# Auto-generated by Strategy Lab — Auto-Quant Factory (Momentum / EMA Crossover Edition)
# Entry: EMA fast/slow crossover filtered by ATR > rolling median (trending markets only).
# Optimise with: freqtrade hyperopt --strategy IntradayMomentum_v1 --spaces buy roi stoploss

from freqtrade.strategy import IntParameter, IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np


class IntradayMomentum_v1(IStrategy):
    INTERFACE_VERSION: int = 3

    minimal_roi = {
        "0": 0.08,
        "30": 0.04,
        "60": 0.02,
        "120": 0,
    }

    stoploss = -0.05
    timeframe = "5m"
    trailing_stop = False
    process_only_new_candles = True

    # ── Tunable EMA periods ───────────────────────────────────────────────────
    ema_fast   = IntParameter(5,  20, default=9,  space="buy", optimize=True)
    ema_slow   = IntParameter(15, 50, default=21, space="buy", optimize=True)

    # ── ATR rolling-median window (volatility gate) ───────────────────────────
    atr_window = IntParameter(10, 50, default=14, space="buy", optimize=True)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Dynamic EMA pair — recalculated for the current parameter values
        dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=self.ema_fast.value)
        dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=self.ema_slow.value)

        # ATR(14) and its rolling median for regime gating
        dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
        dataframe["atr_median"] = (
            dataframe["atr"]
            .rolling(window=self.atr_window.value, min_periods=1)
            .median()
        )

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # EMA crossover in a trending (high-ATR) market only
        dataframe.loc[
            (
                qtpylib.crossed_above(dataframe["ema_fast"], dataframe["ema_slow"])
                & (dataframe["atr"] > dataframe["atr_median"])
                & (dataframe["volume"] > 0)
            ),
            "enter_long",
        ] = 1
        return dataframe

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