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6
.github/workflows/ci.yml vendored
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@ -24,7 +24,7 @@ jobs:
strategy:
matrix:
os: [ ubuntu-18.04, ubuntu-20.04, ubuntu-22.04 ]
python-version: ["3.8", "3.9", "3.10"]
python-version: ["3.8", "3.9", "3.10.6"]
steps:
- uses: actions/checkout@v3
@ -121,7 +121,7 @@ jobs:
strategy:
matrix:
os: [ macos-latest ]
python-version: ["3.8", "3.9", "3.10"]
python-version: ["3.8", "3.9", "3.10.6"]
steps:
- uses: actions/checkout@v3
@ -205,7 +205,7 @@ jobs:
strategy:
matrix:
os: [ windows-latest ]
python-version: ["3.8", "3.9", "3.10"]
python-version: ["3.8", "3.9", "3.10.6"]
steps:
- uses: actions/checkout@v3

2
.pre-commit-config.yaml
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@ -15,7 +15,7 @@ repos:
additional_dependencies:
- types-cachetools==5.2.1
- types-filelock==3.2.7
- types-requests==2.28.9
- types-requests==2.28.10
- types-tabulate==0.8.11
- types-python-dateutil==2.8.19
# stages: [push]

5
config_examples/config_freqai.example.json
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@ -77,7 +77,8 @@
"indicator_periods_candles": [
10,
20
]
],
"plot_feature_importance": false
},
"data_split_parameters": {
"test_size": 0.33,
@ -93,4 +94,4 @@
"internals": {
"process_throttle_secs": 5
}
}
}

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

30
docs/configuration.md
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@ -57,7 +57,7 @@ You can specify additional configuration files in `add_config_files`. Files spec
This is similar to using multiple `--config` parameters, but simpler in usage as you don't have to specify all files for all commands.
!!! Tip "Use multiple configuration files to keep secrets secret"
You can use a 2nd configuration file containing your secrets. That way you can share your "primary" configuration file, while still keeping your API keys for yourself.
You can use a 2nd configuration file containing your secrets. That way you can share your "primary" configuration file, while still keeping your API keys for yourself.
The 2nd file should only specify what you intend to override.
If a key is in more than one of the configurations, then the "last specified configuration" wins (in the above example, `config-private.json`).
@ -110,7 +110,7 @@ This is similar to using multiple `--config` parameters, but simpler in usage as
"stake_amount": "unlimited"
}
```
If multiple files are in the `add_config_files` section, then they will be assumed to be at identical levels, having the last occurrence override the earlier config (unless a parent already defined such a key).
## Configuration parameters
@ -659,17 +659,7 @@ You should also make sure to read the [Exchanges](exchanges.md) section of the d
### Using proxy with Freqtrade
To use a proxy with freqtrade, add the kwarg `"aiohttp_trust_env"=true` to the `"ccxt_async_kwargs"` dict in the exchange section of the configuration.
An example for this can be found in `config_examples/config_full.example.json`
``` json
"ccxt_async_config": {
"aiohttp_trust_env": true
}
```
Then, export your proxy settings using the variables `"HTTP_PROXY"` and `"HTTPS_PROXY"` set to the appropriate values
To use a proxy with freqtrade, export your proxy settings using the variables `"HTTP_PROXY"` and `"HTTPS_PROXY"` set to the appropriate values.
``` bash
export HTTP_PROXY="http://addr:port"
@ -677,6 +667,20 @@ export HTTPS_PROXY="http://addr:port"
freqtrade
```
#### Proxy just exchange requests
To use a proxy just for exchange connections (skips/ignores telegram and coingecko) - you can also define the proxies as part of the ccxt configuration.
``` json
"ccxt_config": {
"aiohttp_proxy": "http://addr:port",
"proxies": {
"http": "http://addr:port",
"https": "http://addr:port"
},
}
```
## Next step
Now you have configured your config.json, the next step is to [start your bot](bot-usage.md).

10
docs/data-download.md
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@ -25,8 +25,7 @@ usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[--include-inactive-pairs]
[--timerange TIMERANGE] [--dl-trades]
[--exchange EXCHANGE]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
[--erase]
[-t TIMEFRAMES [TIMEFRAMES ...]] [--erase]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--data-format-trades {json,jsongz,hdf5}]
[--trading-mode {spot,margin,futures}]
@ -37,7 +36,8 @@ optional arguments:
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--pairs-file FILE File containing a list of pairs to download.
--pairs-file FILE File containing a list of pairs. Takes precedence over
--pairs or pairs configured in the configuration.
--days INT Download data for given number of days.
--new-pairs-days INT Download data of new pairs for given number of days.
Default: `None`.
@ -50,7 +50,7 @@ optional arguments:
as --timeframes/-t.
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
config is provided.
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
-t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
Specify which tickers to download. Space-separated
list. Default: `1m 5m`.
--erase Clean all existing data for the selected
@ -61,7 +61,7 @@ optional arguments:
--data-format-trades {json,jsongz,hdf5}
Storage format for downloaded trades data. (default:
`jsongz`).
--trading-mode {spot,margin,futures}
--trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
Select Trading mode
--prepend Allow data prepending. (Data-appending is disabled)

5
docs/exchanges.md
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@ -57,7 +57,8 @@ This configuration enables kraken, as well as rate-limiting to avoid bans from t
Binance supports [time_in_force](configuration.md#understand-order_time_in_force).
!!! Tip "Stoploss on Exchange"
Binance supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange..
Binance supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
On futures, Binance supports both `stop-limit` as well as `stop-market` orders. You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
### Binance Blacklist
@ -232,7 +233,7 @@ OKX requires a passphrase for each api key, you will therefore need to add this
!!! Warning "Futures"
OKX Futures has the concept of "position mode" - which can be Net or long/short (hedge mode).
Freqtrade supports both modes - but changing the mode mid-trading is not supported and will lead to exceptions and failures to place trades.
Freqtrade supports both modes (we recommend to use net mode) - but changing the mode mid-trading is not supported and will lead to exceptions and failures to place trades.
OKX also only provides MARK candles for the past ~3 months. Backtesting futures prior to that date will therefore lead to slight deviations, as funding-fees cannot be calculated correctly without this data.
## Gate.io

32
docs/freqai.md
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@ -109,11 +109,12 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `indicator_max_period_candles` | **No longer used**. User must use the strategy set `startup_candle_count` which defines the maximum *period* used in `populate_any_indicators()` for indicator creation (timeframe independent). FreqAI uses this information in combination with the maximum timeframe to calculate how many data points it should download so that the first data point does not have a NaN <br> **Datatype:** positive integer.
| `indicator_periods_candles` | Calculate indicators for `indicator_periods_candles` time periods and add them to the feature set. <br> **Datatype:** List of positive integers.
| `stratify_training_data` | This value is used to indicate the grouping of the data. For example, 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing. See details about how it works [here](#stratifying-the-data-for-training-and-testing-the-model) <br> **Datatype:** Positive integer.
| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean.
| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis)
| `plot_feature_importance` | Create an interactive feature importance plot for each model.<br> **Datatype:** Boolean.<br> **Datatype:** Boolean, defaults to `False`
| `DI_threshold` | Activates the Dissimilarity Index for outlier detection when > 0. See details about how it works [here](#removing-outliers-with-the-dissimilarity-index). <br> **Datatype:** Positive float (typically < 1).
| `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training data set, as well as from incoming data points. See details about how it works [here](#removing-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Boolean.
| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](#removing-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Dictionary.
| `use_DBSCAN_to_remove_outliers` | Cluster data using DBSCAN to identify and remove outliers from training and prediction data. See details about how it works [here](#removing-outliers-with-dbscan). <br> **Datatype:** Boolean.
| `use_DBSCAN_to_remove_outliers` | Cluster data using DBSCAN to identify and remove outliers from training and prediction data. See details about how it works [here](#removing-outliers-with-dbscan). <br> **Datatype:** Boolean.
| `inlier_metric_window` | If set, FreqAI will add the `inlier_metric` to the training feature set and set the lookback to be the `inlier_metric_window`. Details of how the `inlier_metric` is computed can be found [here](#using-the-inliermetric) <br> **Datatype:** int. Default: 0
| `noise_standard_deviation` | If > 0, FreqAI adds noise to the training features. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. Value should be kept relative to the normalized space between -1 and 1). In other words, since data is always normalized between -1 and 1 in FreqAI, the user can expect a `noise_standard_deviation: 0.05` to see 32% of data randomly increased/decreased by more than 2.5% (i.e. the percent of data falling within the first standard deviation). Good for preventing overfitting. <br> **Datatype:** int. Default: 0
| `outlier_protection_percentage` | 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 while keeping the original dataset intact. If the outlier protection is triggered, no predictions will be made based on the training data. <br> **Datatype:** Float. Default: `30`
@ -200,19 +201,6 @@ The FreqAI strategy requires the user to include the following lines of code in
# passed to any single indicator)
startup_candle_count: int = 20
def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist()
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
informative_pairs = []
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
for pair in whitelist_pairs:
informative_pairs.append((pair, tf))
for pair in corr_pairs:
if pair in whitelist_pairs:
continue # avoid duplication
informative_pairs.append((pair, tf))
return informative_pairs
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# the model will return all labels created by user in `populate_any_indicators`
@ -533,7 +521,7 @@ The FreqAI backtesting module can be executed with the following command:
freqtrade backtesting --strategy FreqaiExampleStrategy --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701
```
Backtesting mode requires the user to have the data [pre-downloaded](#downloading-data-for-backtesting) (unlike in dry/live mode where FreqAI automatically downloads the necessary data). The user should be careful to consider that the time range of the downloaded data is more than the backtesting time range. This is because FreqAI needs data prior to the desired backtesting time range in order to train a model to be ready to make predictions on the first candle of the user-set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration).
Backtesting mode requires the user to have the data [pre-downloaded](#downloading-data-for-backtesting) (unlike in dry/live mode where FreqAI automatically downloads the necessary data). The user should be careful to consider that the time range of the downloaded data is more than the backtesting time range. This is because FreqAI needs data prior to the desired backtesting time range in order to train a model to be ready to make predictions on the first candle of the user-set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration).
If this command has never been executed with the existing config file, it will train a new model
for each pair, for each backtesting window within the expanded `--timerange`.
@ -561,7 +549,7 @@ Users need to have the data pre-downloaded in the same fashion as if they were d
- It's not possible to hyperopt indicators in `populate_any_indicators()` function. This means that the user cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
- The [Backtesting](#backtesting) instructions also apply to Hyperopt.
The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. Users need to focus on hyperopting parameters that are not used in their FreqAI features. For example, users should not try to hyperopt rolling window lengths in their feature creation, or any of their FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only.
The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. Users need to focus on hyperopting parameters that are not used in their FreqAI features. For example, users should not try to hyperopt rolling window lengths in their feature creation, or any of their FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only.
A good example of a hyperoptable parameter in FreqAI is a value for `DI_values` beyond which we consider outliers and below which we consider inliers:
@ -586,7 +574,7 @@ FreqAI will train have trained 8 separate models at the end of `--timerange` (be
Although fractional `backtest_period_days` is allowed, the user should be aware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, if the user wants to set a `--timerange` of 10 days, and asks for a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. Because of this, a true backtest of FreqAI adaptive training would take a *very* long time. The best way to fully test a model is to run it dry and let it constantly train. In this case, backtesting would take the exact same amount of time as a dry run.
### Downloading data for backtesting
Live/dry instances will download the data automatically for the user, but users who wish to use backtesting functionality still need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). FreqAI users need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that they have a sufficient amount of training data *before* the start of their backtesting timerange. The amount of additional data can be roughly estimated by moving the start date of the timerange backwards by `train_period_days` and the `startup_candle_count` ([details](#setting-the-startupcandlecount)) from the beginning of the desired backtesting timerange.
Live/dry instances will download the data automatically for the user, but users who wish to use backtesting functionality still need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). FreqAI users need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that they have a sufficient amount of training data *before* the start of their backtesting timerange. The amount of additional data can be roughly estimated by moving the start date of the timerange backwards by `train_period_days` and the `startup_candle_count` ([details](#setting-the-startupcandlecount)) from the beginning of the desired backtesting timerange.
As an example, if we wish to backtest the `--timerange` above of `20210501-20210701`, and we use the example config which sets `train_period_days` to 15. The startup candle count is 40 on a maximum `include_timeframes` of 1h. We would need 20210501 - 15 days - 40 * 1h / 24 hours = 20210414 (16.7 days earlier than the start of the desired training timerange).
@ -685,13 +673,13 @@ The test data is used to evaluate the performance of the model after training. I
### Using the `inlier_metric`
The `inlier_metric` is a metric aimed at quantifying how different a prediction data point is from the most recent historic data points.
The `inlier_metric` is a metric aimed at quantifying how different a prediction data point is from the most recent historic data points.
User can set `inlier_metric_window` to set the look back window. FreqAI will compute the distance between the present prediction point and each of the previous data points (total of `inlier_metric_window` points).
User can set `inlier_metric_window` to set the look back window. FreqAI will compute the distance between the present prediction point and each of the previous data points (total of `inlier_metric_window` points).
This function goes one step further - during training, it computes the `inlier_metric` for all training data points and builds weibull distributions for each each lookback point. The cumulative distribution function for the weibull distribution is used to produce a quantile for each of the data points. The quantiles for each lookback point are averaged to create the `inlier_metric`.
This function goes one step further - during training, it computes the `inlier_metric` for all training data points and builds weibull distributions for each each lookback point. The cumulative distribution function for the weibull distribution is used to produce a quantile for each of the data points. The quantiles for each lookback point are averaged to create the `inlier_metric`.
FreqAI adds this `inlier_metric` score to the training features! In other words, your model is trained to recognize how this temporal inlier metric is related to the user set labels.
FreqAI adds this `inlier_metric` score to the training features! In other words, your model is trained to recognize how this temporal inlier metric is related to the user set labels.
This function does **not** remove outliers from the data set.

