
|
||||
file_abs = (bt_results_base / file).with_suffix('.json')
|
||||
# Ensure file is in backtest_results directory
|
||||
if not is_file_in_dir(file_abs, bt_results_base):
|
||||
raise HTTPException(status_code=404, detail="File not found.")
|
||||
|
||||
delete_backtest_result(file_abs)
|
||||
return get_backtest_resultlist(config['user_data_dir'] / 'backtest_results')
|
||||
|
||||
|
||||
@router.patch('/backtest/history/{file}', response_model=List[BacktestHistoryEntry],
|
||||
tags=['webserver', 'backtest'])
|
||||
def api_update_backtest_history_entry(file: str, body: BacktestMetadataUpdate,
|
||||
config=Depends(get_config)):
|
||||
# Get backtest result history, read from metadata files
|
||||
bt_results_base: Path = config['user_data_dir'] / 'backtest_results'
|
||||
file_abs = (bt_results_base / file).with_suffix('.json')
|
||||
# Ensure file is in backtest_results directory
|
||||
if not is_file_in_dir(file_abs, bt_results_base):
|
||||
raise HTTPException(status_code=404, detail="File not found.")
|
||||
content = {
|
||||
'notes': body.notes
|
||||
}
|
||||
try:
|
||||
update_backtest_metadata(file_abs, body.strategy, content)
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
|
||||
return get_backtest_result(file_abs)
|
||||
|
|
|
|||
|
|
@ -136,6 +136,9 @@ class Profit(BaseModel):
|
|||
winning_trades: int
|
||||
losing_trades: int
|
||||
profit_factor: float
|
||||
winrate: float
|
||||
expectancy: float
|
||||
expectancy_ratio: float
|
||||
max_drawdown: float
|
||||
max_drawdown_abs: float
|
||||
trading_volume: Optional[float]
|
||||
|
|
@ -517,11 +520,18 @@ class BacktestResponse(BaseModel):
|
|||
backtest_result: Optional[Dict[str, Any]]
|
||||
|
||||
|
||||
# TODO: This is a copy of BacktestHistoryEntryType
|
||||
class BacktestHistoryEntry(BaseModel):
|
||||
filename: str
|
||||
strategy: str
|
||||
run_id: str
|
||||
backtest_start_time: int
|
||||
notes: Optional[str] = ''
|
||||
|
||||
|
||||
class BacktestMetadataUpdate(BaseModel):
|
||||
strategy: str
|
||||
notes: str = ''
|
||||
|
||||
|
||||
class SysInfo(BaseModel):
|
||||
|
|
|
|||
|
|
@ -49,7 +49,9 @@ logger = logging.getLogger(__name__)
|
|||
# 2.28: Switch reload endpoint to Post
|
||||
# 2.29: Add /exchanges endpoint
|
||||
# 2.30: new /pairlists endpoint
|
||||
API_VERSION = 2.30
|
||||
# 2.31: new /backtest/history/ delete endpoint
|
||||
# 2.32: new /backtest/history/ patch endpoint
|
||||
API_VERSION = 2.32
|
||||
|
||||
# Public API, requires no auth.
|
||||
router_public = APIRouter()
|
||||
|
|
@ -268,7 +270,10 @@ def pair_history(pair: str, timeframe: str, timerange: str, strategy: str,
|
|||
'timerange': timerange,
|
||||
'freqaimodel': freqaimodel if freqaimodel else config.get('freqaimodel'),
|
||||
})
|
||||
return RPC._rpc_analysed_history_full(config, pair, timeframe, exchange)
|
||||
try:
|
||||
return RPC._rpc_analysed_history_full(config, pair, timeframe, exchange)
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=502, detail=str(e))
|
||||
|
||||
|
||||
@router.get('/plot_config', response_model=PlotConfig, tags=['candle data'])
|
||||
|
|
@ -283,7 +288,10 @@ def plot_config(strategy: Optional[str] = None, config=Depends(get_config),
|
|||
config1.update({
|
||||
'strategy': strategy
|
||||
})
|
||||
try:
|
||||
return PlotConfig.parse_obj(RPC._rpc_plot_config_with_strategy(config1))
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=502, detail=str(e))
|
||||
|
||||
|
||||
@router.get('/strategies', response_model=StrategyListResponse, tags=['strategy'])
|
||||
|
|
@ -308,7 +316,8 @@ def get_strategy(strategy: str, config=Depends(get_config)):
|
|||
extra_dir=config_.get('strategy_path'))
|
||||
except OperationalException:
|
||||
raise HTTPException(status_code=404, detail='Strategy not found')
|
||||
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=502, detail=str(e))
|
||||
return {
|
||||
'strategy': strategy_obj.get_strategy_name(),
|
||||
'code': strategy_obj.__source__,
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ async def ui_version():
|
|||
}
|
||||
|
||||
|
||||
def is_relative_to(path, base) -> bool:
|
||||
def is_relative_to(path: Path, base: Path) -> bool:
|
||||
# Helper function simulating behaviour of is_relative_to, which was only added in python 3.9
|
||||
try:
|
||||
path.relative_to(base)
|
||||
|
|
|
|||
|
|
@ -8,6 +8,7 @@ from fastapi import Depends, FastAPI
|
|||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from starlette.responses import JSONResponse
|
||||
|
||||
from freqtrade.configuration import running_in_docker
|
||||
from freqtrade.constants import Config
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.rpc.api_server.uvicorn_threaded import UvicornServer
|
||||
|
|
@ -182,7 +183,7 @@ class ApiServer(RPCHandler):
|
|||
rest_port = self._config['api_server']['listen_port']
|
||||
|
||||
logger.info(f'Starting HTTP Server at {rest_ip}:{rest_port}')
|
||||
if not IPv4Address(rest_ip).is_loopback:
|
||||
if not IPv4Address(rest_ip).is_loopback and not running_in_docker():
|
||||
logger.warning("SECURITY WARNING - Local Rest Server listening to external connections")
|
||||
logger.warning("SECURITY WARNING - This is insecure please set to your loopback,"
|
||||
"e.g 127.0.0.1 in config.json")
|
||||
|
|
|
|||
|
|
@ -25,6 +25,7 @@ coingecko_mapping = {
|
|||
'bnb': 'binancecoin',
|
||||
'sol': 'solana',
|
||||
'usdt': 'tether',
|
||||
'busd': 'binance-usd',
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ from freqtrade import __version__
|
|||
from freqtrade.configuration.timerange import TimeRange
|
||||
from freqtrade.constants import CANCEL_REASON, DATETIME_PRINT_FORMAT, Config
|
||||
from freqtrade.data.history import load_data
|
||||
from freqtrade.data.metrics import calculate_max_drawdown
|
||||
from freqtrade.data.metrics import calculate_expectancy, calculate_max_drawdown
|
||||
from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, MarketDirection, SignalDirection,
|
||||
State, TradingMode)
|
||||
from freqtrade.exceptions import ExchangeError, PricingError
|
||||
|
|
@ -494,6 +494,8 @@ class RPC:
|
|||
profit_all_coin.append(profit_abs)
|
||||
profit_all_ratio.append(profit_ratio)
|
||||
|
||||
closed_trade_count = len([t for t in trades if not t.is_open])
|
||||
|
||||
best_pair = Trade.get_best_pair(start_date)
|
||||
trading_volume = Trade.get_trading_volume(start_date)
|
||||
|
||||
|
|
@ -521,9 +523,14 @@ class RPC:
|
|||
|
||||
profit_factor = winning_profit / abs(losing_profit) if losing_profit else float('inf')
|
||||
|
||||
winrate = (winning_trades / closed_trade_count) if closed_trade_count > 0 else 0
|
||||
|
||||
trades_df = DataFrame([{'close_date': trade.close_date.strftime(DATETIME_PRINT_FORMAT),
|
||||
'profit_abs': trade.close_profit_abs}
|
||||
for trade in trades if not trade.is_open and trade.close_date])
|
||||
|
||||
expectancy, expectancy_ratio = calculate_expectancy(trades_df)
|
||||
|
||||
max_drawdown_abs = 0.0
|
||||
max_drawdown = 0.0
|
||||
if len(trades_df) > 0:
|
||||
|
|
@ -562,7 +569,7 @@ class RPC:
|
|||
'profit_all_percent': round(profit_all_ratio_fromstart * 100, 2),
|
||||
'profit_all_fiat': profit_all_fiat,
|
||||
'trade_count': len(trades),
|
||||
'closed_trade_count': len([t for t in trades if not t.is_open]),
|
||||
'closed_trade_count': closed_trade_count,
|
||||
'first_trade_date': first_date.strftime(DATETIME_PRINT_FORMAT) if first_date else '',
|
||||
'first_trade_humanized': dt_humanize(first_date) if first_date else '',
|
||||
'first_trade_timestamp': int(first_date.timestamp() * 1000) if first_date else 0,
|
||||
|
|
@ -576,6 +583,9 @@ class RPC:
|
|||
'winning_trades': winning_trades,
|
||||
'losing_trades': losing_trades,
|
||||
'profit_factor': profit_factor,
|
||||
'winrate': winrate,
|
||||
'expectancy': expectancy,
|
||||
'expectancy_ratio': expectancy_ratio,
|
||||
'max_drawdown': max_drawdown,
|
||||
'max_drawdown_abs': max_drawdown_abs,
|
||||
'trading_volume': trading_volume,
|
||||
|
|
@ -1169,8 +1179,8 @@ class RPC:
|
|||
""" Analyzed dataframe in Dict form """
|
||||
|
||||
_data, last_analyzed = self.__rpc_analysed_dataframe_raw(pair, timeframe, limit)
|
||||
return self._convert_dataframe_to_dict(self._freqtrade.config['strategy'],
|
||||
pair, timeframe, _data, last_analyzed)
|
||||
return RPC._convert_dataframe_to_dict(self._freqtrade.config['strategy'],
|
||||
pair, timeframe, _data, last_analyzed)
|
||||
|
||||
def __rpc_analysed_dataframe_raw(
|
||||
self,
|
||||
|
|
@ -1240,27 +1250,34 @@ class RPC:
|
|||
exchange) -> Dict[str, Any]:
|
||||
timerange_parsed = TimeRange.parse_timerange(config.get('timerange'))
|
||||
|
||||
from freqtrade.data.converter import trim_dataframe
|
||||
from freqtrade.data.dataprovider import DataProvider
|
||||
from freqtrade.resolvers.strategy_resolver import StrategyResolver
|
||||
|
||||
strategy = StrategyResolver.load_strategy(config)
|
||||
startup_candles = strategy.startup_candle_count
|
||||
|
||||
_data = load_data(
|
||||
datadir=config["datadir"],
|
||||
pairs=[pair],
|
||||
timeframe=timeframe,
|
||||
timerange=timerange_parsed,
|
||||
data_format=config.get('dataformat_ohlcv', 'json'),
|
||||
candle_type=config.get('candle_type_def', CandleType.SPOT)
|
||||
data_format=config['dataformat_ohlcv'],
|
||||
candle_type=config.get('candle_type_def', CandleType.SPOT),
|
||||
startup_candles=startup_candles,
|
||||
)
|
||||
if pair not in _data:
|
||||
raise RPCException(
|
||||
f"No data for {pair}, {timeframe} in {config.get('timerange')} found.")
