# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/Cluc4.py
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

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_DerSalvador_freqtrade_helm_chart__Cluc4__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    minimal_roi = {'0': 0.015, '20': 0.005, '30': 0.001}
    stoploss = -0.01
    timeframe = '1m'
    use_exit_signal = True
    exit_profit_only = True
    ignore_roi_if_entry_signal = True

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, '1h') for pair in pairs]
        return informative_pairs

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        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()
        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()
        dataframe['rocr'] = ta.ROCR(dataframe, timeperiod=28)
        inf_tf = '1h'
        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
        informative['rocr'] = ta.ROCR(informative, timeperiod=168)
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[dataframe['rocr_1h'].gt(0.65) & (dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * 0.006) & dataframe['closedelta'].gt(dataframe['close'] * 0.013) & dataframe['tail'].lt(dataframe['bbdelta'] * 0.968) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) | (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.013 * dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume_mean_slow'].shift(1) * 28)), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        """
        #((qtpylib.crossed_above(dataframe['close'],(dataframe['bb_middleband'] * 1.1))) &
        dataframe.loc[qtpylib.crossed_above(dataframe['close'], dataframe['bb_middleband']) & (dataframe['volume'] > 0), 'exit'] = 1
        return dataframe