2
docs/requirements-docs.txt
View File

@ -1,6 +1,6 @@
markdown==3.3.7
mkdocs==1.3.1
mkdocs-material==8.4.2
mkdocs-material==8.5.2
mdx_truly_sane_lists==1.3
pymdown-extensions==9.5
jinja2==3.1.2

3
docs/strategy-customization.md
View File

@ -264,7 +264,8 @@ def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFram
### Exit signal rules
Edit the method `populate_exit_trend()` into your strategy file to update your exit strategy.
Please note that the exit-signal is only used if `use_exit_signal` is set to true in the configuration.
The exit-signal is only used for exits if `use_exit_signal` is set to true in the configuration.
`use_exit_signal` will not influence [signal collision rules](#colliding-signals) - which will still apply and can prevent entries.
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.

4
docs/telegram-usage.md
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@ -82,6 +82,8 @@ Example configuration showing the different settings:
"warning": "on",
"startup": "off",
"entry": "silent",
"entry_fill": "on",
"entry_cancel": "silent",
"exit": {
"roi": "silent",
"emergency_exit": "on",
@ -93,9 +95,7 @@ Example configuration showing the different settings:
"custom_exit": "silent",
"partial_exit": "on"
},
"entry_cancel": "silent",
"exit_cancel": "on",
"entry_fill": "off",
"exit_fill": "off",
"protection_trigger": "off",
"protection_trigger_global": "on",

4
docs/utils.md
View File

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

8
freqtrade/commands/arguments.py
View File

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

5
freqtrade/commands/cli_options.py
View File

@ -69,7 +69,7 @@ AVAILABLE_CLI_OPTIONS = {
metavar='PATH',
),
"datadir": Arg(
'-d', '--datadir',
'-d', '--datadir', '--data-dir',
help='Path to directory with historical backtesting data.',
metavar='PATH',
),
@ -393,7 +393,8 @@ AVAILABLE_CLI_OPTIONS = {
# Download data
"pairs_file": Arg(
'--pairs-file',
help='File containing a list of pairs to download.',
help='File containing a list of pairs. '
'Takes precedence over --pairs or pairs configured in the configuration.',
metavar='FILE',
),
"days": Arg(

20
freqtrade/commands/deploy_commands.py
View File

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

4
freqtrade/configuration/check_exchange.py
View File

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

31
freqtrade/configuration/configuration.py
View File

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

11
freqtrade/configuration/deprecated_settings.py
View File

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

6
freqtrade/configuration/directory_operations.py
View File

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

4
freqtrade/configuration/load_config.py
View File

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

21
freqtrade/constants.py
View File

@ -3,7 +3,7 @@
"""
bot constants
"""
from typing import List, Literal, Tuple
from typing import Any, Dict, List, Literal, Tuple
from freqtrade.enums import CandleType
@ -289,11 +289,12 @@ CONF_SCHEMA = {
'warning': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'startup': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'entry': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'entry_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'entry_fill': {'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
},
'entry_fill': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
},
'entry_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS, },
'exit': {
'type': ['string', 'object'],
'additionalProperties': {
@ -301,12 +302,12 @@ CONF_SCHEMA = {
'enum': TELEGRAM_SETTING_OPTIONS
}
},
'exit_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'exit_fill': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'on'
},
'exit_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'protection_trigger': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
@ -315,14 +316,17 @@ CONF_SCHEMA = {
'protection_trigger_global': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'on'
},
'show_candle': {
'type': 'string',
'enum': ['off', 'ohlc'],
'default': 'off'
},
'strategy_msg': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'on'
},
}
},
@ -504,6 +508,7 @@ CONF_SCHEMA = {
"weight_factor": {"type": "number", "default": 0},
"principal_component_analysis": {"type": "boolean", "default": False},
"use_SVM_to_remove_outliers": {"type": "boolean", "default": False},
"plot_feature_importance": {"type": "boolean", "default": False},
"svm_params": {"type": "object",
"properties": {
"shuffle": {"type": "boolean", "default": False},
@ -600,3 +605,5 @@ LongShort = Literal['long', 'short']
EntryExit = Literal['entry', 'exit']
BuySell = Literal['buy', 'sell']
MakerTaker = Literal['maker', 'taker']
Config = Dict[str, Any]

11
freqtrade/data/converter.py
View File

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

10
freqtrade/data/dataprovider.py
View File

@ -12,7 +12,7 @@ from typing import Any, Dict, List, Optional, Tuple
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import ListPairsWithTimeframes, PairWithTimeframe
from freqtrade.constants import Config, ListPairsWithTimeframes, PairWithTimeframe
from freqtrade.data.history import load_pair_history
from freqtrade.enums import CandleType, RunMode
from freqtrade.exceptions import ExchangeError, OperationalException
@ -28,7 +28,7 @@ MAX_DATAFRAME_CANDLES = 1000
class DataProvider:
def __init__(self, config: dict, exchange: Optional[Exchange], pairlists=None) -> None:
def __init__(self, config: Config, exchange: Optional[Exchange], pairlists=None) -> None:
self._config = config
self._exchange = exchange
self._pairlists = pairlists
@ -86,7 +86,7 @@ class DataProvider:
"""
_candle_type = CandleType.from_string(
candle_type) if candle_type != '' else self._config['candle_type_def']
saved_pair = (pair, str(timeframe), _candle_type)
saved_pair: PairWithTimeframe = (pair, str(timeframe), _candle_type)
if saved_pair not in self.__cached_pairs_backtesting:
timerange = TimeRange.parse_timerange(None if self._config.get(
'timerange') is None else str(self._config.get('timerange')))
@ -196,7 +196,9 @@ class DataProvider:
Clear pair dataframe cache.
"""
self.__cached_pairs = {}
self.__cached_pairs_backtesting = {}
# Don't reset backtesting pairs -
# otherwise they're reloaded each time during hyperopt due to with analyze_per_epoch
# self.__cached_pairs_backtesting = {}
self.__slice_index = 0
# Exchange functions

36
freqtrade/data/history/hdf5datahandler.py
View File

@ -1,7 +1,5 @@
import logging
import re
from pathlib import Path
from typing import List, Optional
from typing import Optional
import numpy as np
import pandas as pd
@ -20,26 +18,6 @@ class HDF5DataHandler(IDataHandler):
_columns = DEFAULT_DATAFRAME_COLUMNS
@classmethod
def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]:
"""
Returns a list of all pairs with ohlcv data available in this datadir
for the specified timeframe
:param datadir: Directory to search for ohlcv files
:param timeframe: Timeframe to search pairs for
:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: List of Pairs
"""
candle = ""
if candle_type != CandleType.SPOT:
datadir = datadir.joinpath('futures')
candle = f"-{candle_type}"
_tmp = [re.search(r'^(\S+)(?=\-' + timeframe + candle + '.h5)', p.name)
for p in datadir.glob(f"*{timeframe}{candle}.h5")]
# Check if regex found something and only return these results
return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
def ohlcv_store(
self, pair: str, timeframe: str, data: pd.DataFrame, candle_type: CandleType) -> None:
"""
@ -121,18 +99,6 @@ class HDF5DataHandler(IDataHandler):
"""
raise NotImplementedError()
@classmethod
def trades_get_pairs(cls, datadir: Path) -> List[str]:
"""
Returns a list of all pairs for which trade data is available in this
:param datadir: Directory to search for ohlcv files
:return: List of Pairs
"""
_tmp = [re.search(r'^(\S+)(?=\-trades.h5)', p.name)
for p in datadir.glob("*trades.h5")]
# Check if regex found something and only return these results to avoid exceptions.
return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
def trades_store(self, pair: str, data: TradeList) -> None:
"""
Store trades data (list of Dicts) to file

20
freqtrade/data/history/idatahandler.py
View File

@ -26,7 +26,7 @@ logger = logging.getLogger(__name__)
class IDataHandler(ABC):
_OHLCV_REGEX = r'^([a-zA-Z_-]+)\-(\d+[a-zA-Z]{1,2})\-?([a-zA-Z_]*)?(?=\.)'
_OHLCV_REGEX = r'^([a-zA-Z_\d-]+)\-(\d+[a-zA-Z]{1,2})\-?([a-zA-Z_]*)?(?=\.)'
def __init__(self, datadir: Path) -> None:
self._datadir = datadir
@ -61,7 +61,6 @@ class IDataHandler(ABC):
) for match in _tmp if match and len(match.groups()) > 1]
@classmethod
@abstractmethod
def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]:
"""
Returns a list of all pairs with ohlcv data available in this datadir
@ -71,6 +70,15 @@ class IDataHandler(ABC):
:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: List of Pairs
"""
candle = ""
if candle_type != CandleType.SPOT:
datadir = datadir.joinpath('futures')
candle = f"-{candle_type}"
ext = cls._get_file_extension()
_tmp = [re.search(r'^(\S+)(?=\-' + timeframe + candle + f'.{ext})', p.name)
for p in datadir.glob(f"*{timeframe}{candle}.{ext}")]
# Check if regex found something and only return these results
return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
@abstractmethod
def ohlcv_store(
@ -144,13 +152,17 @@ class IDataHandler(ABC):
"""
@classmethod
@abstractmethod
def trades_get_pairs(cls, datadir: Path) -> List[str]:
"""
Returns a list of all pairs for which trade data is available in this
:param datadir: Directory to search for ohlcv files
:return: List of Pairs
"""
_ext = cls._get_file_extension()
_tmp = [re.search(r'^(\S+)(?=\-trades.' + _ext + ')', p.name)
for p in datadir.glob(f"*trades.{_ext}")]
# Check if regex found something and only return these results to avoid exceptions.
return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
@abstractmethod
def trades_store(self, pair: str, data: TradeList) -> None:
@ -255,7 +267,7 @@ class IDataHandler(ABC):
Rebuild pair name from filename
Assumes a asset name of max. 7 length to also support BTC-PERP and BTC-PERP:USD names.
"""
res = re.sub(r'^(([A-Za-z]{1,10})|^([A-Za-z\-]{1,6}))(_)', r'\g<1>/', pair, 1)
res = re.sub(r'^(([A-Za-z\d]{1,10})|^([A-Za-z\-]{1,6}))(_)', r'\g<1>/', pair, 1)
res = re.sub('_', ':', res, 1)
return res

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

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

5
freqtrade/edge/edge_positioning.py
View File

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

12
freqtrade/exchange/binance.py
View File

@ -1,5 +1,4 @@
""" Binance exchange subclass """
import json
import logging
from datetime import datetime
from pathlib import Path
@ -12,7 +11,7 @@ from freqtrade.enums import CandleType, MarginMode, TradingMode
from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import retrier
from freqtrade.misc import deep_merge_dicts
from freqtrade.misc import deep_merge_dicts, json_load
logger = logging.getLogger(__name__)
@ -31,7 +30,7 @@ class Binance(Exchange):
"ccxt_futures_name": "future"
}
_ft_has_futures: Dict = {
"stoploss_order_types": {"limit": "stop"},
"stoploss_order_types": {"limit": "limit", "market": "market"},
"tickers_have_price": False,
}
@ -48,13 +47,12 @@ class Binance(Exchange):
Returns True if adjustment is necessary.
:param side: "buy" or "sell"
"""
ordertype = 'stop' if self.trading_mode == TradingMode.FUTURES else 'stop_loss_limit'
order_types = ('stop_loss_limit', 'stop', 'stop_market')
return (
order.get('stopPrice', None) is None
or (
order['type'] == ordertype
order['type'] in order_types
and (
(side == "sell" and stop_loss > float(order['stopPrice'])) or
(side == "buy" and stop_loss < float(order['stopPrice']))
@ -201,7 +199,7 @@ class Binance(Exchange):
Path(__file__).parent / 'binance_leverage_tiers.json'
)
with open(leverage_tiers_path) as json_file:
return json.load(json_file)
return json_load(json_file)
else:
try:
return self._api.fetch_leverage_tiers()

