freqtrade_origin/tests/strategy/strats/freqai_test_classifier.py
2024-10-04 07:09:51 +02:00

115 lines
3.8 KiB
Python

import logging
from functools import reduce
import numpy as np
import talib.abstract as ta
from pandas import DataFrame
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy
logger = logging.getLogger(__name__)
class freqai_test_classifier(IStrategy):
"""
Test strategy - used for testing freqAI functionalities.
DO not use in production.
"""
minimal_roi = {"0": 0.1, "240": -1}
plot_config = {
"main_plot": {},
"subplots": {
"prediction": {"prediction": {"color": "blue"}},
"target_roi": {
"target_roi": {"color": "brown"},
},
"do_predict": {
"do_predict": {"color": "brown"},
},
},
}
process_only_new_candles = True
stoploss = -0.05
use_exit_signal = True
startup_candle_count: int = 300
can_short = False
linear_roi_offset = DecimalParameter(
0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
)
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=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
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)
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
return dataframe
def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs):
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
dataframe["%-raw_price"] = dataframe["close"]
return dataframe
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: DataFrame, metadata: dict, **kwargs):
self.freqai.class_names = ["down", "up"]
dataframe["&s-up_or_down"] = np.where(
dataframe["close"].shift(-100) > dataframe["close"], "up", "down"
)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self.freqai_info = self.config["freqai"]
dataframe = self.freqai.start(dataframe, metadata, self)
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [df["&s-up_or_down"] == "up"]
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["&s-up_or_down"] == "down"]
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:
return df