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CombinedBinHClucAndMADV5

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remiotore/ccxt-freqtrade/strategies/CombinedBinHClucAndMADV5_converted.py · ★3 · ⑂2 · first seen 2026-07-28 · repo updated 2026-01-11

Basics mode: spot timeframe: 5m interface version: 3 1h
Settings stoploss: -0.99 has minimal roi trailing custom stoploss process only new candles: false startup candle count: 200
Indicators ATR Bollinger_Bands EMA RSI talib
Concepts trailing
Methods SSLChannels custom_stoploss informative_1h_indicators normal_tf_indicators
15 related strategies (⧉ identical code, ≈ similar name)

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

def SSLChannels(dataframe, length=7):
    df = dataframe.copy()
    df['ATR'] = ta.ATR(df, timeperiod=14)
    df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR']
    df['smaLow'] = df['low'].rolling(length).mean() - df['ATR']
    df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.nan))
    df['hlv'] = df['hlv'].ffill()
    df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
    df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])
    return (df['sslDown'], df['sslUp'])

class CombinedBinHClucAndMADV5(IStrategy):
    INTERFACE_VERSION = 3
    minimal_roi = {'0': 0.021, '40': 0.005}
    stoploss = -0.99  # effectively disabled.
    timeframe = '5m'
    inf_1h = '1h'
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.001  # it doesn't meant anything, just to guarantee there is a minimal profit.
    ignore_roi_if_entry_signal = False
    trailing_stop = False
    trailing_only_offset_is_reached = False
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.025
    use_custom_stoploss = True
    process_only_new_candles = False
    startup_candle_count: int = 200
    order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False}

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        if (current_profit < 0) & (current_time - timedelta(minutes=240) > trade.open_date_utc):
            return 0.01
        return 0.99

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

    def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        assert self.dp, 'DataProvider is required for multiple timeframes.'
        informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
        informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
        informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
        informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
        ssl_down_1h, ssl_up_1h = SSLChannels(informative_1h, 20)
        informative_1h['ssl_down'] = ssl_down_1h
        informative_1h['ssl_up'] = ssl_up_1h
        return informative_1h

    def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)
        dataframe['lower'] = bb_40['lower']
        dataframe['mid'] = bb_40['mid']
        dataframe['bbdelta'] = (bb_40['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['bb_upperband'] = bollinger['upper']
        dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()
        dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
        dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
        dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
        dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        informative_1h = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)
        dataframe = self.normal_tf_indicators(dataframe, metadata)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  # strategy ClucMay72018
        # Guard is on, candle should dig not so hard (0,99)
        # strategy ClucMay72018 
        # Guard is off, candle should dig hard (0,975) 
        # Don't buy if someone drop the market.
        # Buy only at dip
        # Make sure Volume is not 0
        # strategy MACD Low buy 
        # Don't buy if someone drop the market.
        # Make sure Volume is not 0
        # strategy MACD Low buy 
        # Don't buy if someone drop the market.
        # Make sure Volume is not 0
        dataframe.loc[(dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.99 * dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume_mean_slow'].shift(1) * 21) & (dataframe['volume'] > 0) | (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.975 * dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume_mean_slow'].shift(1) * 20) & (dataframe['volume'] < dataframe['volume'].shift() * 4) & (dataframe['rsi_1h'] < 15) & (dataframe['volume'] > 0) | (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * 0.02) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['volume'] < dataframe['volume'].shift() * 4) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume'] > 0) | (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * 0.03) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['volume'] < dataframe['volume'].shift() * 4) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume'] > 0) | (dataframe['close'] < dataframe['sma_5']) & (dataframe['ssl_up_1h'] > dataframe['ssl_down_1h']) & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['rsi'] < dataframe['rsi_1h'] - 43.276) & (dataframe['volume'] > 0), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  # Don't be gready, sell fast
        # Make sure Volume is not 0
        dataframe.loc[(dataframe['close'] > dataframe['bb_middleband'] * 1.01) & (dataframe['volume'] > 0), 'exit_long'] = 1
        return dataframe