686
freqtrade/exchange/binance_leverage_tiers.json
View File

@ -81,6 +81,104 @@
}
}
],
"1000LUNC/USDT": [
{
"tier": 1.0,
"currency": "USDT",
"minNotional": 0.0,
"maxNotional": 5000.0,
"maintenanceMarginRate": 0.01,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "25",
"notionalCap": "5000",
"notionalFloor": "0",
"maintMarginRatio": "0.01",
"cum": "0.0"
}
},
{
"tier": 2.0,
"currency": "USDT",
"minNotional": 5000.0,
"maxNotional": 25000.0,
"maintenanceMarginRate": 0.025,
"maxLeverage": 20.0,
"info": {
"bracket": "2",
"initialLeverage": "20",
"notionalCap": "25000",
"notionalFloor": "5000",
"maintMarginRatio": "0.025",
"cum": "75.0"
}
},
{
"tier": 3.0,
"currency": "USDT",
"minNotional": 25000.0,
"maxNotional": 100000.0,
"maintenanceMarginRate": 0.05,
"maxLeverage": 10.0,
"info": {
"bracket": "3",
"initialLeverage": "10",
"notionalCap": "100000",
"notionalFloor": "25000",
"maintMarginRatio": "0.05",
"cum": "700.0"
}
},
{
"tier": 4.0,
"currency": "USDT",
"minNotional": 100000.0,
"maxNotional": 250000.0,
"maintenanceMarginRate": 0.1,
"maxLeverage": 5.0,
"info": {
"bracket": "4",
"initialLeverage": "5",
"notionalCap": "250000",
"notionalFloor": "100000",
"maintMarginRatio": "0.1",
"cum": "5700.0"
}
},
{
"tier": 5.0,
"currency": "USDT",
"minNotional": 250000.0,
"maxNotional": 1000000.0,
"maintenanceMarginRate": 0.125,
"maxLeverage": 2.0,
"info": {
"bracket": "5",
"initialLeverage": "2",
"notionalCap": "1000000",
"notionalFloor": "250000",
"maintMarginRatio": "0.125",
"cum": "11950.0"
}
},
{
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386950.0"
}
}
],
"1000SHIB/BUSD": [
{
"tier": 1.0,
@ -1109,6 +1207,88 @@
}
}
],
"AMB/BUSD": [
{
"tier": 1.0,
"currency": "BUSD",
"minNotional": 0.0,
"maxNotional": 25000.0,
"maintenanceMarginRate": 0.025,
"maxLeverage": 20.0,
"info": {
"bracket": "1",
"initialLeverage": "20",
"notionalCap": "25000",
"notionalFloor": "0",
"maintMarginRatio": "0.025",
"cum": "0.0"
}
},
{
"tier": 2.0,
"currency": "BUSD",
"minNotional": 25000.0,
"maxNotional": 100000.0,
"maintenanceMarginRate": 0.05,
"maxLeverage": 10.0,
"info": {
"bracket": "2",
"initialLeverage": "10",
"notionalCap": "100000",
"notionalFloor": "25000",
"maintMarginRatio": "0.05",
"cum": "625.0"
}
},
{
"tier": 3.0,
"currency": "BUSD",
"minNotional": 100000.0,
"maxNotional": 250000.0,
"maintenanceMarginRate": 0.1,
"maxLeverage": 5.0,
"info": {
"bracket": "3",
"initialLeverage": "5",
"notionalCap": "250000",
"notionalFloor": "100000",
"maintMarginRatio": "0.1",
"cum": "5625.0"
}
},
{
"tier": 4.0,
"currency": "BUSD",
"minNotional": 250000.0,
"maxNotional": 1000000.0,
"maintenanceMarginRate": 0.125,
"maxLeverage": 2.0,
"info": {
"bracket": "4",
"initialLeverage": "2",
"notionalCap": "1000000",
"notionalFloor": "250000",
"maintMarginRatio": "0.125",
"cum": "11875.0"
}
},
{
"tier": 5.0,
"currency": "BUSD",
"minNotional": 1000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "5",
"initialLeverage": "1",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386875.0"
}
}
],
"ANC/BUSD": [
{
"tier": 1.0,
@ -3300,13 +3480,13 @@
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 30000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "30000000",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386950.0"
@ -4880,13 +5060,13 @@
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 30000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "30000000",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386940.0"
@ -8333,6 +8513,104 @@
}
}
],
"FOOTBALL/USDT": [
{
"tier": 1.0,
"currency": "USDT",
"minNotional": 0.0,
"maxNotional": 5000.0,
"maintenanceMarginRate": 0.01,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "25",
"notionalCap": "5000",
"notionalFloor": "0",
"maintMarginRatio": "0.01",
"cum": "0.0"
}
},
{
"tier": 2.0,
"currency": "USDT",
"minNotional": 5000.0,
"maxNotional": 25000.0,
"maintenanceMarginRate": 0.025,
"maxLeverage": 20.0,
"info": {
"bracket": "2",
"initialLeverage": "20",
"notionalCap": "25000",
"notionalFloor": "5000",
"maintMarginRatio": "0.025",
"cum": "75.0"
}
},
{
"tier": 3.0,
"currency": "USDT",
"minNotional": 25000.0,
"maxNotional": 100000.0,
"maintenanceMarginRate": 0.05,
"maxLeverage": 10.0,
"info": {
"bracket": "3",
"initialLeverage": "10",
"notionalCap": "100000",
"notionalFloor": "25000",
"maintMarginRatio": "0.05",
"cum": "700.0"
}
},
{
"tier": 4.0,
"currency": "USDT",
"minNotional": 100000.0,
"maxNotional": 250000.0,
"maintenanceMarginRate": 0.1,
"maxLeverage": 5.0,
"info": {
"bracket": "4",
"initialLeverage": "5",
"notionalCap": "250000",
"notionalFloor": "100000",
"maintMarginRatio": "0.1",
"cum": "5700.0"
}
},
{
"tier": 5.0,
"currency": "USDT",
"minNotional": 250000.0,
"maxNotional": 1000000.0,
"maintenanceMarginRate": 0.125,
"maxLeverage": 2.0,
"info": {
"bracket": "5",
"initialLeverage": "2",
"notionalCap": "1000000",
"notionalFloor": "250000",
"maintMarginRatio": "0.125",
"cum": "11950.0"
}
},
{
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386950.0"
}
}
],
"FTM/BUSD": [
{
"tier": 1.0,
@ -12123,6 +12401,104 @@
}
}
],
"LUNA2/USDT": [
{
"tier": 1.0,
"currency": "USDT",
"minNotional": 0.0,
"maxNotional": 5000.0,
"maintenanceMarginRate": 0.015,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "25",
"notionalCap": "5000",
"notionalFloor": "0",
"maintMarginRatio": "0.015",
"cum": "0.0"
}
},
{
"tier": 2.0,
"currency": "USDT",
"minNotional": 5000.0,
"maxNotional": 25000.0,
"maintenanceMarginRate": 0.025,
"maxLeverage": 20.0,
"info": {
"bracket": "2",
"initialLeverage": "20",
"notionalCap": "25000",
"notionalFloor": "5000",
"maintMarginRatio": "0.025",
"cum": "50.0"
}
},
{
"tier": 3.0,
"currency": "USDT",
"minNotional": 25000.0,
"maxNotional": 100000.0,
"maintenanceMarginRate": 0.05,
"maxLeverage": 10.0,
"info": {
"bracket": "3",
"initialLeverage": "10",
"notionalCap": "100000",
"notionalFloor": "25000",
"maintMarginRatio": "0.05",
"cum": "675.0"
}
},
{
"tier": 4.0,
"currency": "USDT",
"minNotional": 100000.0,
"maxNotional": 250000.0,
"maintenanceMarginRate": 0.1,
"maxLeverage": 5.0,
"info": {
"bracket": "4",
"initialLeverage": "5",
"notionalCap": "250000",
"notionalFloor": "100000",
"maintMarginRatio": "0.1",
"cum": "5675.0"
}
},
{
"tier": 5.0,
"currency": "USDT",
"minNotional": 250000.0,
"maxNotional": 1000000.0,
"maintenanceMarginRate": 0.125,
"maxLeverage": 2.0,
"info": {
"bracket": "5",
"initialLeverage": "2",
"notionalCap": "1000000",
"notionalFloor": "250000",
"maintMarginRatio": "0.125",
"cum": "11925.0"
}
},
{
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386925.0"
}
}
],
"MANA/USDT": [
{
"tier": 1.0,
@ -13028,10 +13404,10 @@
"minNotional": 0.0,
"maxNotional": 5000.0,
"maintenanceMarginRate": 0.01,
"maxLeverage": 50.0,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "50",
"initialLeverage": "25",
"notionalCap": "5000",
"notionalFloor": "0",
"maintMarginRatio": "0.01",
@ -13805,6 +14181,88 @@
}
}
],
"PHB/BUSD": [
{
"tier": 1.0,
"currency": "BUSD",
"minNotional": 0.0,
"maxNotional": 25000.0,
"maintenanceMarginRate": 0.025,
"maxLeverage": 20.0,
"info": {
"bracket": "1",
"initialLeverage": "20",
"notionalCap": "25000",
"notionalFloor": "0",
"maintMarginRatio": "0.025",
"cum": "0.0"
}
},
{
"tier": 2.0,
"currency": "BUSD",
"minNotional": 25000.0,
"maxNotional": 100000.0,
"maintenanceMarginRate": 0.05,
"maxLeverage": 10.0,
"info": {
"bracket": "2",
"initialLeverage": "10",
"notionalCap": "100000",
"notionalFloor": "25000",
"maintMarginRatio": "0.05",
"cum": "625.0"
}
},
{
"tier": 3.0,
"currency": "BUSD",
"minNotional": 100000.0,
"maxNotional": 250000.0,
"maintenanceMarginRate": 0.1,
"maxLeverage": 5.0,
"info": {
"bracket": "3",
"initialLeverage": "5",
"notionalCap": "250000",
"notionalFloor": "100000",
"maintMarginRatio": "0.1",
"cum": "5625.0"
}
},
{
"tier": 4.0,
"currency": "BUSD",
"minNotional": 250000.0,
"maxNotional": 1000000.0,
"maintenanceMarginRate": 0.125,
"maxLeverage": 2.0,
"info": {
"bracket": "4",
"initialLeverage": "2",
"notionalCap": "1000000",
"notionalFloor": "250000",
"maintMarginRatio": "0.125",
"cum": "11875.0"
}
},
{
"tier": 5.0,
"currency": "BUSD",
"minNotional": 1000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "5",
"initialLeverage": "1",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386875.0"
}
}
],
"QTUM/USDT": [
{
"tier": 1.0,
@ -14008,10 +14466,10 @@
"minNotional": 0.0,
"maxNotional": 5000.0,
"maintenanceMarginRate": 0.01,
"maxLeverage": 50.0,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "50",
"initialLeverage": "25",
"notionalCap": "5000",
"notionalFloor": "0",
"maintMarginRatio": "0.01",
@ -14478,13 +14936,13 @@
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 30000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "30000000",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386950.0"
@ -14576,13 +15034,13 @@
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 30000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "30000000",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386950.0"
@ -15487,6 +15945,104 @@
}
}
],
"SPELL/USDT": [
{
"tier": 1.0,
"currency": "USDT",
"minNotional": 0.0,
"maxNotional": 5000.0,
"maintenanceMarginRate": 0.01,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "25",
"notionalCap": "5000",
"notionalFloor": "0",
"maintMarginRatio": "0.01",
"cum": "0.0"
}
},
{
"tier": 2.0,
"currency": "USDT",
"minNotional": 5000.0,
"maxNotional": 25000.0,
"maintenanceMarginRate": 0.025,
"maxLeverage": 20.0,
"info": {
"bracket": "2",
"initialLeverage": "20",
"notionalCap": "25000",
"notionalFloor": "5000",
"maintMarginRatio": "0.025",
"cum": "75.0"
}
},
{
"tier": 3.0,
"currency": "USDT",
"minNotional": 25000.0,
"maxNotional": 100000.0,
"maintenanceMarginRate": 0.05,
"maxLeverage": 10.0,
"info": {
"bracket": "3",
"initialLeverage": "10",
"notionalCap": "100000",
"notionalFloor": "25000",
"maintMarginRatio": "0.05",
"cum": "700.0"
}
},
{
"tier": 4.0,
"currency": "USDT",
"minNotional": 100000.0,
"maxNotional": 250000.0,
"maintenanceMarginRate": 0.1,
"maxLeverage": 5.0,
"info": {
"bracket": "4",
"initialLeverage": "5",
"notionalCap": "250000",
"notionalFloor": "100000",
"maintMarginRatio": "0.1",
"cum": "5700.0"
}
},
{
"tier": 5.0,
"currency": "USDT",
"minNotional": 250000.0,
"maxNotional": 1000000.0,
"maintenanceMarginRate": 0.125,
"maxLeverage": 2.0,
"info": {
"bracket": "5",
"initialLeverage": "2",
"notionalCap": "1000000",
"notionalFloor": "250000",
"maintMarginRatio": "0.125",
"cum": "11950.0"
}
},
{
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386950.0"
}
}
],
"SRM/USDT": [
{
"tier": 1.0,
@ -15585,6 +16141,104 @@
}
}
],
"STG/USDT": [
{
"tier": 1.0,
"currency": "USDT",
"minNotional": 0.0,
"maxNotional": 5000.0,
"maintenanceMarginRate": 0.01,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "25",
"notionalCap": "5000",
"notionalFloor": "0",
"maintMarginRatio": "0.01",
"cum": "0.0"
}
},
{
"tier": 2.0,
"currency": "USDT",
"minNotional": 5000.0,
"maxNotional": 25000.0,
"maintenanceMarginRate": 0.025,
"maxLeverage": 20.0,
"info": {
"bracket": "2",
"initialLeverage": "20",
"notionalCap": "25000",
"notionalFloor": "5000",
"maintMarginRatio": "0.025",
"cum": "75.0"
}
},
{
"tier": 3.0,
"currency": "USDT",
"minNotional": 25000.0,
"maxNotional": 100000.0,
"maintenanceMarginRate": 0.05,
"maxLeverage": 10.0,
"info": {
"bracket": "3",
"initialLeverage": "10",
"notionalCap": "100000",
"notionalFloor": "25000",
"maintMarginRatio": "0.05",
"cum": "700.0"
}
},
{
"tier": 4.0,
"currency": "USDT",
"minNotional": 100000.0,
"maxNotional": 250000.0,
"maintenanceMarginRate": 0.1,
"maxLeverage": 5.0,
"info": {
"bracket": "4",
"initialLeverage": "5",
"notionalCap": "250000",
"notionalFloor": "100000",
"maintMarginRatio": "0.1",
"cum": "5700.0"
}
},
{
"tier": 5.0,
"currency": "USDT",
"minNotional": 250000.0,
"maxNotional": 1000000.0,
"maintenanceMarginRate": 0.125,
"maxLeverage": 2.0,
"info": {
"bracket": "5",
"initialLeverage": "2",
"notionalCap": "1000000",
"notionalFloor": "250000",
"maintMarginRatio": "0.125",
"cum": "11950.0"
}
},
{
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386950.0"
}
}
],
"STMX/USDT": [
{
"tier": 1.0,
@ -16176,13 +16830,13 @@
"tier": 5.0,
"currency": "BUSD",
"minNotional": 1000000.0,
"maxNotional": 30000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "5",
"initialLeverage": "1",
"notionalCap": "30000000",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386875.0"
@ -16470,13 +17124,13 @@
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 30000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "30000000",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386950.0"

