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https://github.com/freqtrade/freqtrade.git
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92 lines
2.7 KiB
Python
92 lines
2.7 KiB
Python
import logging
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from functools import reduce
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import talib.abstract as ta
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from pandas import DataFrame
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from freqtrade.strategy import IStrategy
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logger = logging.getLogger(__name__)
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class freqai_rl_test_strat(IStrategy):
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"""
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Test strategy - used for testing freqAI functionalities.
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DO not use in production.
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"""
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minimal_roi = {"0": 0.1, "240": -1}
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process_only_new_candles = True
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stoploss = -0.05
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use_exit_signal = True
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startup_candle_count: int = 300
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can_short = False
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def feature_engineering_expand_all(self, dataframe, period, **kwargs):
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dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
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return dataframe
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def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs):
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dataframe["%-pct-change"] = dataframe["close"].pct_change()
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dataframe["%-raw_volume"] = dataframe["volume"]
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return dataframe
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def feature_engineering_standard(self, dataframe, **kwargs):
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dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
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dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
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dataframe["%-raw_close"] = dataframe["close"]
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dataframe["%-raw_open"] = dataframe["open"]
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dataframe["%-raw_high"] = dataframe["high"]
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dataframe["%-raw_low"] = dataframe["low"]
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return dataframe
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def set_freqai_targets(self, dataframe, **kwargs):
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dataframe["&-action"] = 0
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe = self.freqai.start(dataframe, metadata, self)
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return dataframe
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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enter_long_conditions = [df["do_predict"] == 1, df["&-action"] == 1]
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if enter_long_conditions:
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df.loc[
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reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
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] = (1, "long")
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enter_short_conditions = [df["do_predict"] == 1, df["&-action"] == 3]
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if enter_short_conditions:
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df.loc[
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reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
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] = (1, "short")
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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exit_long_conditions = [df["do_predict"] == 1, df["&-action"] == 2]
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if exit_long_conditions:
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df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
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exit_short_conditions = [df["do_predict"] == 1, df["&-action"] == 4]
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if exit_short_conditions:
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df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
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return df
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