XGBoostFreqAIStrategy

"""
XGBoost FreqAI Strategy - Regression (continuous target)
"""
import numpy as np
from freqtrade.strategy import IStrategy
from pandas import DataFrame

class XGBoostFreqAIStrategy(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = "1h"
    can_short = True
    minimal_roi = {"0": 0.1, "240": -1}
    stoploss = -0.07
    startup_candle_count = 240
    max_open_trades = 1

    def feature_engineering_expand_all(self, dataframe, period, metadata, **kwargs):
        dataframe[f"%-rsi_{period}"] = 100 - 100 / (1 + dataframe["close"].pct_change().clip(0).rolling(period).mean() / ((-dataframe["close"].pct_change()).clip(0).rolling(period).mean() + 1e-10))
        dataframe[f"%-z_{period}"] = (dataframe["close"] - dataframe["close"].rolling(period).mean()) / (dataframe["close"].rolling(period).std() + 1e-10)
        dataframe[f"%-ret_{period}"] = dataframe["close"].pct_change(period)
        return dataframe

    def feature_engineering_expand_basic(self, dataframe, metadata, **kwargs):
        dataframe["%-volume"] = dataframe["volume"]
        return dataframe

    def feature_engineering_standard(self, dataframe, metadata, **kwargs):
        return dataframe

    def set_freqai_targets(self, dataframe, **kwargs):
        # Regression target: 24h future return
        dataframe["&-target"] = dataframe["close"].pct_change(24).shift(-24).fillna(0)
        return dataframe

    def populate_indicators(self, dataframe, metadata):
        dataframe = self.freqai.start(dataframe, metadata, self)
        return dataframe

    def populate_entry_trend(self, df, metadata):
        if "do_predict" not in df.columns or "&-target" not in df.columns: return df
        df.loc[(df["do_predict"]==1)&(df["&-target"]>0.005),["enter_long","enter_tag"]]=(1,"long")
        df.loc[(df["do_predict"]==1)&(df["&-target"]<-0.005),["enter_short","enter_tag"]]=(1,"short")
        return df

    def populate_exit_trend(self, df, metadata):
        if "do_predict" not in df.columns or "&-target" not in df.columns: return df
        df.loc[(df["do_predict"]!=1)|(df["&-target"].abs()<0.002),"exit_long"]=1
        df.loc[(df["do_predict"]!=1)|(df["&-target"].abs()<0.002),"exit_short"]=1
        return df