9
freqtrade/exchange/exchange.py
View File

@ -21,7 +21,8 @@ from dateutil import parser
from pandas import DataFrame
from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BuySell,
EntryExit, ListPairsWithTimeframes, MakerTaker, PairWithTimeframe)
Config, EntryExit, ListPairsWithTimeframes, MakerTaker,
PairWithTimeframe)
from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
@ -91,7 +92,7 @@ class Exchange:
# TradingMode.SPOT always supported and not required in this list
]
def __init__(self, config: Dict[str, Any], validate: bool = True,
def __init__(self, config: Config, validate: bool = True,
load_leverage_tiers: bool = False) -> None:
"""
Initializes this module with the given config,
@ -108,7 +109,7 @@ class Exchange:
self._loop_lock = Lock()
self.loop = asyncio.new_event_loop()
asyncio.set_event_loop(self.loop)
self._config: Dict = {}
self._config: Config = {}
self._config.update(config)
@ -2304,7 +2305,7 @@ class Exchange:
updated = tiers.get('updated')
if updated:
updated_dt = parser.parse(updated)
if updated_dt < datetime.now(timezone.utc) - timedelta(days=1):
if updated_dt < datetime.now(timezone.utc) - timedelta(weeks=4):
logger.info("Cached leverage tiers are outdated. Will update.")
return None
return tiers['data']

1
freqtrade/exchange/okx.py
View File

@ -71,6 +71,7 @@ class Okx(Exchange):
try:
if self.trading_mode == TradingMode.FUTURES and not self._config['dry_run']:
accounts = self._api.fetch_accounts()
self._log_exchange_response('fetch_accounts', accounts)
if len(accounts) > 0:
self.net_only = accounts[0].get('info', {}).get('posMode') == 'net_mode'
except ccxt.DDoSProtection as e:

19
freqtrade/freqai/data_drawer.py
View File

@ -16,6 +16,7 @@ from numpy.typing import NDArray
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import Config
from freqtrade.data.history import load_pair_history
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
@ -27,9 +28,7 @@ logger = logging.getLogger(__name__)
class pair_info(TypedDict):
model_filename: str
first: bool
trained_timestamp: int
priority: int
data_path: str
extras: dict
@ -58,7 +57,7 @@ class FreqaiDataDrawer:
Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
"""
def __init__(self, full_path: Path, config: dict, follow_mode: bool = False):
def __init__(self, full_path: Path, config: Config, follow_mode: bool = False):
self.config = config
self.freqai_info = config.get("freqai", {})
@ -91,7 +90,7 @@ class FreqaiDataDrawer:
self.old_DBSCAN_eps: Dict[str, float] = {}
self.empty_pair_dict: pair_info = {
"model_filename": "", "trained_timestamp": 0,
"priority": 1, "first": True, "data_path": "", "extras": {}}
"data_path": "", "extras": {}}
self.limit_ram_use = self.freqai_info.get('limit_ram_usage', False)
def load_drawer_from_disk(self):
@ -217,7 +216,6 @@ class FreqaiDataDrawer:
self.pair_dict[pair] = self.empty_pair_dict.copy()
model_filename = ""
trained_timestamp = 0
self.pair_dict[pair]["priority"] = len(self.pair_dict)
if not data_path_set and self.follow_mode:
logger.warning(
@ -237,18 +235,9 @@ class FreqaiDataDrawer:
return
else:
self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy()
self.pair_dict[metadata["pair"]]["priority"] = len(self.pair_dict)
return
def pair_to_end_of_training_queue(self, pair: str) -> None:
# march all pairs up in the queue
with self.pair_dict_lock:
for p in self.pair_dict:
self.pair_dict[p]["priority"] -= 1
# send pair to end of queue
self.pair_dict[pair]["priority"] = len(self.pair_dict)
def set_initial_return_values(self, pair: str, pred_df: DataFrame) -> None:
"""
Set the initial return values to the historical predictions dataframe. This avoids needing
@ -356,7 +345,7 @@ class FreqaiDataDrawer:
for dir in model_folders:
result = pattern.match(str(dir.name))
if result is None:
break
continue
coin = result.group(1)
timestamp = result.group(2)

22
freqtrade/freqai/data_kitchen.py
View File

@ -18,6 +18,7 @@ from sklearn.model_selection import train_test_split
from sklearn.neighbors import NearestNeighbors
from freqtrade.configuration import TimeRange
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.strategy.interface import IStrategy
@ -57,7 +58,7 @@ class FreqaiDataKitchen:
def __init__(
self,
config: Dict[str, Any],
config: Config,
live: bool = False,
pair: str = "",
):
@ -774,12 +775,22 @@ class FreqaiDataKitchen:
def compute_inlier_metric(self, set_='train') -> None:
"""
Compute inlier metric from backwards distance distributions.
This metric defines how well features from a timepoint fit
into previous timepoints.
"""
def normalise(dataframe: DataFrame, key: str) -> DataFrame:
if set_ == 'train':
min_value = dataframe.min()
max_value = dataframe.max()
self.data[f'{key}_min'] = min_value
self.data[f'{key}_max'] = max_value
else:
min_value = self.data[f'{key}_min']
max_value = self.data[f'{key}_max']
return (dataframe - min_value) / (max_value - min_value)
no_prev_pts = self.freqai_config["feature_parameters"]["inlier_metric_window"]
if set_ == 'train':
@ -824,7 +835,12 @@ class FreqaiDataKitchen:
inliers = pd.DataFrame(index=distances.index)
for key in distances.keys():
current_distances = distances[key].dropna()
fit_params = stats.weibull_min.fit(current_distances)
current_distances = normalise(current_distances, key)
if set_ == 'train':
fit_params = stats.weibull_min.fit(current_distances)
self.data[f'{key}_fit_params'] = fit_params
else:
fit_params = self.data[f'{key}_fit_params']
quantiles = stats.weibull_min.cdf(current_distances, *fit_params)
df_inlier = pd.DataFrame(

84
freqtrade/freqai/freqai_interface.py
View File

@ -3,6 +3,7 @@ import shutil
import threading
import time
from abc import ABC, abstractmethod
from collections import deque
from datetime import datetime, timezone
from pathlib import Path
from threading import Lock
@ -14,12 +15,13 @@ from numpy.typing import NDArray
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.constants import DATETIME_PRINT_FORMAT, Config
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.freqai.data_drawer import FreqaiDataDrawer
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.utils import plot_feature_importance
from freqtrade.strategy.interface import IStrategy
@ -50,7 +52,7 @@ class IFreqaiModel(ABC):
Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
"""
def __init__(self, config: Dict[str, Any]) -> None:
def __init__(self, config: Config) -> None:
self.config = config
self.assert_config(self.config)
@ -80,6 +82,7 @@ class IFreqaiModel(ABC):
self.pair_it = 0
self.pair_it_train = 0
self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
self.train_queue = self._set_train_queue()
self.last_trade_database_summary: DataFrame = {}
self.current_trade_database_summary: DataFrame = {}
self.analysis_lock = Lock()
@ -101,7 +104,7 @@ class IFreqaiModel(ABC):
return ({})
self.strategy: Optional[IStrategy] = None
def assert_config(self, config: Dict[str, Any]) -> None:
def assert_config(self, config: Config) -> None:
if not config.get("freqai", {}):
raise OperationalException("No freqai parameters found in configuration file.")
@ -184,29 +187,40 @@ class IFreqaiModel(ABC):
"""
while not self._stop_event.is_set():
time.sleep(1)
for pair in self.config.get("exchange", {}).get("pair_whitelist"):
pair = self.train_queue[0]
(_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
# ensure pair is avaialble in dp
if pair not in strategy.dp.current_whitelist():
self.train_queue.popleft()
logger.warning(f'{pair} not in current whitelist, removing from train queue.')
continue
if self.dd.pair_dict[pair]["priority"] != 1:
continue
dk = FreqaiDataKitchen(self.config, self.live, pair)
dk.set_paths(pair, trained_timestamp)
(
retrain,
new_trained_timerange,
data_load_timerange,
) = dk.check_if_new_training_required(trained_timestamp)
dk.set_paths(pair, new_trained_timerange.stopts)
(_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
if retrain:
self.train_timer('start')
dk = FreqaiDataKitchen(self.config, self.live, pair)
dk.set_paths(pair, trained_timestamp)
(
retrain,
new_trained_timerange,
data_load_timerange,
) = dk.check_if_new_training_required(trained_timestamp)
dk.set_paths(pair, new_trained_timerange.stopts)
if retrain:
self.train_timer('start')
try:
self.extract_data_and_train_model(
new_trained_timerange, pair, strategy, dk, data_load_timerange
)
self.train_timer('stop')
except Exception as msg:
logger.warning(f'Training {pair} raised exception {msg}, skipping.')
self.dd.save_historic_predictions_to_disk()
self.train_timer('stop')
# only rotate the queue after the first has been trained.
self.train_queue.rotate(-1)
self.dd.save_historic_predictions_to_disk()
def start_backtesting(
self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
@ -561,11 +575,11 @@ class IFreqaiModel(ABC):
self.dd.pair_dict[pair]["trained_timestamp"] = new_trained_timerange.stopts
dk.set_new_model_names(pair, new_trained_timerange)
self.dd.pair_dict[pair]["first"] = False
if self.dd.pair_dict[pair]["priority"] == 1 and self.scanning:
self.dd.pair_to_end_of_training_queue(pair)
self.dd.save_data(model, pair, dk)
if self.freqai_info["feature_parameters"].get("plot_feature_importance", False):
plot_feature_importance(model, pair, dk)
if self.freqai_info.get("purge_old_models", False):
self.dd.purge_old_models()
@ -689,6 +703,32 @@ class IFreqaiModel(ABC):
return init_model
def _set_train_queue(self):
"""
Sets train queue from existing train timestamps if they exist
otherwise it sets the train queue based on the provided whitelist.
"""
current_pairlist = self.config.get("exchange", {}).get("pair_whitelist")
if not self.dd.pair_dict:
logger.info('Set fresh train queue from whitelist. '
f'Queue: {current_pairlist}')
return deque(current_pairlist)
best_queue = deque()
pair_dict_sorted = sorted(self.dd.pair_dict.items(),
key=lambda k: k[1]['trained_timestamp'])
for pair in pair_dict_sorted:
if pair[0] in current_pairlist:
best_queue.append(pair[0])
for pair in current_pairlist:
if pair not in best_queue:
best_queue.appendleft(pair)
logger.info('Set existing queue from trained timestamps. '
f'Best approximation queue: {best_queue}')
return best_queue
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModel.py for an example.

85
freqtrade/freqai/prediction_models/XGBoostClassifier.py Normal file
View File

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

69
freqtrade/freqai/utils.py
View File

@ -1,19 +1,25 @@
import logging
from datetime import datetime, timezone
from typing import Any
import numpy as np
import pandas as pd
from freqtrade.configuration import TimeRange
from freqtrade.constants import Config
from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.exchange.exchange import market_is_active
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
logger = logging.getLogger(__name__)
def download_all_data_for_training(dp: DataProvider, config: dict) -> None:
def download_all_data_for_training(dp: DataProvider, config: Config) -> None:
"""
Called only once upon start of bot to download the necessary data for
populating indicators and training the model.
@ -47,9 +53,7 @@ def download_all_data_for_training(dp: DataProvider, config: dict) -> None:
)
def get_required_data_timerange(
config: dict
) -> TimeRange:
def get_required_data_timerange(config: Config) -> TimeRange:
"""
Used to compute the required data download time range
for auto data-download in FreqAI
@ -86,7 +90,7 @@ def get_required_data_timerange(
# Keep below for when we wish to download heterogeneously lengthed data for FreqAI.
# def download_all_data_for_training(dp: DataProvider, config: dict) -> None:
# def download_all_data_for_training(dp: DataProvider, config: Config) -> None:
# """
# Called only once upon start of bot to download the necessary data for
# populating indicators and training a FreqAI model.
@ -132,3 +136,58 @@ def get_required_data_timerange(
# trading_mode=config.get("trading_mode", "spot"),
# prepend=config.get("prepend_data", False),
# )
def plot_feature_importance(model: Any, pair: str, dk: FreqaiDataKitchen,
count_max: int = 25) -> None:
"""
Plot Best and worst features by importance for a single sub-train.
:param model: Any = A model which was `fit` using a common library
such as catboost or lightgbm
:param pair: str = pair e.g. BTC/USD
:param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
:param count_max: int = the amount of features to be loaded per column
"""
from freqtrade.plot.plotting import go, make_subplots, store_plot_file
# Extract feature importance from model
models = {}
if 'FreqaiMultiOutputRegressor' in str(model.__class__):
for estimator, label in zip(model.estimators_, dk.label_list):
models[label] = estimator
else:
models[dk.label_list[0]] = model
for label in models:
mdl = models[label]
if "catboost.core" in str(mdl.__class__):
feature_importance = mdl.get_feature_importance()
elif "lightgbm.sklearn" or "xgb" in str(mdl.__class__):
feature_importance = mdl.feature_importances_
else:
logger.info('Model type not support for generating feature importances.')
return
# Data preparation
fi_df = pd.DataFrame({
"feature_names": np.array(dk.training_features_list),
"feature_importance": np.array(feature_importance)
})
fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1]
fi_df_worst = fi_df.nsmallest(count_max, "feature_importance")[::-1]
# Plotting
def add_feature_trace(fig, fi_df, col):
return fig.add_trace(
go.Bar(
x=fi_df["feature_importance"],
y=fi_df["feature_names"],
orientation='h', showlegend=False
), row=1, col=col
)
fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.5)
fig = add_feature_trace(fig, fi_df_top, 1)
fig = add_feature_trace(fig, fi_df_worst, 2)
fig.update_layout(title_text=f"Best and worst features by importance {pair}")
label = label.replace('&', '').replace('%', '') # escape two FreqAI specific characters
store_plot_file(fig, f"{dk.model_filename}-{label}.html", dk.data_path)