|
||||
from freqtrade.data.dataprovider import DataProvider
|
||||
from freqtrade.resolvers.strategy_resolver import StrategyResolver
|
||||
strategy = StrategyResolver.load_strategy(config)
|
||||
|
||||
strategy.dp = DataProvider(config, exchange=exchange, pairlists=None)
|
||||
strategy.ft_bot_start()
|
||||
|
||||
df_analyzed = strategy.analyze_ticker(_data[pair], {'pair': pair})
|
||||
df_analyzed = trim_dataframe(df_analyzed, timerange_parsed, startup_candles=startup_candles)
|
||||
|
||||
return RPC._convert_dataframe_to_dict(strategy.get_strategy_name(), pair, timeframe,
|
||||
df_analyzed, dt_now())
|
||||
df_analyzed.copy(), dt_now())
|
||||
|
||||
def _rpc_plot_config(self) -> Dict[str, Any]:
|
||||
if (self._freqtrade.strategy.plot_config and
|
||||
|
|
|
|||
|
|
@ -849,6 +849,10 @@ class Telegram(RPCHandler):
|
|||
avg_duration = stats['avg_duration']
|
||||
best_pair = stats['best_pair']
|
||||
best_pair_profit_ratio = stats['best_pair_profit_ratio']
|
||||
winrate = stats['winrate']
|
||||
expectancy = stats['expectancy']
|
||||
expectancy_ratio = stats['expectancy_ratio']
|
||||
|
||||
if stats['trade_count'] == 0:
|
||||
markdown_msg = f"No trades yet.\n*Bot started:* `{stats['bot_start_date']}`"
|
||||
else:
|
||||
|
|
@ -873,7 +877,9 @@ class Telegram(RPCHandler):
|
|||
f"*{'First Trade opened' if not timescale else 'Showing Profit since'}:* "
|
||||
f"`{first_trade_date}`\n"
|
||||
f"*Latest Trade opened:* `{latest_trade_date}`\n"
|
||||
f"*Win / Loss:* `{stats['winning_trades']} / {stats['losing_trades']}`"
|
||||
f"*Win / Loss:* `{stats['winning_trades']} / {stats['losing_trades']}`\n"
|
||||
f"*Winrate:* `{winrate:.2%}`\n"
|
||||
f"*Expectancy (Ratio):* `{expectancy:.2f} ({expectancy_ratio:.2f})`"
|
||||
)
|
||||
if stats['closed_trade_count'] > 0:
|
||||
markdown_msg += (
|
||||
|
|
|
|||
|
|
@ -34,6 +34,7 @@ class Webhook(RPCHandler):
|
|||
self._format = self._config['webhook'].get('format', 'form')
|
||||
self._retries = self._config['webhook'].get('retries', 0)
|
||||
self._retry_delay = self._config['webhook'].get('retry_delay', 0.1)
|
||||
self._timeout = self._config['webhook'].get('timeout', 10)
|
||||
|
||||
def cleanup(self) -> None:
|
||||
"""
|
||||
|
|
@ -107,12 +108,13 @@ class Webhook(RPCHandler):
|
|||
|
||||
try:
|
||||
if self._format == 'form':
|
||||
response = post(self._url, data=payload)
|
||||
response = post(self._url, data=payload, timeout=self._timeout)
|
||||
elif self._format == 'json':
|
||||
response = post(self._url, json=payload)
|
||||
response = post(self._url, json=payload, timeout=self._timeout)
|
||||
elif self._format == 'raw':
|
||||
response = post(self._url, data=payload['data'],
|
||||
headers={'Content-Type': 'text/plain'})
|
||||
headers={'Content-Type': 'text/plain'},
|
||||
timeout=self._timeout)
|
||||
else:
|
||||
raise NotImplementedError(f'Unknown format: {self._format}')
|
||||
|
||||
|
|
|
|||
|
|
@ -825,6 +825,7 @@ class IStrategy(ABC, HyperStrategyMixin):
|
|||
"""
|
||||
Parses the given candle (OHLCV) data and returns a populated DataFrame
|
||||
add several TA indicators and entry order signal to it
|
||||
Should only be used in live.
|
||||
:param dataframe: Dataframe containing data from exchange
|
||||
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
|
||||
:return: DataFrame of candle (OHLCV) data with indicator data and signals added
|
||||
|
|
@ -1321,6 +1322,20 @@ class IStrategy(ABC, HyperStrategyMixin):
|
|||
return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy()
|
||||
for pair, pair_data in data.items()}
|
||||
|
||||
def ft_advise_signals(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Call advise_entry and advise_exit and return the resulting dataframe.
