from functools import reduce

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


class BTC1h(IStrategy):
    # 1-hour timeframe
    timeframe = "1h"

    # Higher timeframe for trend filter
    informative_timeframe = "4h"

    # ROI table (0 = latest candle)
    minimal_roi = {
        "0": 0.10,
        "120": 0.05,
        "360": 0.03,
        "720": 0,
    }

    stoploss = -0.05

    trailing_stop = False
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.03
    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,
    }

    # --- Hyperoptable parameters ---
    ema_short = IntParameter(20, 50, default=34, space="buy")
    ema_long = IntParameter(100, 200, default=144, space="buy")
    rsi_entry = IntParameter(25, 45, default=35, space="buy")
    rsi_exit = IntParameter(60, 80, default=70, space="sell")

    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:
        # --- Higher timeframe trend filter ---
        if self.dp:
            informative = self.dp.get_pair_dataframe(
                pair=metadata["pair"], timeframe=self.informative_timeframe
            )
            informative["ema_200"] = ta.EMA(informative, timeperiod=200)
            informative["htf_bull"] = (
                informative["close"] > informative["ema_200"]
            ).astype(int)

            # Merge HTF data into 1h dataframe
            dataframe = dataframe.merge(
                informative[["date", "htf_bull"]], on="date", how="left"
            )
            dataframe["htf_bull"] = dataframe["htf_bull"].ffill().fillna(0)
        else:
            dataframe["htf_bull"] = 1

        # --- EMAs ---
        dataframe["ema_short"] = ta.EMA(dataframe, timeperiod=self.ema_short.value)
        dataframe["ema_long"] = ta.EMA(dataframe, timeperiod=self.ema_long.value)

        # --- RSI ---
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)

        # --- MACD ---
        macd = ta.MACD(dataframe)
        dataframe["macd"] = macd["macd"]
        dataframe["macd_signal"] = macd["macdsignal"]
        dataframe["macd_hist"] = macd["macdhist"]

        # --- Volume ---
        dataframe["volume_ma"] = ta.SMA(dataframe, timeperiod=20)

        # --- ATR ---
        dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = [
            # 4h trend is bullish
            dataframe["htf_bull"] == 1,
            # Price above long-term EMA
            dataframe["close"] > dataframe["ema_long"],
            # Pullback near short-term EMA
            dataframe["close"] < dataframe["ema_short"] * 1.02,
            # RSI dip
            dataframe["rsi"] < self.rsi_entry.value,
            # MACD turning up
            dataframe["macd_hist"] > dataframe["macd_hist"].shift(1),
            # Volume confirmation
            dataframe["volume"] > dataframe["volume_ma"],
        ]

        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 = [
            # RSI overbought
            dataframe["rsi"] > self.rsi_exit.value,
            # MACD bearish cross
            (
                (dataframe["macd"] < dataframe["macd_signal"])
                & (dataframe["macd"].shift(1) > dataframe["macd_signal"].shift(1))
            ),
        ]

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

        return dataframe