# source: https://raw.githubusercontent.com/MaoBui2907/freqtrade-strategy-analysis/8727737e03425e269872c9d7e972bbf326292bb6/server/strategies/CombinedBinHAndCluc.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_MaoBui2907_freqtrade_strategy_analysis__CombinedBinHAndCluc__20250617_150144(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": 0.05}
    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()

        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))
            ),
            "entry",
        ] = 1
        return dataframe

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