|
||||
:param dataframe: Dataframe containing data from exchange, as well as pre-calculated
|
||||
indicators
|
||||
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
|
||||
:return: DataFrame of candle (OHLCV) data with indicator data and signals added
|
||||
|
||||
"""
|
||||
|
||||
dataframe = self.advise_entry(dataframe, metadata)
|
||||
dataframe = self.advise_exit(dataframe, metadata)
|
||||
return dataframe
|
||||
|
||||
def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Populate indicators that will be used in the Buy, Sell, short, exit_short strategy
|
||||
|
|
|
|||
|
|
@ -1 +1,5 @@
|
|||
from freqtrade.types.valid_exchanges_type import ValidExchangesType # noqa: F401
|
||||
# flake8: noqa: F401
|
||||
from freqtrade.types.backtest_result_type import (BacktestHistoryEntryType, BacktestMetadataType,
|
||||
BacktestResultType,
|
||||
get_BacktestResultType_default)
|
||||
from freqtrade.types.valid_exchanges_type import ValidExchangesType
|
||||
|
|
|
|||
28
freqtrade/types/backtest_result_type.py
Normal file
28
freqtrade/types/backtest_result_type.py
Normal file
|
|
@ -0,0 +1,28 @@
|
|||
from typing import Any, Dict, List
|
||||
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
|
||||
class BacktestMetadataType(TypedDict):
|
||||
run_id: str
|
||||
backtest_start_time: int
|
||||
|
||||
|
||||
class BacktestResultType(TypedDict):
|
||||
metadata: Dict[str, Any] # BacktestMetadataType
|
||||
strategy: Dict[str, Any]
|
||||
strategy_comparison: List[Any]
|
||||
|
||||
|
||||
def get_BacktestResultType_default() -> BacktestResultType:
|
||||
return {
|
||||
'metadata': {},
|
||||
'strategy': {},
|
||||
'strategy_comparison': [],
|
||||
}
|
||||
|
||||
|
||||
class BacktestHistoryEntryType(BacktestMetadataType):
|
||||
filename: str
|
||||
strategy: str
|
||||
notes: str
|
||||
|
|
@ -64,7 +64,7 @@ def migrate_binance_futures_data(config: Config):
|
|||
return
|
||||
|
||||
from freqtrade.data.history.idatahandler import get_datahandler
|
||||
dhc = get_datahandler(config['datadir'], config.get('dataformat_ohlcv', 'json'))
|
||||
dhc = get_datahandler(config['datadir'], config['dataformat_ohlcv'])
|
||||
|
||||
paircombs = dhc.ohlcv_get_available_data(
|
||||
config['datadir'],
|
||||
|
|
|
|||
|
|
@ -84,6 +84,7 @@ class Wallets:
|
|||
tot_profit = Trade.get_total_closed_profit()
|
||||
else:
|
||||
tot_profit = LocalTrade.total_profit
|
||||
tot_profit += sum(trade.realized_profit for trade in open_trades)
|
||||
tot_in_trades = sum(trade.stake_amount for trade in open_trades)
|
||||
used_stake = 0.0
|
||||
|
||||
|
|
|
|||
|
|
@ -7,11 +7,11 @@
|
|||
-r docs/requirements-docs.txt
|
||||
|
||||
coveralls==3.3.1
|
||||
ruff==0.0.277
|
||||
ruff==0.0.282
|
||||
mypy==1.4.1
|
||||
pre-commit==3.3.3
|
||||
pytest==7.4.0
|
||||
pytest-asyncio==0.21.0
|
||||
pytest-asyncio==0.21.1
|
||||
pytest-cov==4.1.0
|
||||
pytest-mock==3.11.1
|
||||
pytest-random-order==1.1.0
|
||||
|
|
@ -20,11 +20,11 @@ isort==5.12.0
|
|||
time-machine==2.11.0
|
||||
|
||||
# Convert jupyter notebooks to markdown documents
|
||||
nbconvert==7.6.0
|
||||
nbconvert==7.7.3
|
||||
|
||||
# mypy types
|
||||
types-cachetools==5.3.0.5
|
||||
types-cachetools==5.3.0.6
|
||||
types-filelock==3.2.7
|
||||
types-requests==2.31.0.1
|
||||
types-tabulate==0.9.0.2
|
||||
types-python-dateutil==2.8.19.13
|
||||
types-requests==2.31.0.2
|
||||
types-tabulate==0.9.0.3
|
||||
types-python-dateutil==2.8.19.14
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@
|
|||
scikit-learn==1.1.3
|
||||
joblib==1.3.1
|
||||
catboost==1.2; 'arm' not in platform_machine
|
||||
lightgbm==3.3.5
|
||||
lightgbm==4.0.0
|
||||
xgboost==1.7.6
|
||||
tensorboard==2.13.0
|
||||
datasieve==0.1.7
|
||||
|
|
|
|||
|
|
@ -1,22 +1,22 @@
|
|||
numpy==1.25.1; python_version > '3.8'
|
||||
numpy==1.25.2; python_version > '3.8'
|
||||
numpy==1.24.3; python_version <= '3.8'
|
||||
pandas==2.0.3
|
||||
pandas-ta==0.3.14b
|
||||
|
||||
ccxt==4.0.17
|
||||
cryptography==41.0.1; platform_machine != 'armv7l'
|
||||
ccxt==4.0.48
|
||||
cryptography==41.0.3; platform_machine != 'armv7l'
|
||||
cryptography==40.0.1; platform_machine == 'armv7l'
|
||||
aiohttp==3.8.4
|
||||
SQLAlchemy==2.0.18
|
||||
aiohttp==3.8.5
|
||||
SQLAlchemy==2.0.19
|
||||
python-telegram-bot==20.4
|
||||
# can't be hard-pinned due to telegram-bot pinning httpx with ~
|
||||
httpx>=0.24.1
|
||||
arrow==1.2.3
|
||||
cachetools==5.3.1
|
||||
requests==2.31.0
|
||||
urllib3==2.0.3
|
||||
jsonschema==4.18.0
|
||||
TA-Lib==0.4.26
|
||||
urllib3==2.0.4
|
||||
jsonschema==4.18.5
|
||||
TA-Lib==0.4.27
|
||||
technical==1.4.0
|
||||
tabulate==0.9.0
|
||||
pycoingecko==3.1.0
|
||||
|
|
@ -24,8 +24,8 @@ jinja2==3.1.2
|
|||
tables==3.8.0
|
||||
blosc==1.11.1
|
||||
joblib==1.3.1
|
||||
rich==13.4.2
|
||||
pyarrow==12.0.0; platform_machine != 'armv7l'
|
||||
rich==13.5.2
|
||||
pyarrow==12.0.1; platform_machine != 'armv7l'
|
||||
|
||||
# find first, C search in arrays
|
||||
py_find_1st==1.1.5
|
||||
|
|
@ -39,10 +39,10 @@ orjson==3.9.2
|
|||
sdnotify==0.3.2
|
||||
|
||||
# API Server
|
||||
fastapi==0.100.0
|
||||
pydantic==1.10.9
|
||||
uvicorn==0.22.0
|
||||
pyjwt==2.7.0
|
||||
fastapi==0.100.1
|
||||
pydantic==1.10.11
|
||||
uvicorn==0.23.2
|
||||
pyjwt==2.8.0
|
||||
aiofiles==23.1.0
|
||||
psutil==5.9.5
|
||||
|
||||
|
|
|
|||
2
setup.py
2
setup.py
|
|
@ -97,7 +97,7 @@ setup(
|
|||
'rich',
|
||||
'pyarrow; platform_machine != "armv7l"',
|
||||
'fastapi',
|
||||
'pydantic>=1.8.0',
|
||||
'pydantic>=1.8.0,<2.0',
|
||||
'uvicorn',
|
||||
'psutil',
|
||||
'pyjwt',
|
||||
|
|
|
|||
|
|
@ -1389,8 +1389,6 @@ def test_convert_data_trades(mocker, testdatadir):
|
|||
def test_start_list_data(testdatadir, capsys):
|
||||
args = [
|
||||
"list-data",
|
||||
"--data-format-ohlcv",
|
||||
"json",
|
||||
"--datadir",
|
||||
str(testdatadir),
|
||||
]
|
||||
|
|
@ -1398,14 +1396,14 @@ def test_start_list_data(testdatadir, capsys):
|
|||
pargs['config'] = None
|
||||
start_list_data(pargs)
|
||||
captured = capsys.readouterr()
|
||||
assert "Found 17 pair / timeframe combinations." in captured.out
|
||||
assert "Found 16 pair / timeframe combinations." in captured.out
|
||||
assert "\n| Pair | Timeframe | Type |\n" in captured.out
|
||||
assert "\n| UNITTEST/BTC | 1m, 5m, 8m, 30m | spot |\n" in captured.out
|
||||
|
||||
args = [
|
||||
"list-data",
|
||||
"--data-format-ohlcv",
|
||||
"json",
|