36
freqtrade/freqtradebot.py
View File

@ -11,9 +11,9 @@ from typing import Any, Dict, List, Optional, Tuple
from schedule import Scheduler
from freqtrade import __version__, constants
from freqtrade import constants
from freqtrade.configuration import validate_config_consistency
from freqtrade.constants import BuySell, LongShort
from freqtrade.constants import BuySell, Config, LongShort
from freqtrade.data.converter import order_book_to_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.edge import Edge
@ -44,7 +44,7 @@ class FreqtradeBot(LoggingMixin):
This is from here the bot start its logic.
"""
def __init__(self, config: Dict[str, Any]) -> None:
def __init__(self, config: Config) -> None:
"""
Init all variables and objects the bot needs to work
:param config: configuration dict, you can use Configuration.get_config()
@ -52,8 +52,6 @@ class FreqtradeBot(LoggingMixin):
"""
self.active_pair_whitelist: List[str] = []
logger.info('Starting freqtrade %s', __version__)
# Init bot state
self.state = State.STOPPED
@ -596,7 +594,7 @@ class FreqtradeBot(LoggingMixin):
amount = trade.amount
if amount == 0.0:
logger.info("Amount to sell is 0.0 due to exchange limits - not selling.")
logger.info("Amount to exit is 0.0 due to exchange limits - not exiting.")
return
remaining = (trade.amount - amount) * current_exit_rate
@ -923,7 +921,7 @@ class FreqtradeBot(LoggingMixin):
'stake_amount': trade.stake_amount,
'stake_currency': self.config['stake_currency'],
'fiat_currency': self.config.get('fiat_display_currency', None),
'amount': order.safe_amount_after_fee if fill else order.amount,
'amount': order.safe_amount_after_fee if fill else (order.amount or trade.amount),
'open_date': trade.open_date or datetime.utcnow(),
'current_rate': current_rate,
'sub_trade': sub_trade,
@ -1072,6 +1070,7 @@ class FreqtradeBot(LoggingMixin):
order_obj = Order.parse_from_ccxt_object(stoploss_order, trade.pair, 'stoploss')
trade.orders.append(order_obj)
trade.stoploss_order_id = str(stoploss_order['id'])
trade.stoploss_last_update = datetime.now(timezone.utc)
return True
except InsufficientFundsError as e:
logger.warning(f"Unable to place stoploss order {e}.")
@ -1145,10 +1144,9 @@ class FreqtradeBot(LoggingMixin):
if self.create_stoploss_order(trade=trade, stop_price=stop_price):
# The above will return False if the placement failed and the trade was force-sold.
# in which case the trade will be closed - which we must check below.
trade.stoploss_last_update = datetime.utcnow()
return False
# If stoploss order is canceled for some reason we add it
# If stoploss order is canceled for some reason we add it again
if (trade.is_open
and stoploss_order
and stoploss_order['status'] in ('canceled', 'cancelled')):
@ -1186,7 +1184,8 @@ class FreqtradeBot(LoggingMixin):
if self.exchange.stoploss_adjust(stoploss_norm, order, side=trade.exit_side):
# we check if the update is necessary
update_beat = self.strategy.order_types.get('stoploss_on_exchange_interval', 60)
if (datetime.utcnow() - trade.stoploss_last_update).total_seconds() >= update_beat:
upd_req = datetime.now(timezone.utc) - timedelta(seconds=update_beat)
if trade.stoploss_last_update_utc and upd_req >= trade.stoploss_last_update_utc:
# cancelling the current stoploss on exchange first
logger.info(f"Cancelling current stoploss on exchange for pair {trade.pair} "
f"(orderid:{order['id']}) in order to add another one ...")
@ -1598,14 +1597,14 @@ class FreqtradeBot(LoggingMixin):
# second condition is for mypy only; order will always be passed during sub trade
if sub_trade and order is not None:
amount = order.safe_filled if fill else order.amount
profit_rate = order.safe_price
order_rate: float = order.safe_price
profit = trade.calc_profit(rate=profit_rate, amount=amount, open_rate=trade.open_rate)
profit_ratio = trade.calc_profit_ratio(profit_rate, amount, trade.open_rate)
profit = trade.calc_profit(rate=order_rate, amount=amount, open_rate=trade.open_rate)
profit_ratio = trade.calc_profit_ratio(order_rate, amount, trade.open_rate)
else:
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit = trade.calc_profit(rate=profit_rate) + (0.0 if fill else trade.realized_profit)
profit_ratio = trade.calc_profit_ratio(profit_rate)
order_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit = trade.calc_profit(rate=order_rate) + (0.0 if fill else trade.realized_profit)
profit_ratio = trade.calc_profit_ratio(order_rate)
amount = trade.amount
gain = "profit" if profit_ratio > 0 else "loss"
@ -1618,11 +1617,12 @@ class FreqtradeBot(LoggingMixin):
'leverage': trade.leverage,
'direction': 'Short' if trade.is_short else 'Long',
'gain': gain,
'limit': profit_rate,
'limit': order_rate, # Deprecated
'order_rate': order_rate,
'order_type': order_type,
'amount': amount,
'open_rate': trade.open_rate,
'close_rate': profit_rate,
'close_rate': order_rate,
'current_rate': current_rate,
'profit_amount': profit,
'profit_ratio': profit_ratio,

4
freqtrade/loggers.py
View File

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

2
freqtrade/main.py
View File

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

21
freqtrade/optimize/backtesting.py
View File

@ -15,7 +15,7 @@ from pandas import DataFrame
from freqtrade import constants
from freqtrade.configuration import TimeRange, validate_config_consistency
from freqtrade.constants import DATETIME_PRINT_FORMAT, LongShort
from freqtrade.constants import DATETIME_PRINT_FORMAT, Config, LongShort
from freqtrade.data import history
from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe
from freqtrade.data.converter import trim_dataframe, trim_dataframes
@ -70,7 +70,7 @@ class Backtesting:
backtesting.start()
"""
def __init__(self, config: Dict[str, Any]) -> None:
def __init__(self, config: Config) -> None:
LoggingMixin.show_output = False
self.config = config
@ -812,14 +812,6 @@ class Backtesting:
return trade
time_in_force = self.strategy.order_time_in_force['entry']
if not pos_adjust:
# Confirm trade entry:
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=stake_amount, rate=propose_rate,
time_in_force=time_in_force, current_time=current_time,
entry_tag=entry_tag, side=direction):
return trade
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
self.order_id_counter += 1
base_currency = self.exchange.get_pair_base_currency(pair)
@ -834,6 +826,15 @@ class Backtesting:
# Backcalculate actual stake amount.
stake_amount = amount * propose_rate / leverage
if not pos_adjust:
# Confirm trade entry:
if not strategy_safe_wrapper(
self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=amount, rate=propose_rate,
time_in_force=time_in_force, current_time=current_time,
entry_tag=entry_tag, side=direction):
return trade
is_short = (direction == 'short')
# Necessary for Margin trading. Disabled until support is enabled.
# interest_rate = self.exchange.get_interest_rate()

4
freqtrade/optimize/edge_cli.py
View File

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

123
freqtrade/optimize/hyperopt.py
View File

@ -21,7 +21,7 @@ from joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_
from joblib.externals import cloudpickle
from pandas import DataFrame
from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN
from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN, Config
from freqtrade.data.converter import trim_dataframes
from freqtrade.data.history import get_timerange
from freqtrade.enums import HyperoptState
@ -66,7 +66,7 @@ class Hyperopt:
hyperopt.start()
"""
def __init__(self, config: Dict[str, Any]) -> None:
def __init__(self, config: Config) -> None:
self.buy_space: List[Dimension] = []
self.sell_space: List[Dimension] = []
self.protection_space: List[Dimension] = []
@ -132,7 +132,7 @@ class Hyperopt:
self.print_json = self.config.get('print_json', False)
@staticmethod
def get_lock_filename(config: Dict[str, Any]) -> str:
def get_lock_filename(config: Config) -> str:
return str(config['user_data_dir'] / 'hyperopt.lock')
@ -290,7 +290,7 @@ class Hyperopt:
# noinspection PyProtectedMember
attr.value = params_dict[attr_name]
def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
def generate_optimizer(self, raw_params: List[Any]) -> Dict[str, Any]:
"""
Used Optimize function.
Called once per epoch to optimize whatever is configured.
@ -410,9 +410,11 @@ class Hyperopt:
model_queue_size=SKOPT_MODEL_QUEUE_SIZE,
)
def run_optimizer_parallel(self, parallel, asked, i) -> List:
def run_optimizer_parallel(
self, parallel: Parallel, asked: List[List]) -> List[Dict[str, Any]]:
""" Start optimizer in a parallel way """
return parallel(delayed(
wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
wrap_non_picklable_objects(self.generate_optimizer))(v) for v in asked)
def _set_random_state(self, random_state: Optional[int]) -> int:
return random_state or random.randint(1, 2**16 - 1)
@ -491,6 +493,53 @@ class Hyperopt:
else:
return self.opt.ask(n_points=n_points), [False for _ in range(n_points)]
def get_progressbar_widgets(self):
if self.print_colorized:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
fill_wrap=Fore.GREEN + '{}' + Fore.RESET,
marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL,
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
else:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
return widgets
def evaluate_result(self, val: Dict[str, Any], current: int, is_random: bool):
"""
Evaluate results returned from generate_optimizer
"""
val['current_epoch'] = current
val['is_initial_point'] = current <= INITIAL_POINTS
logger.debug("Optimizer epoch evaluated: %s", val)
is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
# This value is assigned here and not in the optimization method
# to keep proper order in the list of results. That's because
# evaluations can take different time. Here they are aligned in the
# order they will be shown to the user.
val['is_best'] = is_best
val['is_random'] = is_random
self.print_results(val)
if is_best:
self.current_best_loss = val['loss']
self.current_best_epoch = val
self._save_result(val)
def start(self) -> None:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state'))
logger.info(f"Using optimizer random state: {self.random_state}")
@ -526,64 +575,40 @@ class Hyperopt:
logger.info(f'Effective number of parallel workers used: {jobs}')
# Define progressbar
if self.print_colorized:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
fill_wrap=Fore.GREEN + '{}' + Fore.RESET,
marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL,
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
else:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
widgets = self.get_progressbar_widgets()
with progressbar.ProgressBar(
max_value=self.total_epochs, redirect_stdout=False, redirect_stderr=False,
widgets=widgets
) as pbar:
EVALS = ceil(self.total_epochs / jobs)
for i in range(EVALS):
start = 0
if self.analyze_per_epoch:
# First analysis not in parallel mode when using --analyze-per-epoch.
# This allows dataprovider to load it's informative cache.
asked, is_random = self.get_asked_points(n_points=1)
f_val0 = self.generate_optimizer(asked[0])
self.opt.tell(asked, [f_val0['loss']])
self.evaluate_result(f_val0, 1, is_random[0])
pbar.update(1)
start += 1
evals = ceil((self.total_epochs - start) / jobs)
for i in range(evals):
# Correct the number of epochs to be processed for the last
# iteration (should not exceed self.total_epochs in total)
n_rest = (i + 1) * jobs - self.total_epochs
n_rest = (i + 1) * jobs - (self.total_epochs - start)
current_jobs = jobs - n_rest if n_rest > 0 else jobs
asked, is_random = self.get_asked_points(n_points=current_jobs)
f_val = self.run_optimizer_parallel(parallel, asked, i)
f_val = self.run_optimizer_parallel(parallel, asked)
self.opt.tell(asked, [v['loss'] for v in f_val])
# Calculate progressbar outputs
for j, val in enumerate(f_val):
# Use human-friendly indexes here (starting from 1)
current = i * jobs + j + 1
val['current_epoch'] = current
val['is_initial_point'] = current <= INITIAL_POINTS
current = i * jobs + j + 1 + start
logger.debug(f"Optimizer epoch evaluated: {val}")
is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
# This value is assigned here and not in the optimization method
# to keep proper order in the list of results. That's because
# evaluations can take different time. Here they are aligned in the
# order they will be shown to the user.
val['is_best'] = is_best
val['is_random'] = is_random[j]
self.print_results(val)
if is_best:
self.current_best_loss = val['loss']
self.current_best_epoch = val
self._save_result(val)
self.evaluate_result(val, current, is_random[j])
pbar.update(current)

3
freqtrade/optimize/hyperopt_interface.py
View File

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

3
freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py
View File

@ -10,6 +10,7 @@ from typing import Any, Dict
from pandas import DataFrame
from freqtrade.constants import Config
from freqtrade.data.metrics import calculate_max_drawdown
from freqtrade.optimize.hyperopt import IHyperOptLoss
@ -27,7 +28,7 @@ class CalmarHyperOptLoss(IHyperOptLoss):
trade_count: int,
min_date: datetime,
max_date: datetime,
config: Dict,
config: Config,
processed: Dict[str, DataFrame],
backtest_stats: Dict[str, Any],
*args,

5
freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown_relative.py
View File

@ -4,10 +4,9 @@ MaxDrawDownRelativeHyperOptLoss
This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
from typing import Dict
from pandas import DataFrame
from freqtrade.constants import Config
from freqtrade.data.metrics import calculate_underwater
from freqtrade.optimize.hyperopt import IHyperOptLoss
@ -22,7 +21,7 @@ class MaxDrawDownRelativeHyperOptLoss(IHyperOptLoss):
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, config: Dict,
def hyperopt_loss_function(results: DataFrame, config: Config,
*args, **kwargs) -> float:
"""

