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Update function signatures in all templates

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add typehints to help the user's editor suggest the right things.
This commit is contained in:
Matthias 2023-02-04 20:04:16 +01:00
parent 0dd2472385
commit 801714a588
8 changed files with 67 additions and 45 deletions
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18
freqtrade/strategy/interface.py
View File

@ -614,8 +614,8 @@ class IStrategy(ABC, HyperStrategyMixin):
"""
return df
def feature_engineering_expand_all(self, dataframe: DataFrame,
period: int, **kwargs):
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
@ -634,14 +634,14 @@ class IStrategy(ABC, HyperStrategyMixin):
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
:param dataframe: strategy dataframe which will receive the features
:param period: period of the indicator - usage example:
:param metadata: metadata of current pair
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
"""
return dataframe
def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs):
def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
@ -663,14 +663,14 @@ class IStrategy(ABC, HyperStrategyMixin):
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
:param dataframe: strategy dataframe which will receive the features
:param metadata: metadata of current pair
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
"""
return dataframe
def feature_engineering_standard(self, dataframe: DataFrame, **kwargs):
def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe.
@ -688,13 +688,13 @@ class IStrategy(ABC, HyperStrategyMixin):
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the features
:param dataframe: strategy dataframe which will receive the features
:param metadata: metadata of current pair
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
"""
return dataframe
def set_freqai_targets(self, dataframe, **kwargs):
def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
@ -704,7 +704,7 @@ class IStrategy(ABC, HyperStrategyMixin):
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the targets
:param dataframe: strategy dataframe which will receive the targets
:param metadata: metadata of current pair
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""

22
freqtrade/templates/FreqaiExampleHybridStrategy.py
View File

@ -1,4 +1,5 @@
import logging
from typing import Dict
import numpy as np
import pandas as pd
@ -95,7 +96,8 @@ 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)
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
@ -114,8 +116,9 @@ class FreqaiExampleHybridStrategy(IStrategy):
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
:param dataframe: strategy dataframe which will receive the features
:param period: period of the indicator - usage example:
:param metadata: metadata of current pair
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
"""
@ -148,7 +151,7 @@ class FreqaiExampleHybridStrategy(IStrategy):
return dataframe
def feature_engineering_expand_basic(self, dataframe, **kwargs):
def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
@ -170,7 +173,8 @@ class FreqaiExampleHybridStrategy(IStrategy):
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
:param dataframe: strategy dataframe which will receive the features
:param metadata: metadata of current pair
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
"""
@ -179,7 +183,7 @@ class FreqaiExampleHybridStrategy(IStrategy):
dataframe["%-raw_price"] = dataframe["close"]
return dataframe
def feature_engineering_standard(self, dataframe, **kwargs):
def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe.
@ -197,14 +201,15 @@ class FreqaiExampleHybridStrategy(IStrategy):
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the features
:param dataframe: strategy dataframe which will receive the features
:param metadata: metadata of current pair
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
"""
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
return dataframe
def set_freqai_targets(self, dataframe, **kwargs):
def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
@ -214,7 +219,8 @@ class FreqaiExampleHybridStrategy(IStrategy):
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the targets
:param dataframe: strategy dataframe which will receive the targets
:param metadata: metadata of current pair
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-50) >

22
freqtrade/templates/FreqaiExampleStrategy.py
View File

@ -1,5 +1,6 @@
import logging
from functools import reduce
from typing import Dict
import talib.abstract as ta
from pandas import DataFrame
@ -46,7 +47,8 @@ class FreqaiExampleStrategy(IStrategy):
std_dev_multiplier_sell = CategoricalParameter(
[0.75, 1, 1.25, 1.5, 1.75], space="sell", default=1.25, optimize=True)
def feature_engineering_expand_all(self, dataframe, period, metadata, **kwargs):
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
@ -69,8 +71,9 @@ class FreqaiExampleStrategy(IStrategy):
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
:param dataframe: strategy dataframe which will receive the features
:param period: period of the indicator - usage example:
:param metadata: metadata of current pair
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
"""
@ -103,7 +106,7 @@ class FreqaiExampleStrategy(IStrategy):
return dataframe
def feature_engineering_expand_basic(self, dataframe, metadata, **kwargs):
def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
@ -129,7 +132,8 @@ class FreqaiExampleStrategy(IStrategy):
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
:param dataframe: strategy dataframe which will receive the features
:param metadata: metadata of current pair
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
"""
@ -138,7 +142,7 @@ class FreqaiExampleStrategy(IStrategy):
dataframe["%-raw_price"] = dataframe["close"]
return dataframe
def feature_engineering_standard(self, dataframe, metadata, **kwargs):
def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe.
@ -160,14 +164,15 @@ class FreqaiExampleStrategy(IStrategy):
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the features
:param dataframe: strategy dataframe which will receive the features
:param metadata: metadata of current pair
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
"""
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
return dataframe
def set_freqai_targets(self, dataframe, metadata, **kwargs):
def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
@ -181,7 +186,8 @@ class FreqaiExampleStrategy(IStrategy):
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the targets
:param dataframe: strategy dataframe which will receive the targets
:param metadata: metadata of current pair
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
dataframe["&-s_close"] = (