||||
"feather",
|
||||
"--pairs", "XRP/ETH",
|
||||
"--datadir",
|
||||
str(testdatadir),
|
||||
|
|
@ -1421,8 +1419,6 @@ def test_start_list_data(testdatadir, capsys):
|
|||
|
||||
args = [
|
||||
"list-data",
|
||||
"--data-format-ohlcv",
|
||||
"json",
|
||||
"--trading-mode", "futures",
|
||||
"--datadir",
|
||||
str(testdatadir),
|
||||
|
|
@ -1439,8 +1435,6 @@ def test_start_list_data(testdatadir, capsys):
|
|||
|
||||
args = [
|
||||
"list-data",
|
||||
"--data-format-ohlcv",
|
||||
"json",
|
||||
"--pairs", "XRP/ETH",
|
||||
"--datadir",
|
||||
str(testdatadir),
|
||||
|
|
|
|||
|
|
@ -526,6 +526,7 @@ def get_default_conf(testdatadir):
|
|||
"disableparamexport": True,
|
||||
"internals": {},
|
||||
"export": "none",
|
||||
"dataformat_ohlcv": "feather",
|
||||
"candle_type_def": CandleType.SPOT,
|
||||
}
|
||||
return configuration
|
||||
|
|
@ -3001,85 +3002,85 @@ def mark_ohlcv():
|
|||
def funding_rate_history_hourly():
|
||||
return [
|
||||
{
|
||||
"symbol": "ADA/USDT",
|
||||
"symbol": "ADA/USDT:USDT",
|
||||
"fundingRate": -0.000008,
|
||||
"timestamp": 1630454400000,
|
||||
"datetime": "2021-09-01T00:00:00.000Z"
|
||||
},
|
||||
{
|
||||
"symbol": "ADA/USDT",
|
||||
"symbol": "ADA/USDT:USDT",
|
||||
"fundingRate": -0.000004,
|
||||
"timestamp": 1630458000000,
|
||||
"datetime": "2021-09-01T01:00:00.000Z"
|
||||
},
|
||||
{
|
||||
"symbol": "ADA/USDT",
|
||||
"symbol": "ADA/USDT:USDT",
|
||||
"fundingRate": 0.000012,
|
||||
"timestamp": 1630461600000,
|
||||
"datetime": "2021-09-01T02:00:00.000Z"
|
||||
},
|
||||
{
|
||||
"symbol": "ADA/USDT",
|
||||
"symbol": "ADA/USDT:USDT",
|
||||
"fundingRate": -0.000003,
|
||||
"timestamp": 1630465200000,
|
||||
"datetime": "2021-09-01T03:00:00.000Z"
|
||||
},
|
||||
{
|
||||
"symbol": "ADA/USDT",
|
||||
"symbol": "ADA/USDT:USDT",
|
||||
"fundingRate": -0.000007,
|
||||
"timestamp": 1630468800000,
|
||||
"datetime": "2021-09-01T04:00:00.000Z"
|
||||
},
|
||||
{
|
||||
"symbol": "ADA/USDT",
|
||||
"symbol": "ADA/USDT:USDT",
|
||||
"fundingRate": 0.000003,
|
||||
"timestamp": 1630472400000,
|
||||
"datetime": "2021-09-01T05:00:00.000Z"
|
||||
},
|
||||
{
|
||||
"symbol": "ADA/USDT",
|
||||
"symbol": "ADA/USDT:USDT",
|
||||
"fundingRate": 0.000019,
|
||||
"timestamp": 1630476000000,
|
||||
"datetime": "2021-09-01T06:00:00.000Z"
|
||||
},
|
||||
{
|
||||
"symbol": "ADA/USDT",
|
||||
"symbol": "ADA/USDT:USDT",
|
||||
"fundingRate": 0.000003,
|
||||
"timestamp": 1630479600000,
|
||||
"datetime": "2021-09-01T07:00:00.000Z"
|
||||
},
|
||||
{
|
||||
"symbol": "ADA/USDT",
|
||||
"symbol": "ADA/USDT:USDT",
|
||||
"fundingRate": -0.000003,
|
||||
"timestamp": 1630483200000,
|
||||
"datetime": "2021-09-01T08:00:00.000Z"
|
||||
},
|
||||
{
|
||||
"symbol": "ADA/USDT",
|
||||
"symbol": "ADA/USDT:USDT",
|
||||
"fundingRate": 0,
|
||||
"timestamp": 1630486800000,
|
||||
"datetime": "2021-09-01T09:00:00.000Z"
|
||||
},
|
||||
{
|
||||
"symbol": "ADA/USDT",
|
||||
"symbol": "ADA/USDT:USDT",
|
||||
"fundingRate": 0.000013,
|
||||
"timestamp": 1630490400000,
|
||||
"datetime": "2021-09-01T10:00:00.000Z"
|
||||
},
|
||||
{
|
||||
"symbol": "ADA/USDT",
|
||||
"symbol": "ADA/USDT:USDT",
|
||||
"fundingRate": 0.000077,
|
||||
"timestamp": 1630494000000,
|
||||
"datetime": "2021-09-01T11:00:00.000Z"
|
||||
},
|
||||
{
|
||||
"symbol": "ADA/USDT",
|
||||
"symbol": "ADA/USDT:USDT",
|
||||
"fundingRate": 0.000072,
|
||||
"timestamp": 1630497600000,
|
||||
"datetime": "2021-09-01T12:00:00.000Z"
|
||||
},
|
||||
{
|
||||
"symbol": "ADA/USDT",
|
||||
"symbol": "ADA/USDT:USDT",
|
||||
"fundingRate": 0.000097,
|
||||
"timestamp": 1630501200000,
|
||||
"datetime": "2021-09-01T13:00:00.000Z"
|
||||
|
|
@ -3091,13 +3092,13 @@ def funding_rate_history_hourly():
|
|||
def funding_rate_history_octohourly():
|
||||
return [
|
||||
{
|
||||
"symbol": "ADA/USDT",
|
||||
"symbol": "ADA/USDT:USDT",
|
||||
"fundingRate": -0.000008,
|
||||
"timestamp": 1630454400000,
|
||||
"datetime": "2021-09-01T00:00:00.000Z"
|
||||
},
|
||||
{
|
||||
"symbol": "ADA/USDT",
|
||||
"symbol": "ADA/USDT:USDT",
|
||||
"fundingRate": -0.000003,
|
||||
"timestamp": 1630483200000,
|
||||
"datetime": "2021-09-01T08:00:00.000Z"
|
||||
|
|
|
|||
|
|
@ -343,12 +343,24 @@ def test_calculate_expectancy(testdatadir):
|
|||
filename = testdatadir / "backtest_results/backtest-result.json"
|
||||
bt_data = load_backtest_data(filename)
|
||||
|
||||
expectancy = calculate_expectancy(DataFrame())
|
||||
expectancy, expectancy_ratio = calculate_expectancy(DataFrame())
|
||||
assert expectancy == 0.0
|
||||
assert expectancy_ratio == 100
|
||||
|
||||
expectancy = calculate_expectancy(bt_data)
|
||||
expectancy, expectancy_ratio = calculate_expectancy(bt_data)
|
||||
assert isinstance(expectancy, float)
|
||||
assert pytest.approx(expectancy) == 0.07151374226574791
|
||||
assert isinstance(expectancy_ratio, float)
|
||||
assert pytest.approx(expectancy) == 5.820687070932315e-06
|
||||
assert pytest.approx(expectancy_ratio) == 0.07151374226574791
|
||||
|
||||
data = {
|
||||
'profit_abs': [100, 200, 50, -150, 300, -100, 80, -30]
|
||||
}
|
||||
df = DataFrame(data)
|
||||
expectancy, expectancy_ratio = calculate_expectancy(df)
|
||||
|
||||
assert pytest.approx(expectancy) == 56.25
|
||||
assert pytest.approx(expectancy_ratio) == 0.60267857
|
||||
|
||||
|
||||
def test_calculate_sortino(testdatadir):
|
||||
|
|
|
|||
|
|
@ -135,6 +135,73 @@ def test_ohlcv_fill_up_missing_data2(caplog):
|
|||
f"{len(data)} - after: {len(data2)}.*", caplog)
|
||||
|
||||
|
||||
def test_ohlcv_to_dataframe_1M():
|
||||
|
||||
# Monthly ticks from 2019-09-01 to 2023-07-01
|
||||
ticks = [
|
||||
[1567296000000, 8042.08, 10475.54, 7700.67, 8041.96, 608742.1109999999],
|
||||
[1569888000000, 8285.31, 10408.48, 7172.76, 9150.0, 2439561.887],
|
||||
[1572566400000, 9149.88, 9550.0, 6510.19, 7542.93, 4042674.725],
|
||||
[1575158400000, 7541.08, 7800.0, 6427.0, 7189.0, 4063882.296],
|
||||
[1577836800000, 7189.43, 9599.0, 6863.44, 9364.51, 5165281.358],
|
||||
[1580515200000, 9364.5, 10540.0, 8450.0, 8531.98, 4581788.124],
|
||||
[1583020800000, 8532.5, 9204.0, 3621.81, 6407.1, 10859497.479],
|
||||
[1585699200000, 6407.1, 9479.77, 6140.0, 8624.76, 11276526.968],
|
||||
[1588291200000, 8623.61, 10080.0, 7940.0, 9446.43, 12469561.02],
|
||||
[1590969600000, 9446.49, 10497.25, 8816.4, 9138.87, 6684044.201],
|
||||