4
freqtrade/optimize/hyperopt_loss_interface.py
View File

@ -9,6 +9,8 @@ from typing import Any, Dict
from pandas import DataFrame
from freqtrade.constants import Config
class IHyperOptLoss(ABC):
"""
@ -21,7 +23,7 @@ class IHyperOptLoss(ABC):
@abstractmethod
def hyperopt_loss_function(*, results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
config: Dict, processed: Dict[str, DataFrame],
config: Config, processed: Dict[str, DataFrame],
backtest_stats: Dict[str, Any],
**kwargs) -> float:
"""

14
freqtrade/optimize/hyperopt_tools.py
View File

@ -12,7 +12,7 @@ import tabulate
from colorama import Fore, Style
from pandas import isna, json_normalize
from freqtrade.constants import FTHYPT_FILEVERSION, USERPATH_STRATEGIES
from freqtrade.constants import FTHYPT_FILEVERSION, USERPATH_STRATEGIES, Config
from freqtrade.enums import HyperoptState
from freqtrade.exceptions import OperationalException
from freqtrade.misc import deep_merge_dicts, round_coin_value, round_dict, safe_value_fallback2
@ -45,7 +45,7 @@ class HyperoptStateContainer():
class HyperoptTools():
@staticmethod
def get_strategy_filename(config: Dict, strategy_name: str) -> Optional[Path]:
def get_strategy_filename(config: Config, strategy_name: str) -> Optional[Path]:
"""
Get Strategy-location (filename) from strategy_name
"""
@ -81,7 +81,7 @@ class HyperoptTools():
)
@staticmethod
def try_export_params(config: Dict[str, Any], strategy_name: str, params: Dict):
def try_export_params(config: Config, strategy_name: str, params: Dict):
if params.get(FTHYPT_FILEVERSION, 1) >= 2 and not config.get('disableparamexport', False):
# Export parameters ...
fn = HyperoptTools.get_strategy_filename(config, strategy_name)
@ -91,7 +91,7 @@ class HyperoptTools():
logger.warning("Strategy not found, not exporting parameter file.")
@staticmethod
def has_space(config: Dict[str, Any], space: str) -> bool:
def has_space(config: Config, space: str) -> bool:
"""
Tell if the space value is contained in the configuration
"""
@ -131,7 +131,7 @@ class HyperoptTools():
return False
@staticmethod
def load_filtered_results(results_file: Path, config: Dict[str, Any]) -> Tuple[List, int]:
def load_filtered_results(results_file: Path, config: Config) -> Tuple[List, int]:
filteroptions = {
'only_best': config.get('hyperopt_list_best', False),
'only_profitable': config.get('hyperopt_list_profitable', False),
@ -346,7 +346,7 @@ class HyperoptTools():
return trials
@staticmethod
def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
def get_result_table(config: Config, results: list, total_epochs: int, highlight_best: bool,
print_colorized: bool, remove_header: int) -> str:
"""
Log result table
@ -444,7 +444,7 @@ class HyperoptTools():
return table
@staticmethod
def export_csv_file(config: dict, results: list, csv_file: str) -> None:
def export_csv_file(config: Config, results: list, csv_file: str) -> None:
"""
Log result to csv-file
"""

7
freqtrade/optimize/optimize_reports.py
View File

@ -7,7 +7,8 @@ from typing import Any, Dict, List, Union
from pandas import DataFrame, to_datetime
from tabulate import tabulate
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT
from freqtrade.constants import (DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT,
Config)
from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change,
calculate_max_drawdown)
from freqtrade.misc import decimals_per_coin, file_dump_joblib, file_dump_json, round_coin_value
@ -898,7 +899,7 @@ def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency:
print()
def show_backtest_results(config: Dict, backtest_stats: Dict):
def show_backtest_results(config: Config, backtest_stats: Dict):
stake_currency = config['stake_currency']
for strategy, results in backtest_stats['strategy'].items():
@ -918,7 +919,7 @@ def show_backtest_results(config: Dict, backtest_stats: Dict):
print('\nFor more details, please look at the detail tables above')
def show_sorted_pairlist(config: Dict, backtest_stats: Dict):
def show_sorted_pairlist(config: Config, backtest_stats: Dict):
if config.get('backtest_show_pair_list', False):
for strategy, results in backtest_stats['strategy'].items():
print(f"Pairs for Strategy {strategy}: \n[")

9
freqtrade/persistence/trade_model.py
View File

@ -83,7 +83,7 @@ class Order(_DECL_BASE):
@property
def safe_price(self) -> float:
return self.average or self.price
return self.average or self.price or self.stop_price
@property
def safe_filled(self) -> float:
@ -376,6 +376,12 @@ class LocalTrade():
def open_date_utc(self):
return self.open_date.replace(tzinfo=timezone.utc)
@property
def stoploss_last_update_utc(self):
if self.stoploss_last_update:
return self.stoploss_last_update.replace(tzinfo=timezone.utc)
return None
@property
def close_date_utc(self):
return self.close_date.replace(tzinfo=timezone.utc)
@ -560,7 +566,6 @@ class LocalTrade():
self.stop_loss = stop_loss_norm
self.stop_loss_pct = -1 * abs(percent)
self.stoploss_last_update = datetime.utcnow()
def adjust_stop_loss(self, current_price: float, stoploss: float,
initial: bool = False, refresh: bool = False) -> None:

7
freqtrade/plot/plotting.py
View File

@ -1,10 +1,11 @@
import logging
from pathlib import Path
from typing import Any, Dict, List, Optional
from typing import Dict, List, Optional
import pandas as pd
from freqtrade.configuration import TimeRange
from freqtrade.constants import Config
from freqtrade.data.btanalysis import (analyze_trade_parallelism, extract_trades_of_period,
load_trades)
from freqtrade.data.converter import trim_dataframe
@ -618,7 +619,7 @@ def store_plot_file(fig, filename: str, directory: Path, auto_open: bool = False
logger.info(f"Stored plot as {_filename}")
def load_and_plot_trades(config: Dict[str, Any]):
def load_and_plot_trades(config: Config):
"""
From configuration provided
- Initializes plot-script
@ -666,7 +667,7 @@ def load_and_plot_trades(config: Dict[str, Any]):
logger.info('End of plotting process. %s plots generated', pair_counter)
def plot_profit(config: Dict[str, Any]) -> None:
def plot_profit(config: Config) -> None:
"""
Plots the total profit for all pairs.
Note, the profit calculation isn't realistic.

4
freqtrade/plugins/pairlist/AgeFilter.py
View File

@ -8,7 +8,7 @@ from typing import Any, Dict, List, Optional
import arrow
from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.constants import Config, ListPairsWithTimeframes
from freqtrade.exceptions import OperationalException
from freqtrade.misc import plural
from freqtrade.plugins.pairlist.IPairList import IPairList
@ -21,7 +21,7 @@ logger = logging.getLogger(__name__)
class AgeFilter(IPairList):
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
config: Config, pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)

3
freqtrade/plugins/pairlist/IPairList.py
View File

@ -6,6 +6,7 @@ from abc import ABC, abstractmethod, abstractproperty
from copy import deepcopy
from typing import Any, Dict, List
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import Exchange, market_is_active
from freqtrade.mixins import LoggingMixin
@ -17,7 +18,7 @@ logger = logging.getLogger(__name__)
class IPairList(LoggingMixin, ABC):
def __init__(self, exchange: Exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
config: Config, pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
"""
:param exchange: Exchange instance

3
freqtrade/plugins/pairlist/OffsetFilter.py
View File

@ -4,6 +4,7 @@ Offset pair list filter
import logging
from typing import Any, Dict, List
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
from freqtrade.plugins.pairlist.IPairList import IPairList
@ -14,7 +15,7 @@ logger = logging.getLogger(__name__)
class OffsetFilter(IPairList):
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
config: Config, pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)

3
freqtrade/plugins/pairlist/PerformanceFilter.py
View File

@ -6,6 +6,7 @@ from typing import Any, Dict, List
import pandas as pd
from freqtrade.constants import Config
from freqtrade.persistence import Trade
from freqtrade.plugins.pairlist.IPairList import IPairList
@ -16,7 +17,7 @@ logger = logging.getLogger(__name__)
class PerformanceFilter(IPairList):
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
config: Config, pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)

3
freqtrade/plugins/pairlist/PrecisionFilter.py
View File

@ -4,6 +4,7 @@ Precision pair list filter
import logging
from typing import Any, Dict
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
from freqtrade.plugins.pairlist.IPairList import IPairList
@ -14,7 +15,7 @@ logger = logging.getLogger(__name__)
class PrecisionFilter(IPairList):
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
config: Config, pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)

3
freqtrade/plugins/pairlist/PriceFilter.py
View File

@ -4,6 +4,7 @@ Price pair list filter
import logging
from typing import Any, Dict
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
from freqtrade.plugins.pairlist.IPairList import IPairList
@ -14,7 +15,7 @@ logger = logging.getLogger(__name__)
class PriceFilter(IPairList):
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
config: Config, pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)

3
freqtrade/plugins/pairlist/ShuffleFilter.py
View File

@ -5,6 +5,7 @@ import logging
import random
from typing import Any, Dict, List
from freqtrade.constants import Config
from freqtrade.enums import RunMode
from freqtrade.plugins.pairlist.IPairList import IPairList
@ -15,7 +16,7 @@ logger = logging.getLogger(__name__)
class ShuffleFilter(IPairList):
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
config: Config, pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)

3
freqtrade/plugins/pairlist/SpreadFilter.py
View File

@ -4,6 +4,7 @@ Spread pair list filter
import logging
from typing import Any, Dict
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
from freqtrade.plugins.pairlist.IPairList import IPairList
@ -14,7 +15,7 @@ logger = logging.getLogger(__name__)
class SpreadFilter(IPairList):
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
config: Config, pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)

3
freqtrade/plugins/pairlist/StaticPairList.py
View File

@ -7,6 +7,7 @@ import logging
from copy import deepcopy
from typing import Any, Dict, List
from freqtrade.constants import Config
from freqtrade.plugins.pairlist.IPairList import IPairList
@ -16,7 +17,7 @@ logger = logging.getLogger(__name__)
class StaticPairList(IPairList):
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
config: Config, pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)

4
freqtrade/plugins/pairlist/VolatilityFilter.py
View File

@ -11,7 +11,7 @@ import numpy as np
from cachetools import TTLCache
from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.constants import Config, ListPairsWithTimeframes
from freqtrade.exceptions import OperationalException
from freqtrade.misc import plural
from freqtrade.plugins.pairlist.IPairList import IPairList
@ -26,7 +26,7 @@ class VolatilityFilter(IPairList):
"""
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
config: Config, pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)

4
freqtrade/plugins/pairlist/VolumePairList.py
View File

@ -9,7 +9,7 @@ from typing import Any, Dict, List
from cachetools import TTLCache
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.constants import Config, ListPairsWithTimeframes
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date
from freqtrade.misc import format_ms_time
@ -25,7 +25,7 @@ SORT_VALUES = ['quoteVolume']
class VolumePairList(IPairList):
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
config: Config, pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)

6
freqtrade/plugins/pairlist/pairlist_helpers.py
View File

@ -1,5 +1,7 @@
import re
from typing import Any, Dict, List
from typing import List
from freqtrade.constants import Config
def expand_pairlist(wildcardpl: List[str], available_pairs: List[str],
@ -42,7 +44,7 @@ def expand_pairlist(wildcardpl: List[str], available_pairs: List[str],
return result
def dynamic_expand_pairlist(config: Dict[str, Any], markets: List[str]) -> List[str]:
def dynamic_expand_pairlist(config: Config, markets: List[str]) -> List[str]:
expanded_pairs = expand_pairlist(config['pairs'], markets)
if config.get('freqai', {}).get('enabled', False):
corr_pairlist = config['freqai']['feature_parameters']['include_corr_pairlist']

14
freqtrade/plugins/pairlist/rangestabilityfilter.py
View File

@ -9,7 +9,7 @@ import arrow
from cachetools import TTLCache
from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.constants import Config, ListPairsWithTimeframes
from freqtrade.exceptions import OperationalException
from freqtrade.misc import plural
from freqtrade.plugins.pairlist.IPairList import IPairList
@ -21,7 +21,7 @@ logger = logging.getLogger(__name__)
class RangeStabilityFilter(IPairList):
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
config: Config, pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
@ -100,23 +100,19 @@ class RangeStabilityFilter(IPairList):
if cached_res is not None:
return cached_res
result = False
result = True
if daily_candles is not None and not daily_candles.empty:
highest_high = daily_candles['high'].max()
lowest_low = daily_candles['low'].min()
pct_change = ((highest_high - lowest_low) / lowest_low) if lowest_low > 0 else 0
if pct_change >= self._min_rate_of_change:
result = True
else:
if pct_change < self._min_rate_of_change:
self.log_once(f"Removed {pair} from whitelist, because rate of change "
f"over {self._days} {plural(self._days, 'day')} is {pct_change:.3f}, "
f"which is below the threshold of {self._min_rate_of_change}.",
logger.info)
result = False
if self._max_rate_of_change:
if pct_change <= self._max_rate_of_change:
result = True
else:
if pct_change > self._max_rate_of_change:
self.log_once(
f"Removed {pair} from whitelist, because rate of change "
f"over {self._days} {plural(self._days, 'day')} is {pct_change:.3f}, "

4
freqtrade/plugins/pairlistmanager.py
View File

@ -7,7 +7,7 @@ from typing import Dict, List
from cachetools import TTLCache, cached
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.constants import Config, ListPairsWithTimeframes
from freqtrade.enums import CandleType
from freqtrade.exceptions import OperationalException
from freqtrade.mixins import LoggingMixin
@ -21,7 +21,7 @@ logger = logging.getLogger(__name__)
class PairListManager(LoggingMixin):
def __init__(self, exchange, config: dict) -> None:
def __init__(self, exchange, config: Config) -> None:
self._exchange = exchange
self._config = config
self._whitelist = self._config['exchange'].get('pair_whitelist')

