stable/tests/strategy/strats/freqai_rl_test_strat.py
Wagner Costa abdeb72eb0 fix tests
2023-01-05 17:54:56 -03:00

92 lines
2.7 KiB
Python

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