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


def bollinger_bands(stock_price, window_size, num_of_std):
    rolling_mean = stock_price.rolling(window=window_size).mean()
    rolling_std = stock_price.rolling(window=window_size).std()
    lower_band = rolling_mean - (rolling_std * num_of_std)
    return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band)


class CombinedBinHAndCluc(IStrategy):
    # Best with max_open_trades = 2
    INTERFACE_VERSION: int = 3
    minimal_roi = {
        "0": 0.05
    }
    stoploss = -0.05
    timeframe = '5m'

    use_exit_signal = True
    exit_profit_only = True
    ignore_roi_if_entry_signal = False

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # strategy BinHV45
        mid, lower = bollinger_bands(dataframe['close'], window_size=40, num_of_std=2)
        dataframe['lower'] = lower
        dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
        # strategy ClucMay72018
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()

        # --- Regime Detection ---
        # ATR regime — detect crash/high volatility periods
        dataframe['regime_atr_14'] = ta.ATR(dataframe, timeperiod=14)
        dataframe['regime_atr_sma_50'] = dataframe['regime_atr_14'].rolling(50).mean()
        dataframe['regime_volatile'] = (dataframe['regime_atr_14'] > 2.0 * dataframe['regime_atr_sma_50']).astype(int)

        # ADX regime — detect trending vs ranging
        dataframe['regime_adx_14'] = ta.ADX(dataframe, timeperiod=14)
        dataframe['regime_trending'] = (dataframe['regime_adx_14'] > 25).astype(int)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            ((  # strategy BinHV45
                    dataframe['lower'].shift().gt(0) &
                    dataframe['bbdelta'].gt(dataframe['close'] * 0.008) &
                    dataframe['closedelta'].gt(dataframe['close'] * 0.0175) &
                    dataframe['tail'].lt(dataframe['bbdelta'] * 0.25) &
                    dataframe['close'].lt(dataframe['lower'].shift()) &
                    dataframe['close'].le(dataframe['close'].shift())
            ) |
            (  # strategy ClucMay72018
                    (dataframe['close'] < dataframe['ema_slow']) &
                    (dataframe['close'] < 0.985 * dataframe['bb_lowerband']) &
                    (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 20))
            )) &
            (dataframe['regime_volatile'] == 0) &  # Don't enter during high volatility
            (dataframe['regime_trending'] == 0),  # Mean reversion: only enter in ranging markets (ADX < 25)
            'enter_long'
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe['close'] > dataframe['bb_middleband']) |
            (dataframe['regime_atr_14'] > 2.5 * dataframe['regime_atr_sma_50']),  # Exit on volatility spike
            'exit_long'
        ] = 1
        return dataframe