4
freqtrade/plugins/protectionmanager.py
View File

@ -5,7 +5,7 @@ import logging
from datetime import datetime, timezone
from typing import Dict, List, Optional
from freqtrade.constants import LongShort
from freqtrade.constants import Config, LongShort
from freqtrade.persistence import PairLocks
from freqtrade.persistence.models import PairLock
from freqtrade.plugins.protections import IProtection
@ -17,7 +17,7 @@ logger = logging.getLogger(__name__)
class ProtectionManager():
def __init__(self, config: Dict, protections: List) -> None:
def __init__(self, config: Config, protections: List) -> None:
self._config = config
self._protection_handlers: List[IProtection] = []

4
freqtrade/plugins/protections/iprotection.py
View File

@ -5,7 +5,7 @@ from dataclasses import dataclass
from datetime import datetime, timedelta, timezone
from typing import Any, Dict, List, Optional
from freqtrade.constants import LongShort
from freqtrade.constants import Config, LongShort
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import plural
from freqtrade.mixins import LoggingMixin
@ -30,7 +30,7 @@ class IProtection(LoggingMixin, ABC):
# Can stop trading for one pair
has_local_stop: bool = False
def __init__(self, config: Dict[str, Any], protection_config: Dict[str, Any]) -> None:
def __init__(self, config: Config, protection_config: Dict[str, Any]) -> None:
self._config = config
self._protection_config = protection_config
self._stop_duration_candles: Optional[int] = None

4
freqtrade/plugins/protections/low_profit_pairs.py
View File

@ -3,7 +3,7 @@ import logging
from datetime import datetime, timedelta
from typing import Any, Dict, Optional
from freqtrade.constants import LongShort
from freqtrade.constants import Config, LongShort
from freqtrade.persistence import Trade
from freqtrade.plugins.protections import IProtection, ProtectionReturn
@ -16,7 +16,7 @@ class LowProfitPairs(IProtection):
has_global_stop: bool = False
has_local_stop: bool = True
def __init__(self, config: Dict[str, Any], protection_config: Dict[str, Any]) -> None:
def __init__(self, config: Config, protection_config: Dict[str, Any]) -> None:
super().__init__(config, protection_config)
self._trade_limit = protection_config.get('trade_limit', 1)

4
freqtrade/plugins/protections/max_drawdown_protection.py
View File

@ -5,7 +5,7 @@ from typing import Any, Dict, Optional
import pandas as pd
from freqtrade.constants import LongShort
from freqtrade.constants import Config, LongShort
from freqtrade.data.metrics import calculate_max_drawdown
from freqtrade.persistence import Trade
from freqtrade.plugins.protections import IProtection, ProtectionReturn
@ -19,7 +19,7 @@ class MaxDrawdown(IProtection):
has_global_stop: bool = True
has_local_stop: bool = False
def __init__(self, config: Dict[str, Any], protection_config: Dict[str, Any]) -> None:
def __init__(self, config: Config, protection_config: Dict[str, Any]) -> None:
super().__init__(config, protection_config)
self._trade_limit = protection_config.get('trade_limit', 1)

4
freqtrade/plugins/protections/stoploss_guard.py
View File

@ -3,7 +3,7 @@ import logging
from datetime import datetime, timedelta
from typing import Any, Dict, Optional
from freqtrade.constants import LongShort
from freqtrade.constants import Config, LongShort
from freqtrade.enums import ExitType
from freqtrade.persistence import Trade
from freqtrade.plugins.protections import IProtection, ProtectionReturn
@ -17,7 +17,7 @@ class StoplossGuard(IProtection):
has_global_stop: bool = True
has_local_stop: bool = True
def __init__(self, config: Dict[str, Any], protection_config: Dict[str, Any]) -> None:
def __init__(self, config: Config, protection_config: Dict[str, Any]) -> None:
super().__init__(config, protection_config)
self._trade_limit = protection_config.get('trade_limit', 10)

3
freqtrade/resolvers/exchange_resolver.py
View File

@ -4,6 +4,7 @@ This module loads custom exchanges
import logging
import freqtrade.exchange as exchanges
from freqtrade.constants import Config
from freqtrade.exchange import MAP_EXCHANGE_CHILDCLASS, Exchange
from freqtrade.resolvers import IResolver
@ -18,7 +19,7 @@ class ExchangeResolver(IResolver):
object_type = Exchange
@staticmethod
def load_exchange(exchange_name: str, config: dict, validate: bool = True,
def load_exchange(exchange_name: str, config: Config, validate: bool = True,
load_leverage_tiers: bool = False) -> Exchange:
"""
Load the custom class from config parameter

5
freqtrade/resolvers/freqaimodel_resolver.py
View File

@ -5,9 +5,8 @@ This module load a custom model for freqai
"""
import logging
from pathlib import Path
from typing import Dict
from freqtrade.constants import USERPATH_FREQAIMODELS
from freqtrade.constants import USERPATH_FREQAIMODELS, Config
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.freqai_interface import IFreqaiModel
from freqtrade.resolvers import IResolver
@ -29,7 +28,7 @@ class FreqaiModelResolver(IResolver):
)
@staticmethod
def load_freqaimodel(config: Dict) -> IFreqaiModel:
def load_freqaimodel(config: Config) -> IFreqaiModel:
"""
Load the custom class from config parameter
:param config: configuration dictionary

5
freqtrade/resolvers/hyperopt_resolver.py
View File

@ -5,9 +5,8 @@ This module load custom hyperopt
"""
import logging
from pathlib import Path
from typing import Dict
from freqtrade.constants import HYPEROPT_LOSS_BUILTIN, USERPATH_HYPEROPTS
from freqtrade.constants import HYPEROPT_LOSS_BUILTIN, USERPATH_HYPEROPTS, Config
from freqtrade.exceptions import OperationalException
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
from freqtrade.resolvers import IResolver
@ -26,7 +25,7 @@ class HyperOptLossResolver(IResolver):
initial_search_path = Path(__file__).parent.parent.joinpath('optimize/hyperopt_loss').resolve()
@staticmethod
def load_hyperoptloss(config: Dict) -> IHyperOptLoss:
def load_hyperoptloss(config: Config) -> IHyperOptLoss:
"""
Load the custom class from config parameter
:param config: configuration dictionary

5
freqtrade/resolvers/iresolver.py
View File

@ -10,6 +10,7 @@ import sys
from pathlib import Path
from typing import Any, Dict, Iterator, List, Optional, Tuple, Type, Union
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
@ -43,7 +44,7 @@ class IResolver:
initial_search_path: Optional[Path]
@classmethod
def build_search_paths(cls, config: Dict[str, Any], user_subdir: Optional[str] = None,
def build_search_paths(cls, config: Config, user_subdir: Optional[str] = None,
extra_dirs: List[str] = []) -> List[Path]:
abs_paths: List[Path] = []
@ -153,7 +154,7 @@ class IResolver:
return None
@classmethod
def load_object(cls, object_name: str, config: dict, *, kwargs: dict,
def load_object(cls, object_name: str, config: Config, *, kwargs: dict,
extra_dir: Optional[str] = None) -> Any:
"""
Search and loads the specified object as configured in hte child class.

3
freqtrade/resolvers/pairlist_resolver.py
View File

@ -6,6 +6,7 @@ This module load custom pairlists
import logging
from pathlib import Path
from freqtrade.constants import Config
from freqtrade.plugins.pairlist.IPairList import IPairList
from freqtrade.resolvers import IResolver
@ -24,7 +25,7 @@ class PairListResolver(IResolver):
@staticmethod
def load_pairlist(pairlist_name: str, exchange, pairlistmanager,
config: dict, pairlistconfig: dict, pairlist_pos: int) -> IPairList:
config: Config, pairlistconfig: dict, pairlist_pos: int) -> IPairList:
"""
Load the pairlist with pairlist_name
:param pairlist_name: Classname of the pairlist

4
freqtrade/resolvers/protection_resolver.py
View File

@ -5,6 +5,7 @@ import logging
from pathlib import Path
from typing import Dict
from freqtrade.constants import Config
from freqtrade.plugins.protections import IProtection
from freqtrade.resolvers import IResolver
@ -22,7 +23,8 @@ class ProtectionResolver(IResolver):
initial_search_path = Path(__file__).parent.parent.joinpath('plugins/protections').resolve()
@staticmethod
def load_protection(protection_name: str, config: Dict, protection_config: Dict) -> IProtection:
def load_protection(protection_name: str, config: Config,
protection_config: Dict) -> IProtection:
"""
Load the protection with protection_name
:param protection_name: Classname of the pairlist

11
freqtrade/resolvers/strategy_resolver.py
View File

@ -9,10 +9,10 @@ from base64 import urlsafe_b64decode
from inspect import getfullargspec
from os import walk
from pathlib import Path
from typing import Any, Dict, List, Optional
from typing import Any, List, Optional
from freqtrade.configuration.config_validation import validate_migrated_strategy_settings
from freqtrade.constants import REQUIRED_ORDERTIF, REQUIRED_ORDERTYPES, USERPATH_STRATEGIES
from freqtrade.constants import REQUIRED_ORDERTIF, REQUIRED_ORDERTYPES, USERPATH_STRATEGIES, Config
from freqtrade.enums import TradingMode
from freqtrade.exceptions import OperationalException
from freqtrade.resolvers import IResolver
@ -32,7 +32,7 @@ class StrategyResolver(IResolver):
initial_search_path = None
@staticmethod
def load_strategy(config: Dict[str, Any] = None) -> IStrategy:
def load_strategy(config: Config = None) -> IStrategy:
"""
Load the custom class from config parameter
:param config: configuration dictionary or None
@ -91,8 +91,7 @@ class StrategyResolver(IResolver):
return strategy
@staticmethod
def _override_attribute_helper(strategy, config: Dict[str, Any],
attribute: str, default: Any):
def _override_attribute_helper(strategy, config: Config, attribute: str, default: Any):
"""
Override attributes in the strategy.
Prevalence:
@ -215,7 +214,7 @@ class StrategyResolver(IResolver):
@staticmethod
def _load_strategy(strategy_name: str,
config: dict, extra_dir: Optional[str] = None) -> IStrategy:
config: Config, extra_dir: Optional[str] = None) -> IStrategy:
"""
Search and loads the specified strategy.
:param strategy_name: name of the module to import

7
freqtrade/rpc/api_server/webserver.py
View File

@ -8,6 +8,7 @@ from fastapi import Depends, FastAPI
from fastapi.middleware.cors import CORSMiddleware
from starlette.responses import JSONResponse
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
from freqtrade.rpc.api_server.uvicorn_threaded import UvicornServer
from freqtrade.rpc.rpc import RPC, RPCException, RPCHandler
@ -37,10 +38,10 @@ class ApiServer(RPCHandler):
_bt = None
_bt_data = None
_bt_timerange = None
_bt_last_config: Dict[str, Any] = {}
_bt_last_config: Config = {}
_has_rpc: bool = False
_bgtask_running: bool = False
_config: Dict[str, Any] = {}
_config: Config = {}
# Exchange - only available in webserver mode.
_exchange = None
@ -54,7 +55,7 @@ class ApiServer(RPCHandler):
ApiServer.__initialized = False
return ApiServer.__instance
def __init__(self, config: Dict[str, Any], standalone: bool = False) -> None:
def __init__(self, config: Config, standalone: bool = False) -> None:
ApiServer._config = config
if self.__initialized and (standalone or self._standalone):
return

4
freqtrade/rpc/discord.py
View File

@ -1,6 +1,6 @@
import logging
from typing import Any, Dict
from freqtrade.constants import Config
from freqtrade.enums import RPCMessageType
from freqtrade.rpc import RPC
from freqtrade.rpc.webhook import Webhook
@ -10,7 +10,7 @@ logger = logging.getLogger(__name__)
class Discord(Webhook):
def __init__(self, rpc: 'RPC', config: Dict[str, Any]):
def __init__(self, rpc: 'RPC', config: Config):
# super().__init__(rpc, config)
self.rpc = rpc
self.config = config

8
freqtrade/rpc/rpc.py
View File

@ -16,7 +16,7 @@ from pandas import DataFrame, NaT
from freqtrade import __version__
from freqtrade.configuration.timerange import TimeRange
from freqtrade.constants import CANCEL_REASON, DATETIME_PRINT_FORMAT
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.enums import (CandleType, ExitCheckTuple, ExitType, SignalDirection, State,
@ -58,7 +58,7 @@ class RPCException(Exception):
class RPCHandler:
def __init__(self, rpc: 'RPC', config: Dict[str, Any]) -> None:
def __init__(self, rpc: 'RPC', config: Config) -> None:
"""
Initializes RPCHandlers
:param rpc: instance of RPC Helper class
@ -66,7 +66,7 @@ class RPCHandler:
:return: None
"""
self._rpc = rpc
self._config: Dict[str, Any] = config
self._config: Config = config
@property
def name(self) -> str:
@ -96,7 +96,7 @@ class RPC:
:return: None
"""
self._freqtrade = freqtrade
self._config: Dict[str, Any] = freqtrade.config
self._config: Config = freqtrade.config
if self._config.get('fiat_display_currency'):
self._fiat_converter = CryptoToFiatConverter()

5
freqtrade/rpc/rpc_manager.py
View File

@ -5,6 +5,7 @@ import logging
from collections import deque
from typing import Any, Dict, List
from freqtrade.constants import Config
from freqtrade.enums import RPCMessageType
from freqtrade.rpc import RPC, RPCHandler
@ -77,6 +78,8 @@ class RPCManager:
mod.send_msg(msg)
except NotImplementedError:
logger.error(f"Message type '{msg['type']}' not implemented by handler {mod.name}.")
except Exception:
logger.exception('Exception occurred within RPC module %s', mod.name)
def process_msg_queue(self, queue: deque) -> None:
"""
@ -89,7 +92,7 @@ class RPCManager:
'msg': msg,
})
def startup_messages(self, config: Dict[str, Any], pairlist, protections) -> None:
def startup_messages(self, config: Config, pairlist, protections) -> None:
if config['dry_run']:
self.send_msg({
'type': RPCMessageType.WARNING,

