# source: https://raw.githubusercontent.com/Zer0phucks/trading-lab/90a6eef67476f5a442f148722ac2f56de8ee2fd1/user_data/strategies/FreqaiTrendV1.py
from __future__ import annotations

from pandas import DataFrame
import talib.abstract as ta

from freqtrade.strategy import DecimalParameter, IStrategy


class Github_Zer0phucks_trading_lab__FreqaiTrendV1__20260505_075652(IStrategy):
    INTERFACE_VERSION = 3

    timeframe = "5m"
    can_short = False
    process_only_new_candles = True
    startup_candle_count = 240

    minimal_roi = {"0": 0.04, "120": 0.01, "360": 0.0}
    stoploss = -0.08
    trailing_stop = True
    trailing_stop_positive = 0.015
    trailing_stop_positive_offset = 0.035
    trailing_only_offset_is_reached = True

    prediction_entry = DecimalParameter(0.004, 0.025, default=0.01, decimals=3, space="buy")
    prediction_exit = DecimalParameter(-0.020, 0.004, default=-0.004, decimals=3, space="sell")

    def feature_engineering_expand_all(
        self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
    ) -> DataFrame:
        dataframe[f"%-rsi-{period}"] = ta.RSI(dataframe, timeperiod=period)
        dataframe[f"%-mfi-{period}"] = ta.MFI(dataframe, timeperiod=period)
        dataframe[f"%-adx-{period}"] = ta.ADX(dataframe, timeperiod=period)
        dataframe[f"%-ema-distance-{period}"] = (
            dataframe["close"] / ta.EMA(dataframe, timeperiod=period) - 1.0
        )
        return dataframe

    def feature_engineering_standard(
        self, dataframe: DataFrame, metadata: dict, **kwargs
    ) -> DataFrame:
        dataframe["%-pct-change"] = dataframe["close"].pct_change()
        dataframe["%-raw-volume"] = dataframe["volume"]
        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) -> DataFrame:
        label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
        dataframe["&-s_close"] = (
            dataframe["close"].shift(-label_period) / dataframe["close"] - 1.0
        )
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe = self.freqai.start(dataframe, metadata, self)
        dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)
        dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["do_predict"] == 1)
                & (dataframe["&-s_close"] > self.prediction_entry.value)
                & (dataframe["ema_50"] > dataframe["ema_200"])
                & (dataframe["rsi"] > 45)
            ),
            "enter_long",
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["do_predict"] != 1)
                | (dataframe["&-s_close"] < self.prediction_exit.value)
                | (dataframe["close"] < dataframe["ema_50"])
            ),
            "exit_long",
        ] = 1
        return dataframe