# source: https://raw.githubusercontent.com/TheoBrigitte/freqtrade/82c1c1e1f5f3457fa5376f65124042aff88d7493/strategies/cluc/CombinedBinHAndCluc2021.py
# --- Do not remove these libs ---
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
# --------------------------------
import talib.abstract as ta
from freqtrade.strategy.interface 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 Github_TheoBrigitte_freqtrade__CombinedBinHAndCluc2021__20241203_205711(IStrategy):
    # Based on a backtesting:
    # - the best perfomance is reached with "max_open_trades" = 2 (in average for any market),
    #   so it is better to increase "stake_amount" value rather then "max_open_trades" to get more profit
    # - if the market is constantly green(like in JAN 2018) the best performance is reached with
    #   "max_open_trades" = 2 and minimal_roi = 0.01
    minimal_roi = {
        "0": 0.0888,
        "21": 0.06115,
        "60": 0.02667,
        "80": 0.00
    }
    # minimal_roi = { "0": 0.01 }

    stoploss = -0.09
    timeframe = '5m'

    process_only_new_candles = False

    use_exit_signal = True
    exit_profit_only = False
    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['ema100'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()
        # strategy BBRSI
        dataframe['rsi'] = ta.RSI(dataframe)
        bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=4)
        dataframe['bb_lowerband4'] = bollinger4['lower']
        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['ema100']) &
                    (dataframe['close'] < 0.985 * dataframe['bb_lowerband']) &
                    (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 20))

            ) |
            (  # strategy BBRSI
                    (dataframe['rsi'] < 12) &
                    (dataframe['close'] < dataframe['bb_lowerband4'])
            ),
            'buy'
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        """
        dataframe.loc[
            (
                dataframe['close'] > dataframe['bb_middleband']
            ),
            'sell'
        ] = 1
        return dataframe