[1593561600000, 9138.88, 11488.0, 8900.0, 11343.68, 5709327.926],
|
||||
[1596240000000, 11343.67, 12499.42, 10490.0, 11658.11, 6746487.129],
|
||||
[1598918400000, 11658.11, 12061.07, 9808.58, 10773.0, 6442697.051],
|
||||
[1601510400000, 10773.0, 14140.0, 10371.03, 13783.73, 7404103.004],
|
||||
[1604188800000, 13783.73, 19944.0, 13195.0, 19720.0, 12328272.549],
|
||||
[1606780800000, 19722.09, 29376.7, 17555.0, 28951.68, 10067314.24],
|
||||
[1609459200000, 28948.19, 42125.51, 27800.0, 33126.21, 12408873.079],
|
||||
[1612137600000, 33125.11, 58472.14, 32322.47, 45163.36, 8784474.482],
|
||||
[1614556800000, 45162.64, 61950.0, 44972.49, 58807.24, 9459821.267],
|
||||
[1617235200000, 58810.99, 64986.11, 46930.43, 57684.16, 7895051.389],
|
||||
[1619827200000, 57688.29, 59654.0, 28688.0, 37243.38, 16790964.443],
|
||||
[1622505600000, 37244.36, 41413.0, 28780.01, 35031.39, 23474519.886],
|
||||
[1625097600000, 35031.39, 48168.6, 29242.24, 41448.11, 16932491.175],
|
||||
[1627776000000, 41448.1, 50600.0, 37291.0, 47150.32, 13645800.254],
|
||||
[1630454400000, 47150.32, 52950.0, 39503.58, 43796.57, 10734742.869],
|
||||
[1633046400000, 43799.49, 67150.0, 43260.01, 61348.61, 9111112.847],
|
||||
[1635724800000, 61347.14, 69198.7, 53245.0, 56975.0, 7111424.463],
|
||||
[1638316800000, 56978.06, 59100.0, 40888.89, 46210.56, 8404449.024],
|
||||
[1640995200000, 46210.57, 48000.0, 32853.83, 38439.04, 11047479.277],
|
||||
[1643673600000, 38439.04, 45847.5, 34303.7, 43155.0, 10910339.91],
|
||||
[1646092800000, 43155.0, 48200.0, 37134.0, 45506.0, 10459721.586],
|
||||
[1648771200000, 45505.9, 47448.0, 37550.0, 37614.5, 8463568.862],
|
||||
[1651363200000, 37614.4, 40071.7, 26631.0, 31797.8, 14463715.774],
|
||||
[1654041600000, 31797.9, 31986.1, 17593.2, 19923.5, 20710810.306],
|
||||
[1656633600000, 19923.3, 24700.0, 18780.1, 23290.1, 20582518.513],
|
||||
[1659312000000, 23290.1, 25200.0, 19508.0, 20041.5, 17221921.557],
|
||||
[1661990400000, 20041.4, 22850.0, 18084.3, 19411.7, 21935261.414],
|
||||
[1664582400000, 19411.6, 21088.0, 17917.8, 20482.0, 16625843.584],
|
||||
[1667260800000, 20482.1, 21473.7, 15443.2, 17153.3, 18460614.013],
|
||||
[1669852800000, 17153.4, 18400.0, 16210.0, 16537.6, 9702408.711],
|
||||
[1672531200000, 16537.5, 23962.7, 16488.0, 23119.4, 14732180.645],
|
||||
[1675209600000, 23119.5, 25347.6, 21338.0, 23129.6, 15025197.415],
|
||||
[1677628800000, 23129.7, 29184.8, 19521.6, 28454.9, 23317458.541],
|
||||
[1680307200000, 28454.8, 31059.0, 26919.3, 29223.0, 14654208.219],
|
||||
[1682899200000, 29223.0, 29840.0, 25751.0, 27201.1, 13328157.284],
|
||||
[1685577600000, 27201.1, 31500.0, 24777.0, 30460.2, 14099299.273],
|
||||
[1688169600000, 30460.2, 31850.0, 28830.0, 29338.8, 8760361.377]
|
||||
]
|
||||
|
||||
data = ohlcv_to_dataframe(ticks, '1M', pair="UNITTEST/USDT",
|
||||
fill_missing=False, drop_incomplete=False)
|
||||
assert len(data) == len(ticks)
|
||||
assert data.iloc[0]['date'].strftime('%Y-%m-%d') == '2019-09-01'
|
||||
assert data.iloc[-1]['date'].strftime('%Y-%m-%d') == '2023-07-01'
|
||||
|
||||
# Test with filling missing data
|
||||
data = ohlcv_to_dataframe(ticks, '1M', pair="UNITTEST/USDT",
|
||||
fill_missing=True, drop_incomplete=False)
|
||||
assert len(data) == len(ticks)
|
||||
assert data.iloc[0]['date'].strftime('%Y-%m-%d') == '2019-09-01'
|
||||
assert data.iloc[-1]['date'].strftime('%Y-%m-%d') == '2023-07-01'
|
||||
|
||||
|
||||
def test_ohlcv_drop_incomplete(caplog):
|
||||
timeframe = '1d'
|
||||
ticks = [
|
||||
|
|
@ -304,8 +371,8 @@ def test_convert_ohlcv_format(default_conf, testdatadir, tmpdir, file_base, cand
|
|||
files_temp = []
|
||||
files_new = []
|
||||
for file in file_base:
|
||||
file_orig = testdatadir / f"{prependix}{file}.json"
|
||||
file_temp = tmpdir1 / f"{prependix}{file}.json"
|
||||
file_orig = testdatadir / f"{prependix}{file}.feather"
|
||||
file_temp = tmpdir1 / f"{prependix}{file}.feather"
|
||||
file_new = tmpdir1 / f"{prependix}{file}.json.gz"
|
||||
IDataHandler.create_dir_if_needed(file_temp)
|
||||
copyfile(file_orig, file_temp)
|
||||
|
|
@ -327,7 +394,7 @@ def test_convert_ohlcv_format(default_conf, testdatadir, tmpdir, file_base, cand
|
|||
|
||||
convert_ohlcv_format(
|
||||
default_conf,
|
||||
convert_from='json',
|
||||
convert_from='feather',
|
||||
convert_to='jsongz',
|
||||
erase=False,
|
||||
)
|
||||
|
|
@ -341,7 +408,7 @@ def test_convert_ohlcv_format(default_conf, testdatadir, tmpdir, file_base, cand
|
|||
convert_ohlcv_format(
|
||||
default_conf,
|
||||
convert_from='jsongz',
|
||||
convert_to='json',
|
||||
convert_to='feather',
|
||||
erase=True,
|
||||
)
|
||||
for file in (files_temp):
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ from tests.conftest import log_has, log_has_re
|
|||
|
||||
|
||||
def test_datahandler_ohlcv_get_pairs(testdatadir):
|
||||
pairs = JsonDataHandler.ohlcv_get_pairs(testdatadir, '5m', candle_type=CandleType.SPOT)
|
||||
pairs = FeatherDataHandler.ohlcv_get_pairs(testdatadir, '5m', candle_type=CandleType.SPOT)
|
||||
# Convert to set to avoid failures due to sorting
|
||||
assert set(pairs) == {'UNITTEST/BTC', 'XLM/BTC', 'ETH/BTC', 'TRX/BTC', 'LTC/BTC',
|
||||
'XMR/BTC', 'ZEC/BTC', 'ADA/BTC', 'ETC/BTC', 'NXT/BTC',
|
||||
|
|
@ -32,7 +32,7 @@ def test_datahandler_ohlcv_get_pairs(testdatadir):
|
|||
pairs = HDF5DataHandler.ohlcv_get_pairs(testdatadir, '5m', candle_type=CandleType.SPOT)
|
||||
assert set(pairs) == {'UNITTEST/BTC'}
|
||||
|
||||
pairs = JsonDataHandler.ohlcv_get_pairs(testdatadir, '1h', candle_type=CandleType.MARK)
|
||||
pairs = FeatherDataHandler.ohlcv_get_pairs(testdatadir, '1h', candle_type=CandleType.MARK)
|
||||
assert set(pairs) == {'UNITTEST/USDT:USDT', 'XRP/USDT:USDT'}
|
||||
|
||||
pairs = JsonGzDataHandler.ohlcv_get_pairs(testdatadir, '1h', candle_type=CandleType.FUTURES)
|
||||
|
|
@ -79,7 +79,7 @@ def test_rebuild_pair_from_filename(input, expected):
|
|||
|
||||
|
||||
def test_datahandler_ohlcv_get_available_data(testdatadir):
|
||||
paircombs = JsonDataHandler.ohlcv_get_available_data(testdatadir, TradingMode.SPOT)
|
||||
paircombs = FeatherDataHandler.ohlcv_get_available_data(testdatadir, TradingMode.SPOT)
|
||||
# Convert to set to avoid failures due to sorting
|
||||
assert set(paircombs) == {
|
||||
('UNITTEST/BTC', '5m', CandleType.SPOT),
|
||||
|
|
@ -98,10 +98,9 @@ def test_datahandler_ohlcv_get_available_data(testdatadir):
