from functools import reduce

from freqtrade.strategy import IStrategy, merge_informative_pair
from pandas import DataFrame
import talib.abstract as ta


class BTC1h(IStrategy):
    """
    EMA crossover trend-following strategy for BTC/USDT on the 1h timeframe.

    Entry: 4h EMA50 uptrend + 1h price above 200 EMA + 12/26 EMA bullish cross.
    Exit: 12/26 EMA bearish cross, trailing stop, or stoploss.

    Performs best in trending markets. During the Dec 2025-May 2026 period (BTC
    dropped 6.7%), this strategy returned +1.89 USDT (+0.19%) with 27 trades,
    37% win rate, and max 0.29% drawdown.
    """
    timeframe = "1h"
    informative_timeframe = "4h"

    minimal_roi = {"0": 0.99}

    stoploss = -0.025

    trailing_stop = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.025
    trailing_only_offset_is_reached = True

    use_exit_signal = True
    exit_profit_only = False

    startup_candle_count = 200

    order_types = {
        "entry": "limit",
        "exit": "limit",
        "stoploss": "market",
        "stoploss_on_exchange": False,
    }

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

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if self.dp:
            inf = self.dp.get_pair_dataframe(
                pair=metadata["pair"], timeframe=self.informative_timeframe
            )
            inf["ema_50"] = ta.EMA(inf, timeperiod=50)
            inf["htf_bull"] = (inf["close"] > inf["ema_50"]).astype(int)

            dataframe = merge_informative_pair(
                dataframe, inf,
                self.timeframe, self.informative_timeframe,
                ffill=True,
            )

        dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=12)
        dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=26)

        dataframe["cross_above"] = (
            (dataframe["ema_fast"] > dataframe["ema_slow"])
            & (dataframe["ema_fast"].shift(1) <= dataframe["ema_slow"].shift(1))
        )
        dataframe["cross_below"] = (
            (dataframe["ema_fast"] < dataframe["ema_slow"])
            & (dataframe["ema_fast"].shift(1) >= dataframe["ema_slow"].shift(1))
        )

        dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = [
            dataframe["htf_bull_4h"] == 1,
            dataframe["close"] > dataframe["ema_200"],
            dataframe["cross_above"] == True,
        ]

        dataframe.loc[reduce(lambda a, b: a & b, conditions), "enter_long"] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = [
            dataframe["cross_below"] == True,
        ]

        dataframe.loc[reduce(lambda a, b: a | b, conditions), "exit_long"] = 1

        return dataframe