10
tests/strategy/strats/freqai_rl_test_strat.py
View File

@ -1,5 +1,6 @@
import logging
from functools import reduce
from typing import Dict
import talib.abstract as ta
from pandas import DataFrame
@ -24,20 +25,21 @@ class freqai_rl_test_strat(IStrategy):
startup_candle_count: int = 300
can_short = False
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
metadata: Dict, **kwargs):
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
return dataframe
def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs):
def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
return dataframe
def feature_engineering_standard(self, dataframe, **kwargs):
def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
@ -49,7 +51,7 @@ class freqai_rl_test_strat(IStrategy):
return dataframe
def set_freqai_targets(self, dataframe, **kwargs):
def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
dataframe["&-action"] = 0

10
tests/strategy/strats/freqai_test_classifier.py
View File

@ -1,5 +1,6 @@
import logging
from functools import reduce
from typing import Dict
import numpy as np
import talib.abstract as ta
@ -56,7 +57,8 @@ class freqai_test_classifier(IStrategy):
informative_pairs.append((pair, tf))
return informative_pairs
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
metadata: Dict, **kwargs):
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
@ -64,7 +66,7 @@ class freqai_test_classifier(IStrategy):
return dataframe
def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs):
def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
@ -72,14 +74,14 @@ class freqai_test_classifier(IStrategy):
return dataframe
def feature_engineering_standard(self, dataframe, **kwargs):
def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
return dataframe
def set_freqai_targets(self, dataframe, **kwargs):
def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-100) >
dataframe["close"], 'up', 'down')

10
tests/strategy/strats/freqai_test_multimodel_classifier_strat.py
View File

@ -1,5 +1,6 @@
import logging
from functools import reduce
from typing import Dict
import numpy as np
import talib.abstract as ta
@ -43,7 +44,8 @@ class freqai_test_multimodel_classifier_strat(IStrategy):
)
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
metadata: Dict, **kwargs):
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
@ -51,7 +53,7 @@ class freqai_test_multimodel_classifier_strat(IStrategy):
return dataframe
def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs):
def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
@ -59,14 +61,14 @@ class freqai_test_multimodel_classifier_strat(IStrategy):
return dataframe
def feature_engineering_standard(self, dataframe, **kwargs):
def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
return dataframe
def set_freqai_targets(self, dataframe, **kwargs):
def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-50) >
dataframe["close"], 'up', 'down')

10
tests/strategy/strats/freqai_test_multimodel_strat.py
View File

@ -1,5 +1,6 @@
import logging
from functools import reduce
from typing import Dict
import talib.abstract as ta
from pandas import DataFrame
@ -42,7 +43,8 @@ class freqai_test_multimodel_strat(IStrategy):
)
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
metadata: Dict, **kwargs):
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
@ -50,7 +52,7 @@ class freqai_test_multimodel_strat(IStrategy):
return dataframe
def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs):
def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
@ -58,14 +60,14 @@ class freqai_test_multimodel_strat(IStrategy):
return dataframe
def feature_engineering_standard(self, dataframe, **kwargs):
def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
return dataframe
def set_freqai_targets(self, dataframe, **kwargs):
def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
dataframe["&-s_close"] = (
dataframe["close"]

10
tests/strategy/strats/freqai_test_strat.py
View File

@ -1,5 +1,6 @@
import logging
from functools import reduce
from typing import Dict
import talib.abstract as ta
from pandas import DataFrame
@ -42,7 +43,8 @@ class freqai_test_strat(IStrategy):
)
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
metadata: Dict, **kwargs):
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
@ -50,7 +52,7 @@ class freqai_test_strat(IStrategy):
return dataframe
def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs):
def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
@ -58,14 +60,14 @@ class freqai_test_strat(IStrategy):
return dataframe
def feature_engineering_standard(self, dataframe, **kwargs):
def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
return dataframe
def set_freqai_targets(self, dataframe, **kwargs):
def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
dataframe["&-s_close"] = (
dataframe["close"]