|
|||
('XRP/ETH', '5m', CandleType.SPOT),
|
||||
('UNITTEST/BTC', '30m', CandleType.SPOT),
|
||||
('UNITTEST/BTC', '8m', CandleType.SPOT),
|
||||
('NOPAIR/XXX', '4m', CandleType.SPOT),
|
||||
}
|
||||
|
||||
paircombs = JsonDataHandler.ohlcv_get_available_data(testdatadir, TradingMode.FUTURES)
|
||||
paircombs = FeatherDataHandler.ohlcv_get_available_data(testdatadir, TradingMode.FUTURES)
|
||||
# Convert to set to avoid failures due to sorting
|
||||
assert set(paircombs) == {
|
||||
('UNITTEST/USDT:USDT', '1h', 'mark'),
|
||||
|
|
@ -140,16 +139,11 @@ def test_jsondatahandler_ohlcv_purge(mocker, testdatadir):
|
|||
|
||||
def test_jsondatahandler_ohlcv_load(testdatadir, caplog):
|
||||
dh = JsonDataHandler(testdatadir)
|
||||
df = dh.ohlcv_load('XRP/ETH', '5m', 'spot')
|
||||
assert len(df) == 712
|
||||
|
||||
df_mark = dh.ohlcv_load('UNITTEST/USDT:USDT', '1h', candle_type="mark")
|
||||
assert len(df_mark) == 100
|
||||
df = dh.ohlcv_load('UNITTEST/BTC', '1m', 'spot')
|
||||
assert len(df) > 0
|
||||
|
||||
df_no_mark = dh.ohlcv_load('UNITTEST/USDT', '1h', 'spot')
|
||||
assert len(df_no_mark) == 0
|
||||
|
||||
# Failure case (empty array)
|
||||
# # Failure case (empty array)
|
||||
df1 = dh.ohlcv_load('NOPAIR/XXX', '4m', 'spot')
|
||||
assert len(df1) == 0
|
||||
assert log_has("Could not load data for NOPAIR/XXX.", caplog)
|
||||
|
|
@ -444,7 +438,7 @@ def test_generic_datahandler_ohlcv_load_and_resave(
|
|||
tmpdir2 = tmpdir1 / 'futures'
|
||||
tmpdir2.mkdir()
|
||||
# Load data from one common file
|
||||
dhbase = get_datahandler(testdatadir, 'json')
|
||||
dhbase = get_datahandler(testdatadir, 'feather')
|
||||
ohlcv = dhbase._ohlcv_load(pair, timeframe, None, candle_type=candle_type)
|
||||
assert isinstance(ohlcv, DataFrame)
|
||||
assert len(ohlcv) > 0
|
||||
|
|
|
|||
|
|
@ -63,9 +63,10 @@ def test_historic_ohlcv(mocker, default_conf, ohlcv_history):
|
|||
|
||||
def test_historic_ohlcv_dataformat(mocker, default_conf, ohlcv_history):
|
||||
hdf5loadmock = MagicMock(return_value=ohlcv_history)
|
||||
jsonloadmock = MagicMock(return_value=ohlcv_history)
|
||||
featherloadmock = MagicMock(return_value=ohlcv_history)
|
||||
mocker.patch("freqtrade.data.history.hdf5datahandler.HDF5DataHandler._ohlcv_load", hdf5loadmock)
|
||||
mocker.patch("freqtrade.data.history.jsondatahandler.JsonDataHandler._ohlcv_load", jsonloadmock)
|
||||
mocker.patch("freqtrade.data.history.featherdatahandler.FeatherDataHandler._ohlcv_load",
|
||||
featherloadmock)
|
||||
|
||||
default_conf["runmode"] = RunMode.BACKTEST
|
||||
exchange = get_patched_exchange(mocker, default_conf)
|
||||
|
|
@ -73,17 +74,17 @@ def test_historic_ohlcv_dataformat(mocker, default_conf, ohlcv_history):
|
|||
data = dp.historic_ohlcv("UNITTEST/BTC", "5m")
|
||||
assert isinstance(data, DataFrame)
|
||||
hdf5loadmock.assert_not_called()
|
||||
jsonloadmock.assert_called_once()
|
||||
featherloadmock.assert_called_once()
|
||||
|
||||
# Switching to dataformat hdf5
|
||||
hdf5loadmock.reset_mock()
|
||||
jsonloadmock.reset_mock()
|
||||
featherloadmock.reset_mock()
|
||||
default_conf["dataformat_ohlcv"] = "hdf5"
|
||||
dp = DataProvider(default_conf, exchange)
|
||||
data = dp.historic_ohlcv("UNITTEST/BTC", "5m")
|
||||
assert isinstance(data, DataFrame)
|
||||
hdf5loadmock.assert_called_once()
|
||||
jsonloadmock.assert_not_called()
|
||||
featherloadmock.assert_not_called()
|
||||
|
||||
|
||||
@pytest.mark.parametrize('candle_type', [
|
||||
|
|
|
|||
|
|
@ -68,7 +68,7 @@ def test_load_data_7min_timeframe(caplog, testdatadir) -> None:
|
|||
|
||||
def test_load_data_1min_timeframe(ohlcv_history, mocker, caplog, testdatadir) -> None:
|
||||
mocker.patch(f'{EXMS}.get_historic_ohlcv', return_value=ohlcv_history)
|
||||
file = testdatadir / 'UNITTEST_BTC-1m.json'
|
||||
file = testdatadir / 'UNITTEST_BTC-1m.feather'
|
||||
load_data(datadir=testdatadir, timeframe='1m', pairs=['UNITTEST/BTC'])
|
||||
assert file.is_file()
|
||||
assert not log_has(
|
||||
|
|
@ -79,7 +79,7 @@ def test_load_data_1min_timeframe(ohlcv_history, mocker, caplog, testdatadir) ->
|
|||
|
||||
def test_load_data_mark(ohlcv_history, mocker, caplog, testdatadir) -> None:
|
||||
mocker.patch(f'{EXMS}.get_historic_ohlcv', return_value=ohlcv_history)
|
||||
file = testdatadir / 'futures/UNITTEST_USDT_USDT-1h-mark.json'
|
||||
file = testdatadir / 'futures/UNITTEST_USDT_USDT-1h-mark.feather'
|
||||
load_data(datadir=testdatadir, timeframe='1h', pairs=['UNITTEST/BTC'], candle_type='mark')
|
||||
assert file.is_file()
|
||||
assert not log_has(
|
||||
|
|
@ -90,7 +90,7 @@ def test_load_data_mark(ohlcv_history, mocker, caplog, testdatadir) -> None:
|
|||
|
||||
def test_load_data_startup_candles(mocker, testdatadir) -> None:
|
||||
ltfmock = mocker.patch(
|
||||
'freqtrade.data.history.jsondatahandler.JsonDataHandler._ohlcv_load',
|
||||
'freqtrade.data.history.featherdatahandler.FeatherDataHandler._ohlcv_load',
|
||||
MagicMock(return_value=DataFrame()))
|
||||
timerange = TimeRange('date', None, 1510639620, 0)
|
||||
load_pair_history(pair='UNITTEST/BTC', timeframe='1m',
|
||||
|
|
@ -112,7 +112,7 @@ def test_load_data_with_new_pair_1min(ohlcv_history_list, mocker, caplog,
|
|||
tmpdir1 = Path(tmpdir)
|
||||
mocker.patch(f'{EXMS}.get_historic_ohlcv', return_value=ohlcv_history_list)
|
||||
exchange = get_patched_exchange(mocker, default_conf)
|
||||
file = tmpdir1 / 'MEME_BTC-1m.json'
|
||||
file = tmpdir1 / 'MEME_BTC-1m.feather'
|
||||
|
||||
# do not download a new pair if refresh_pairs isn't set
|
||||
load_pair_history(datadir=tmpdir1, timeframe='1m', pair='MEME/BTC', candle_type=candle_type)
|
||||
|
|
@ -280,10 +280,10 @@ def test_download_pair_history(
|
|||
mocker.patch(f'{EXMS}.get_historic_ohlcv', return_value=ohlcv_history_list)
|
||||
exchange = get_patched_exchange(mocker, default_conf)
|
||||
tmpdir1 = Path(tmpdir)
|
||||
file1_1 = tmpdir1 / f'{subdir}MEME_BTC-1m{file_tail}.json'
|
||||
file1_5 = tmpdir1 / f'{subdir}MEME_BTC-5m{file_tail}.json'
|
||||
file2_1 = tmpdir1 / f'{subdir}CFI_BTC-1m{file_tail}.json'
|
||||
file2_5 = tmpdir1 / f'{subdir}CFI_BTC-5m{file_tail}.json'
|
||||
file1_1 = tmpdir1 / f'{subdir}MEME_BTC-1m{file_tail}.feather'
|
||||
file1_5 = tmpdir1 / f'{subdir}MEME_BTC-5m{file_tail}.feather'
|
||||
file2_1 = tmpdir1 / f'{subdir}CFI_BTC-1m{file_tail}.feather'
|
||||
file2_5 = tmpdir1 / f'{subdir}CFI_BTC-5m{file_tail}.feather'
