# source: https://raw.githubusercontent.com/manasipatel090-commits/my-freqtrade-strategy/e6cb6057673cbb31cd2bdbfc9305c8a0c7500b1e/TrendFollowStrategy.py
from freqtrade.strategy import IStrategy, IntParameter, merge_informative_pair
from pandas import DataFrame
import talib.abstract as ta


class Github_manasipatel090_commits_my_freqtrade_strategy__TrendFollowStrategy__20260320_062358(IStrategy):

    INTERFACE_VERSION = 3
    timeframe = "5m"
    can_short = True

    stoploss = -0.02
    trailing_stop = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.015
    trailing_only_offset_is_reached = True

    minimal_roi = {
        "0": 0.05,
        "60": 0.03,
        "120": 0.02
    }

    ema_fast = IntParameter(5, 15, default=9, space="buy")
    ema_slow = IntParameter(18, 30, default=21, space="buy")
    rsi_long_min = IntParameter(35, 55, default=40, space="buy")
    rsi_long_max = IntParameter(60, 75, default=70, space="buy")
    rsi_short_min = IntParameter(45, 55, default=50, space="sell")
    rsi_short_max = IntParameter(60, 70, default=65, space="sell")
    adx_threshold = IntParameter(15, 30, default=20, space="buy")

    startup_candle_count: int = 100

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        return [(pair, "1h") for pair in pairs]

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        # --- 5m indicators ---
        dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=self.ema_fast.value)
        dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=self.ema_slow.value)

        macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
        dataframe["macd"] = macd["macd"]
        dataframe["macd_signal"] = macd["macdsignal"]
        dataframe["macd_hist"] = macd["macdhist"]

        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)

        # --- 1h trend filter ---
        informative_1h = self.dp.get_pair_dataframe(
            pair=metadata["pair"], timeframe="1h"
        )
        informative_1h["ema_1h_fast"] = ta.EMA(informative_1h, timeperiod=9)
        informative_1h["ema_1h_slow"] = ta.EMA(informative_1h, timeperiod=21)
        informative_1h["trend_up"] = (
            informative_1h["ema_1h_fast"] > informative_1h["ema_1h_slow"]
        ).astype(int)

        dataframe = merge_informative_pair(
            dataframe, informative_1h, self.timeframe, "1h", ffill=True
        )

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        # LONG
        dataframe.loc[
            (
                (dataframe["trend_up_1h"] == 1) &
                (dataframe["ema_fast"] > dataframe["ema_slow"]) &
                (dataframe["ema_fast"].shift(1) <= dataframe["ema_slow"].shift(1)) &
                (dataframe["macd_hist"] > 0) &
                (dataframe["macd_hist"].shift(1) <= 0) &
                (dataframe["rsi"] >= self.rsi_long_min.value) &
                (dataframe["rsi"] <= self.rsi_long_max.value) &
                (dataframe["adx"] > self.adx_threshold.value) &
                (dataframe["volume"] > 0)
            ),
            ["enter_long", "enter_tag"]
        ] = 1, "ema_macd_long"

        # SHORT
        dataframe.loc[
            (
                (dataframe["trend_up_1h"] == 0) &
                (dataframe["ema_fast"] < dataframe["ema_slow"]) &
                (dataframe["ema_fast"].shift(1) >= dataframe["ema_slow"].shift(1)) &
                (dataframe["macd_hist"] < 0) &
                (dataframe["macd_hist"].shift(1) >= 0) &
                (dataframe["rsi"] >= self.rsi_short_min.value) &
                (dataframe["rsi"] <= self.rsi_short_max.value) &
                (dataframe["adx"] > self.adx_threshold.value) &
                (dataframe["volume"] > 0)
            ),
            ["enter_short", "enter_tag"]
        ] = 1, "ema_macd_short"

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        return dataframe