11
freqtrade/rpc/telegram.py
View File

@ -24,7 +24,7 @@ from telegram.ext import CallbackContext, CallbackQueryHandler, CommandHandler,
from telegram.utils.helpers import escape_markdown
from freqtrade.__init__ import __version__
from freqtrade.constants import DUST_PER_COIN
from freqtrade.constants import DUST_PER_COIN, Config
from freqtrade.enums import RPCMessageType, SignalDirection, TradingMode
from freqtrade.exceptions import OperationalException
from freqtrade.misc import chunks, plural, round_coin_value
@ -88,7 +88,7 @@ def authorized_only(command_handler: Callable[..., None]) -> Callable[..., Any]:
class Telegram(RPCHandler):
""" This class handles all telegram communication """
def __init__(self, rpc: RPC, config: Dict[str, Any]) -> None:
def __init__(self, rpc: RPC, config: Config) -> None:
"""
Init the Telegram call, and init the super class RPCHandler
:param rpc: instance of RPC Helper class
@ -286,7 +286,7 @@ class Telegram(RPCHandler):
if msg['type'] in [RPCMessageType.ENTRY_FILL]:
message += f"*Open Rate:* `{msg['open_rate']:.8f}`\n"
elif msg['type'] in [RPCMessageType.ENTRY]:
message += f"*Open Rate:* `{msg['limit']:.8f}`\n"\
message += f"*Open Rate:* `{msg['open_rate']:.8f}`\n"\
f"*Current Rate:* `{msg['current_rate']:.8f}`\n"
message += f"*Total:* `({round_coin_value(msg['stake_amount'], msg['stake_currency'])}"
@ -353,8 +353,9 @@ class Telegram(RPCHandler):
f"*Open Rate:* `{msg['open_rate']:.8f}`\n"
)
if msg['type'] == RPCMessageType.EXIT:
message += (f"*Current Rate:* `{msg['current_rate']:.8f}`\n"
f"*Exit Rate:* `{msg['limit']:.8f}`")
message += f"*Current Rate:* `{msg['current_rate']:.8f}`\n"
if msg['order_rate']:
message += f"*Exit Rate:* `{msg['order_rate']:.8f}`"
elif msg['type'] == RPCMessageType.EXIT_FILL:
message += f"*Exit Rate:* `{msg['close_rate']:.8f}`"

3
freqtrade/rpc/webhook.py
View File

@ -7,6 +7,7 @@ from typing import Any, Dict
from requests import RequestException, post
from freqtrade.constants import Config
from freqtrade.enums import RPCMessageType
from freqtrade.rpc import RPC, RPCHandler
@ -19,7 +20,7 @@ logger.debug('Included module rpc.webhook ...')
class Webhook(RPCHandler):
""" This class handles all webhook communication """
def __init__(self, rpc: RPC, config: Dict[str, Any]) -> None:
def __init__(self, rpc: RPC, config: Config) -> None:
"""
Init the Webhook class, and init the super class RPCHandler
:param rpc: instance of RPC Helper class

3
freqtrade/strategy/hyper.py
View File

@ -6,6 +6,7 @@ import logging
from pathlib import Path
from typing import Any, Dict, Iterator, List, Tuple, Type, Union
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
from freqtrade.misc import deep_merge_dicts, json_load
from freqtrade.optimize.hyperopt_tools import HyperoptTools
@ -21,7 +22,7 @@ class HyperStrategyMixin:
strategy logic.
"""
def __init__(self, config: Dict[str, Any], *args, **kwargs):
def __init__(self, config: Config, *args, **kwargs):
"""
Initialize hyperoptable strategy mixin.
"""

21
freqtrade/strategy/interface.py
View File

@ -10,7 +10,7 @@ from typing import Dict, List, Optional, Tuple, Union
import arrow
from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.constants import Config, ListPairsWithTimeframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, RunMode, SignalDirection,
SignalTagType, SignalType, TradingMode)
@ -118,7 +118,7 @@ class IStrategy(ABC, HyperStrategyMixin):
# Definition of plot_config. See plotting documentation for more details.
plot_config: Dict = {}
def __init__(self, config: dict) -> None:
def __init__(self, config: Config) -> None:
self.config = config
# Dict to determine if analysis is necessary
self._last_candle_seen_per_pair: Dict[str, datetime] = {}
@ -613,6 +613,22 @@ class IStrategy(ABC, HyperStrategyMixin):
# END - Intended to be overridden by strategy
###
def __informative_pairs_freqai(self) -> ListPairsWithTimeframes:
"""
Create informative-pairs needed for FreqAI
"""
if self.config.get('freqai', {}).get('enabled', False):
whitelist_pairs = self.dp.current_whitelist()
candle_type = self.config.get('candle_type_def', CandleType.SPOT)
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
informative_pairs = []
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
for pair in set(whitelist_pairs + corr_pairs):
informative_pairs.append((pair, tf, candle_type))
return informative_pairs
return []
def gather_informative_pairs(self) -> ListPairsWithTimeframes:
"""
Internal method which gathers all informative pairs (user or automatically defined).
@ -637,6 +653,7 @@ class IStrategy(ABC, HyperStrategyMixin):
else:
for pair in self.dp.current_whitelist():
informative_pairs.append((pair, inf_data.timeframe, candle_type))
informative_pairs.extend(self.__informative_pairs_freqai())
return list(set(informative_pairs))
def get_strategy_name(self) -> str:

49
freqtrade/templates/FreqaiExampleStrategy.py
View File

@ -45,20 +45,7 @@ class FreqaiExampleStrategy(IStrategy):
std_dev_multiplier_buy = CategoricalParameter(
[0.75, 1, 1.25, 1.5, 1.75], default=1.25, space="buy", optimize=True)
std_dev_multiplier_sell = CategoricalParameter(
[0.1, 0.25, 0.4], space="sell", default=0.2, optimize=True)
def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist()
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
informative_pairs = []
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
for pair in whitelist_pairs:
informative_pairs.append((pair, tf))
for pair in corr_pairs:
if pair in whitelist_pairs:
continue # avoid duplication
informative_pairs.append((pair, tf))
return informative_pairs
[0.75, 1, 1.25, 1.5, 1.75], space="sell", default=1.25, optimize=True)
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
@ -183,25 +170,31 @@ class FreqaiExampleStrategy(IStrategy):
dataframe = self.freqai.start(dataframe, metadata, self)
for val in self.std_dev_multiplier_buy.range:
dataframe[f'target_roi_{val}'] = dataframe["&-s_close_mean"] + \
dataframe["&-s_close_std"] * val
dataframe[f'target_roi_{val}'] = (
dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * val
)
for val in self.std_dev_multiplier_sell.range:
dataframe[f'sell_roi_{val}'] = dataframe["&-s_close_mean"] - \
dataframe["&-s_close_std"] * val
dataframe[f'sell_roi_{val}'] = (
dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * val
)
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"]
> df[f"target_roi_{self.std_dev_multiplier_buy.value}"]]
enter_long_conditions = [
df["do_predict"] == 1,
df["&-s_close"] > df[f"target_roi_{self.std_dev_multiplier_buy.value}"],
]
if enter_long_conditions:
df.loc[
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
] = (1, "long")
enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"]
< df[f"sell_roi_{self.std_dev_multiplier_sell.value}"]]
enter_short_conditions = [
df["do_predict"] == 1,
df["&-s_close"] < df[f"sell_roi_{self.std_dev_multiplier_sell.value}"],
]
if enter_short_conditions:
df.loc[
@ -211,13 +204,17 @@ class FreqaiExampleStrategy(IStrategy):
return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] <
df[f"sell_roi_{self.std_dev_multiplier_sell.value}"] * 0.25]
exit_long_conditions = [
df["do_predict"] == 1,
df["&-s_close"] < df[f"sell_roi_{self.std_dev_multiplier_sell.value}"] * 0.25,
]
if exit_long_conditions:
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] >
df[f"target_roi_{self.std_dev_multiplier_buy.value}"] * 0.25]
exit_short_conditions = [
df["do_predict"] == 1,
df["&-s_close"] > df[f"target_roi_{self.std_dev_multiplier_buy.value}"] * 0.25,
]
if exit_short_conditions:
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1

14
freqtrade/templates/FreqaiHybridExampleStrategy.py
View File

@ -95,20 +95,6 @@ class FreqaiExampleHybridStrategy(IStrategy):
short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
# FreqAI required function, leave as is or add additional informatives to existing structure.
def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist()
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
informative_pairs = []
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
for pair in whitelist_pairs:
informative_pairs.append((pair, tf))
for pair in corr_pairs:
if pair in whitelist_pairs:
continue # avoid duplication
informative_pairs.append((pair, tf))
return informative_pairs
# FreqAI required function, user can add or remove indicators, but general structure
# must stay the same.
def populate_any_indicators(

16
freqtrade/templates/base_strategy.py.j2
View File

@ -1,21 +1,21 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame # noqa
from datetime import datetime # noqa
from typing import Optional, Union # noqa
import numpy as np
import pandas as pd
from pandas import DataFrame
from datetime import datetime
from typing import Optional, Union
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)
IntParameter, IStrategy, merge_informative_pair)
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical import qtpylib
class {{ strategy }}(IStrategy):

3
freqtrade/templates/sample_hyperopt_loss.py
View File

@ -4,6 +4,7 @@ from typing import Dict
from pandas import DataFrame
from freqtrade.constants import Config
from freqtrade.optimize.hyperopt import IHyperOptLoss
@ -36,7 +37,7 @@ class SampleHyperOptLoss(IHyperOptLoss):
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
config: Dict, processed: Dict[str, DataFrame],
config: Config, processed: Dict[str, DataFrame],
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for better results

0
freqtrade/templates/subtemplates/buy_trend_full.j2 → freqtrade/templates/strategy_subtemplates/buy_trend_full.j2
View File

0
freqtrade/templates/subtemplates/buy_trend_minimal.j2 → freqtrade/templates/strategy_subtemplates/buy_trend_minimal.j2
View File

0
freqtrade/templates/subtemplates/indicators_full.j2 → freqtrade/templates/strategy_subtemplates/indicators_full.j2
View File

0
freqtrade/templates/subtemplates/indicators_minimal.j2 → freqtrade/templates/strategy_subtemplates/indicators_minimal.j2
View File

0
freqtrade/templates/subtemplates/plot_config_full.j2 → freqtrade/templates/strategy_subtemplates/plot_config_full.j2
View File

0
freqtrade/templates/subtemplates/plot_config_minimal.j2 → freqtrade/templates/strategy_subtemplates/plot_config_minimal.j2
View File

0
freqtrade/templates/subtemplates/sell_trend_full.j2 → freqtrade/templates/strategy_subtemplates/sell_trend_full.j2
View File

0
freqtrade/templates/subtemplates/sell_trend_minimal.j2 → freqtrade/templates/strategy_subtemplates/sell_trend_minimal.j2
View File

0
freqtrade/templates/subtemplates/strategy_methods_advanced.j2 → freqtrade/templates/strategy_subtemplates/strategy_methods_advanced.j2
View File

0
freqtrade/templates/subtemplates/strategy_methods_empty.j2 → freqtrade/templates/strategy_subtemplates/strategy_methods_empty.j2
View File

4
freqtrade/wallets.py
View File

@ -7,7 +7,7 @@ from typing import Dict, NamedTuple, Optional
import arrow
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, Config
from freqtrade.enums import RunMode, TradingMode
from freqtrade.exceptions import DependencyException
from freqtrade.exchange import Exchange
@ -35,7 +35,7 @@ class PositionWallet(NamedTuple):
class Wallets:
def __init__(self, config: dict, exchange: Exchange, log: bool = True) -> None:
def __init__(self, config: Config, exchange: Exchange, log: bool = True) -> None:
self._config = config
self._log = log
self._exchange = exchange

11
freqtrade/worker.py
View File

@ -9,8 +9,9 @@ from typing import Any, Callable, Dict, Optional
import sdnotify
from freqtrade import __version__, constants
from freqtrade import __version__
from freqtrade.configuration import Configuration
from freqtrade.constants import PROCESS_THROTTLE_SECS, RETRY_TIMEOUT, Config
from freqtrade.enums import State
from freqtrade.exceptions import OperationalException, TemporaryError
from freqtrade.freqtradebot import FreqtradeBot
@ -24,7 +25,7 @@ class Worker:
Freqtradebot worker class
"""
def __init__(self, args: Dict[str, Any], config: Dict[str, Any] = None) -> None:
def __init__(self, args: Dict[str, Any], config: Config = None) -> None:
"""
Init all variables and objects the bot needs to work
"""
@ -53,7 +54,7 @@ class Worker:
internals_config = self._config.get('internals', {})
self._throttle_secs = internals_config.get('process_throttle_secs',
constants.PROCESS_THROTTLE_SECS)
PROCESS_THROTTLE_SECS)
self._heartbeat_interval = internals_config.get('heartbeat_interval', 60)
self._sd_notify = sdnotify.SystemdNotifier() if \
@ -151,8 +152,8 @@ class Worker:
try:
self.freqtrade.process()
except TemporaryError as error:
logger.warning(f"Error: {error}, retrying in {constants.RETRY_TIMEOUT} seconds...")
time.sleep(constants.RETRY_TIMEOUT)
logger.warning(f"Error: {error}, retrying in {RETRY_TIMEOUT} seconds...")
time.sleep(RETRY_TIMEOUT)
except OperationalException:
tb = traceback.format_exc()
hint = 'Issue `/start` if you think it is safe to restart.'

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