|
||||
|
||||
assert not file1_1.is_file()
|
||||
assert not file2_1.is_file()
|
||||
|
|
@ -326,7 +326,7 @@ def test_download_pair_history2(mocker, default_conf, testdatadir) -> None:
|
|||
[1509836580000, 0.00161, 0.00161, 0.00161, 0.00161, 82.390199]
|
||||
]
|
||||
json_dump_mock = mocker.patch(
|
||||
'freqtrade.data.history.jsondatahandler.JsonDataHandler.ohlcv_store',
|
||||
'freqtrade.data.history.featherdatahandler.FeatherDataHandler.ohlcv_store',
|
||||
return_value=None)
|
||||
mocker.patch(f'{EXMS}.get_historic_ohlcv', return_value=tick)
|
||||
exchange = get_patched_exchange(mocker, default_conf)
|
||||
|
|
@ -386,7 +386,7 @@ def test_load_partial_missing(testdatadir, caplog) -> None:
|
|||
|
||||
def test_init(default_conf) -> None:
|
||||
assert {} == load_data(
|
||||
datadir=Path(''),
|
||||
datadir=Path(),
|
||||
pairs=[],
|
||||
timeframe=default_conf['timeframe']
|
||||
)
|
||||
|
|
@ -395,14 +395,14 @@ def test_init(default_conf) -> None:
|
|||
def test_init_with_refresh(default_conf, mocker) -> None:
|
||||
exchange = get_patched_exchange(mocker, default_conf)
|
||||
refresh_data(
|
||||
datadir=Path(''),
|
||||
datadir=Path(),
|
||||
pairs=[],
|
||||
timeframe=default_conf['timeframe'],
|
||||
exchange=exchange,
|
||||
candle_type=CandleType.SPOT
|
||||
)
|
||||
assert {} == load_data(
|
||||
datadir=Path(''),
|
||||
datadir=Path(),
|
||||
pairs=[],
|
||||
timeframe=default_conf['timeframe']
|
||||
)
|
||||
|
|
@ -627,8 +627,8 @@ def test_download_trades_history(trades_history, mocker, default_conf, testdatad
|
|||
def test_convert_trades_to_ohlcv(testdatadir, tmpdir, caplog):
|
||||
tmpdir1 = Path(tmpdir)
|
||||
pair = 'XRP/ETH'
|
||||
file1 = tmpdir1 / 'XRP_ETH-1m.json'
|
||||
file5 = tmpdir1 / 'XRP_ETH-5m.json'
|
||||
file1 = tmpdir1 / 'XRP_ETH-1m.feather'
|
||||
file5 = tmpdir1 / 'XRP_ETH-5m.feather'
|
||||
filetrades = tmpdir1 / 'XRP_ETH-trades.json.gz'
|
||||
copyfile(testdatadir / file1.name, file1)
|
||||
copyfile(testdatadir / file5.name, file5)
|
||||
|
|
@ -641,6 +641,7 @@ def test_convert_trades_to_ohlcv(testdatadir, tmpdir, caplog):
|
|||
tr = TimeRange.parse_timerange('20191011-20191012')
|
||||
|
||||
convert_trades_to_ohlcv([pair], timeframes=['1m', '5m'],
|
||||
data_format_trades='jsongz',
|
||||
datadir=tmpdir1, timerange=tr, erase=True)
|
||||
|
||||
assert log_has("Deleting existing data for pair XRP/ETH, interval 1m.", caplog)
|
||||
|
|
@ -648,11 +649,12 @@ def test_convert_trades_to_ohlcv(testdatadir, tmpdir, caplog):
|
|||
df_1m = load_pair_history(datadir=tmpdir1, timeframe="1m", pair=pair)
|
||||
df_5m = load_pair_history(datadir=tmpdir1, timeframe="5m", pair=pair)
|
||||
|
||||
assert df_1m.equals(dfbak_1m)
|
||||
assert df_5m.equals(dfbak_5m)
|
||||
assert_frame_equal(dfbak_1m, df_1m, check_exact=True)
|
||||
assert_frame_equal(dfbak_5m, df_5m, check_exact=True)
|
||||
|
||||
assert not log_has('Could not convert NoDatapair to OHLCV.', caplog)
|
||||
|
||||
convert_trades_to_ohlcv(['NoDatapair'], timeframes=['1m', '5m'],
|
||||
data_format_trades='jsongz',
|
||||
datadir=tmpdir1, timerange=tr, erase=True)
|
||||
assert log_has('Could not convert NoDatapair to OHLCV.', caplog)
|
||||
|
|
|
|||
|
|
@ -391,7 +391,7 @@ class TestCCXTExchange:
|
|||
assert po['id'] is not None
|
||||
if len(order.keys()) < 5:
|
||||
# Kucoin case
|
||||
assert po['status'] == 'closed'
|
||||
assert po['status'] is None
|
||||
continue
|
||||
assert po['timestamp'] == 1674493798550
|
||||
assert isinstance(po['datetime'], str)
|
||||
|
|
|
|||
|
|
@ -4343,11 +4343,11 @@ def test__fetch_and_calculate_funding_fees(
|
|||
ex = get_patched_exchange(mocker, default_conf, api_mock, id=exchange)
|
||||
mocker.patch(f'{EXMS}.timeframes', PropertyMock(return_value=['1h', '4h', '8h']))
|
||||
funding_fees = ex._fetch_and_calculate_funding_fees(
|
||||
pair='ADA/USDT', amount=amount, is_short=True, open_date=d1, close_date=d2)
|
||||
pair='ADA/USDT:USDT', amount=amount, is_short=True, open_date=d1, close_date=d2)
|
||||
assert pytest.approx(funding_fees) == expected_fees
|
||||
# Fees for Longs are inverted
|
||||
funding_fees = ex._fetch_and_calculate_funding_fees(
|
||||
pair='ADA/USDT', amount=amount, is_short=False, open_date=d1, close_date=d2)
|
||||
pair='ADA/USDT:USDT', amount=amount, is_short=False, open_date=d1, close_date=d2)
|
||||
assert pytest.approx(funding_fees) == -expected_fees
|
||||
|
||||
# Return empty "refresh_latest"
|
||||
|
|
@ -4355,7 +4355,7 @@ def test__fetch_and_calculate_funding_fees(
|
|||
ex = get_patched_exchange(mocker, default_conf, api_mock, id=exchange)
|
||||
with pytest.raises(ExchangeError, match="Could not find funding rates."):
|
||||
ex._fetch_and_calculate_funding_fees(
|
||||
pair='ADA/USDT', amount=amount, is_short=False, open_date=d1, close_date=d2)
|
||||
pair='ADA/USDT:USDT', amount=amount, is_short=False, open_date=d1, close_date=d2)
|
||||
|
||||
|
||||
@pytest.mark.parametrize('exchange,expected_fees', [
|
||||
|
|
@ -5424,7 +5424,7 @@ def test_stoploss_contract_size(mocker, default_conf, contract_size, order_amoun
|
|||
|
||||
assert api_mock.create_order.call_args_list[0][1]['amount'] == order_amount
|
||||
assert order['amount'] == 100
|
||||
assert order['cost'] == 100
|
||||
assert order['cost'] == order_amount
|
||||
assert order['filled'] == 100
|
||||
assert order['remaining'] == 100
|
||||
|
||||
|
|
|
|||
|
|
@ -21,7 +21,7 @@ from freqtrade.data.history import get_timerange
|
|||
from freqtrade.enums import CandleType, ExitType, RunMode
|
||||
from freqtrade.exceptions import DependencyException, OperationalException
|
||||
from freqtrade.exchange.exchange import timeframe_to_next_date
|
||||
from freqtrade.optimize.backtest_caching import get_strategy_run_id
|
||||
from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename, get_strategy_run_id
|
||||
from freqtrade.optimize.backtesting import Backtesting
|
||||
from freqtrade.persistence import LocalTrade, Trade
|
||||
from freqtrade.resolvers import StrategyResolver
|
||||
|
|
@ -601,6 +601,9 @@ def test_backtest__enter_trade_futures(default_conf_usdt, fee, mocker) -> None:
|
|||
|
||||
trade = backtesting._enter_trade(pair, row=row, direction='short')
|
||||
assert pytest.approx(trade.liquidation_price) == 0.11787191
|
||||
assert pytest.approx(trade.orders[0].cost) == (
|
||||
trade.stake_amount * trade.leverage + trade.fee_open)
