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


class Cluc7werk(IStrategy):

    INTERFACE_VERSION = 3

    can_short = True

    # Buy hyperspace params:
    buy_params = {
        "bbdelta-close": 0.00732,
        "bbdelta-tail": 0.94138,
        "close-bblower": 0.0199,
        "closedelta-close": 0.01825,
        "fisher": -0.22987,
        "volume": 16,
    }

    # Sell hyperspace params:
    sell_params = {"sell-bbmiddle-close": 0.99184, "sell-fisher": 0.26832}

    # Short entry hyperspace params (inverted long logic):
    short_params = {
        "short-fisher": 0.22987,  # Overbought Fisher-RSI threshold (inverted from long)
        "short-close-bbupper": 0.0199,  # Distance above upper BB2
        "short-bbdelta-close": 0.00732,  # BB delta threshold
        "short-closedelta-close": 0.01825,
        "short-bbdelta-tail": 0.94138,
        "short-volume": 16,
    }

    # Short exit hyperspace params:
    short_exit_params = {
        "exit-short-bbmiddle-close": 1.00816,  # Inverted from sell-bbmiddle-close
        "exit-short-fisher": -0.26832,  # Inverted from sell-fisher
    }

    # ROI table:
    minimal_roi = {"0": 0.15373, "14": 0.1105, "57": 0.08376, "147": 0.03427, "201": 0.01352, "366": 0.00667, "469": 0}

    # Stoploss:
    stoploss = -0.02

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.01007
    trailing_stop_positive_offset = 0.01258
    trailing_only_offset_is_reached = False

    timeframe = "1m"

    startup_candle_count: int = 72

    # Make sure these match or are not overridden in config
    use_exit_signal = True
    exit_profit_only = True
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = True

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

        # Set Up Bollinger Bands
        upper_bb1, mid_bb1, lower_bb1 = ta.BBANDS(dataframe["close"], timeperiod=40)
        upper_bb2, mid_bb2, lower_bb2 = ta.BBANDS(qtpylib.typical_price(dataframe), timeperiod=20)

        dataframe["lower-bb1"] = lower_bb1
        dataframe["upper-bb1"] = upper_bb1
        dataframe["lower-bb2"] = lower_bb2
        dataframe["upper-bb2"] = upper_bb2
        dataframe["mid-bb2"] = mid_bb2

        dataframe["bb1-delta"] = (mid_bb1 - dataframe["lower-bb1"]).abs()
        dataframe["closedelta"] = (dataframe["close"] - dataframe["close"].shift()).abs()
        dataframe["tail"] = (dataframe["close"] - dataframe["low"]).abs()
        dataframe["upper-tail"] = (dataframe["high"] - dataframe["close"]).abs()

        dataframe["ema_fast"] = ta.EMA(dataframe["close"], timeperiod=6)
        dataframe["ema_slow"] = ta.EMA(dataframe["close"], timeperiod=48)
        dataframe["volume_mean_slow"] = dataframe["volume"].rolling(window=24).mean()

        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=9)

        # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
        rsi = 0.1 * (dataframe["rsi"] - 50)
        dataframe["fisher-rsi"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        params = self.buy_params
        short_params = self.short_params

        # Long entries: oversold conditions (Fisher-RSI < threshold, price near lower BB)
        dataframe.loc[
            (dataframe["fisher-rsi"].lt(params["fisher"]))
            & (
                (
                    dataframe["bb1-delta"].gt(dataframe["close"] * params["bbdelta-close"])
                    & dataframe["closedelta"].gt(dataframe["close"] * params["closedelta-close"])
                    & dataframe["tail"].lt(dataframe["bb1-delta"] * params["bbdelta-tail"])
                    & dataframe["close"].lt(dataframe["lower-bb1"].shift())
                    & dataframe["close"].le(dataframe["close"].shift())
                )
                | (
                    (dataframe["close"] < dataframe["ema_slow"])
                    & (dataframe["close"] < params["close-bblower"] * dataframe["lower-bb2"])
                    & (dataframe["volume"] < (dataframe["volume_mean_slow"].shift(1) * params["volume"]))
                )
            ),
            "enter_long",
        ] = 1

        # Short entries: overbought conditions (Fisher-RSI > threshold, price near upper BB)
        dataframe.loc[
            (dataframe["fisher-rsi"].gt(short_params["short-fisher"]))
            & (
                (
                    dataframe["bb1-delta"].gt(dataframe["close"] * short_params["short-bbdelta-close"])
                    & dataframe["closedelta"].gt(dataframe["close"] * short_params["short-closedelta-close"])
                    & dataframe["upper-tail"].lt(dataframe["bb1-delta"] * short_params["short-bbdelta-tail"])
                    & dataframe["close"].gt(dataframe["upper-bb1"].shift())
                    & dataframe["close"].ge(dataframe["close"].shift())
                )
                | (
                    (dataframe["close"] > dataframe["ema_slow"])
                    & (dataframe["close"] > (1 + short_params["short-close-bbupper"]) * dataframe["upper-bb2"])
                    & (dataframe["volume"] < (dataframe["volume_mean_slow"].shift(1) * short_params["short-volume"]))
                )
            ),
            "enter_short",
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        params = self.sell_params
        short_exit_params = self.short_exit_params

        # Exit long: mean reversion above mid BB + overbought Fisher-RSI
        dataframe.loc[
            ((dataframe["close"] * params["sell-bbmiddle-close"]) > dataframe["mid-bb2"])
            & dataframe["ema_fast"].gt(dataframe["close"])
            & dataframe["fisher-rsi"].gt(params["sell-fisher"])
            & dataframe["volume"].gt(0),
            "exit_long",
        ] = 1

        # Exit short: mean reversion below mid BB + oversold Fisher-RSI
        dataframe.loc[
            ((dataframe["close"] * short_exit_params["exit-short-bbmiddle-close"]) < dataframe["mid-bb2"])
            & dataframe["ema_fast"].lt(dataframe["close"])
            & dataframe["fisher-rsi"].lt(short_exit_params["exit-short-fisher"])
            & dataframe["volume"].gt(0),
            "exit_short",
        ] = 1

        return dataframe