|
||||
assert pytest.approx(trade.orders[-1].stake_amount) == trade.stake_amount
|
||||
|
||||
# Stake-amount too high!
|
||||
mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=600.0)
|
||||
|
|
@ -1995,3 +1998,40 @@ def test_get_strategy_run_id(default_conf_usdt):
|
|||
strategy = StrategyResolver.load_strategy(default_conf_usdt)
|
||||
x = get_strategy_run_id(strategy)
|
||||
assert isinstance(x, str)
|
||||
|
||||
|
||||
def test_get_backtest_metadata_filename():
|
||||
# Test with a file path
|
||||
filename = Path('backtest_results.json')
|
||||
expected = Path('backtest_results.meta.json')
|
||||
assert get_backtest_metadata_filename(filename) == expected
|
||||
|
||||
# Test with a file path with multiple dots in the name
|
||||
filename = Path('/path/to/backtest.results.json')
|
||||
expected = Path('/path/to/backtest.results.meta.json')
|
||||
assert get_backtest_metadata_filename(filename) == expected
|
||||
|
||||
# Test with a file path with no parent directory
|
||||
filename = Path('backtest_results.json')
|
||||
expected = Path('backtest_results.meta.json')
|
||||
assert get_backtest_metadata_filename(filename) == expected
|
||||
|
||||
# Test with a string file path
|
||||
filename = '/path/to/backtest_results.json'
|
||||
expected = Path('/path/to/backtest_results.meta.json')
|
||||
assert get_backtest_metadata_filename(filename) == expected
|
||||
|
||||
# Test with a string file path with no extension
|
||||
filename = '/path/to/backtest_results'
|
||||
expected = Path('/path/to/backtest_results.meta')
|
||||
assert get_backtest_metadata_filename(filename) == expected
|
||||
|
||||
# Test with a string file path with multiple dots in the name
|
||||
filename = '/path/to/backtest.results.json'
|
||||
expected = Path('/path/to/backtest.results.meta.json')
|
||||
assert get_backtest_metadata_filename(filename) == expected
|
||||
|
||||
# Test with a string file path with no parent directory
|
||||
filename = 'backtest_results.json'
|
||||
expected = Path('backtest_results.meta.json')
|
||||
assert get_backtest_metadata_filename(filename) == expected
|
||||
|
|
|
|||
|
|
@ -23,7 +23,8 @@ from freqtrade.optimize.optimize_reports import (generate_backtest_stats, genera
|
|||
store_backtest_analysis_results,
|
||||
store_backtest_stats, text_table_bt_results,
|
||||
text_table_exit_reason, text_table_strategy)
|
||||
from freqtrade.optimize.optimize_reports.optimize_reports import _get_resample_from_period
|
||||
from freqtrade.optimize.optimize_reports.optimize_reports import (_get_resample_from_period,
|
||||
calc_streak)
|
||||
from freqtrade.resolvers.strategy_resolver import StrategyResolver
|
||||
from freqtrade.util import dt_ts
|
||||
from freqtrade.util.datetime_helpers import dt_from_ts, dt_utc
|
||||
|
|
@ -212,7 +213,8 @@ def test_store_backtest_stats(testdatadir, mocker):
|
|||
|
||||
dump_mock = mocker.patch('freqtrade.optimize.optimize_reports.bt_storage.file_dump_json')
|
||||
|
||||
store_backtest_stats(testdatadir, {'metadata': {}}, '2022_01_01_15_05_13')
|
||||
data = {'metadata': {}, 'strategy': {}, 'strategy_comparison': []}
|
||||
store_backtest_stats(testdatadir, data, '2022_01_01_15_05_13')
|
||||
|
||||
assert dump_mock.call_count == 3
|
||||
assert isinstance(dump_mock.call_args_list[0][0][0], Path)
|
||||
|
|
@ -220,7 +222,7 @@ def test_store_backtest_stats(testdatadir, mocker):
|
|||
|
||||
dump_mock.reset_mock()
|
||||
filename = testdatadir / 'testresult.json'
|
||||
store_backtest_stats(filename, {'metadata': {}}, '2022_01_01_15_05_13')
|
||||
store_backtest_stats(filename, data, '2022_01_01_15_05_13')
|
||||
assert dump_mock.call_count == 3
|
||||
assert isinstance(dump_mock.call_args_list[0][0][0], Path)
|
||||
# result will be testdatadir / testresult-<timestamp>.json
|
||||
|
|
@ -348,6 +350,32 @@ def test_generate_trading_stats(testdatadir):
|
|||
assert res['losses'] == 0
|
||||
|
||||
|
||||
def test_calc_streak(testdatadir):
|
||||
df = pd.DataFrame({
|
||||
'profit_ratio': [0.05, -0.02, -0.03, -0.05, 0.01, 0.02, 0.03, 0.04, -0.02, -0.03],
|
||||
})
|
||||
# 4 consecutive wins, 3 consecutive losses
|
||||
res = calc_streak(df)
|
||||
assert res == (4, 3)
|
||||
assert isinstance(res[0], int)
|
||||
assert isinstance(res[1], int)
|
||||
|
||||
# invert situation
|
||||
df1 = df.copy()
|
||||
df1['profit_ratio'] = df1['profit_ratio'] * -1
|
||||
assert calc_streak(df1) == (3, 4)
|
||||
|
||||
df_empty = pd.DataFrame({
|
||||
'profit_ratio': [],
|
||||
})
|
||||
assert df_empty.empty
|
||||
assert calc_streak(df_empty) == (0, 0)
|
||||
|
||||
filename = testdatadir / "backtest_results/backtest-result.json"
|
||||
bt_data = load_backtest_data(filename)
|
||||
assert calc_streak(bt_data) == (7, 18)
|
||||
|
||||
|
||||
def test_text_table_exit_reason():
|
||||
|
||||
results = pd.DataFrame(
|
||||
|
|
|
|||
|
|
@ -563,14 +563,14 @@ def test_calc_open_close_trade_price(
|
|||
trade.open_order_id = f'something-{is_short}-{lev}-{exchange}'
|
||||
|
||||
oobj = Order.parse_from_ccxt_object(entry_order, 'ADA/USDT', trade.entry_side)
|
||||
oobj.trade = trade
|
||||
oobj._trade_live = trade
|
||||
oobj.update_from_ccxt_object(entry_order)
|
||||
trade.update_trade(oobj)
|
||||
|
||||
trade.funding_fees = funding_fees
|
||||
|
||||
oobj = Order.parse_from_ccxt_object(exit_order, 'ADA/USDT', trade.exit_side)
|
||||
oobj.trade = trade
|
||||
oobj._trade_live = trade
|
||||
oobj.update_from_ccxt_object(exit_order)
|
||||
trade.update_trade(oobj)
|
||||
|
||||
|
|
|
|||
|
|
@ -179,6 +179,7 @@ def test_trade_fromjson():
|
|||
assert trade.open_date_utc == datetime(2022, 10, 18, 9, 12, 42, tzinfo=timezone.utc)
|
||||
assert isinstance(trade.open_date, datetime)
|
||||
assert trade.exit_reason == 'no longer good'
|
||||
assert trade.realized_profit == 2.76315361
|
||||
|
||||
assert len(trade.orders) == 5
|
||||
last_o = trade.orders[-1]
|
||||
|
|
|
|||
|
|
@ -35,7 +35,7 @@ def test_gen_pairlist_with_local_file(mocker, rpl_config):
|
|||
mock_file_path.exists.return_value = True
|
||||
|
||||
jsonparse = json.loads(mock_file.read.return_value)
|
||||
mocker.patch('freqtrade.plugins.pairlist.RemotePairList.json.load', return_value=jsonparse)
|
||||
mocker.patch('freqtrade.plugins.pairlist.RemotePairList.rapidjson.load', return_value=jsonparse)
|
||||
|
||||
rpl_config['pairlists'] = [
|
||||
{
|
||||
|
|
|
|||
|
|
@ -402,6 +402,8 @@ def test_rpc_trade_statistics(default_conf_usdt, ticker, fee, mocker) -> None:
|
|||
assert res['first_trade_timestamp'] == 0
|
||||
assert res['latest_trade_date'] == ''
|
||||
assert res['latest_trade_timestamp'] == 0
|
||||
assert res['expectancy'] == 0
|
||||
assert res['expectancy_ratio'] == 100
|
||||
|
||||
# Create some test data
|
||||
create_mock_trades_usdt(fee)
|
||||
|
|
@ -413,6 +415,9 @@ def test_rpc_trade_statistics(default_conf_usdt, ticker, fee, mocker) -> None:
|
|||
assert pytest.approx(stats['profit_all_coin']) == -77.45964918
|
||||
assert pytest.approx(stats['profit_all_percent_mean']) == -57.86
|
||||
assert pytest.approx(stats['profit_all_fiat']) == -85.205614098
|
||||
assert pytest.approx(stats['winrate']) == 0.666666667
|
||||
assert pytest.approx(stats['expectancy']) == 0.913333333
|
||||
assert pytest.approx(stats['expectancy_ratio']) == 0.223308883
|
||||
assert stats['trade_count'] == 7
|
||||
assert stats['first_trade_humanized'] == '2 days ago'
|
||||
assert stats['latest_trade_humanized'] == '17 minutes ago'
|
||||
|
|
|
|||
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Loading…
Reference in New Issue
Block a user