# source: https://raw.githubusercontent.com/jaredrsommer/freqtradestrategies/9d66f71ce21f2f65feaad6bb967215be22a3cfd6/2Candle%20(3).py from datetime import datetime from freqtrade.persistence import Trade from freqtrade.strategy import IStrategy, informative from pandas import DataFrame import numpy import talib.abstract as ta from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, RealParameter, merge_informative_pair) class Github_jaredrsommer_freqtradestrategies__2Candle__20260518_195114(IStrategy): # Strategy parameters timeframe = "1h" # 5-minute timeframe as per the video example minimal_roi = {} # 1.5% ROI (1:1.5 risk-reward ratio) stoploss = -0.04 # 1% stop loss (adjustable) # Trailing stop: trailing_stop = True # value loaded from strategy trailing_stop_positive = 0.025 # value loaded from strategy trailing_stop_positive_offset = 0.10 # value loaded from strategy trailing_only_offset_is_reached = True # value loaded from strategy buy_threshold = DecimalParameter(3.0, 8.0, default=4.0, decimals=1, space="buy") rsi_threshold = DecimalParameter(40.0, 70.0, default=65.0, decimals=1, space="buy") sell_threshold = DecimalParameter(3.0, 4.0, default=4.0, decimals=1, space="sell") sr_length = IntParameter(12, 50, default=24, space="buy") sr_shift = IntParameter(12, 50, default=24, space="buy") fast_rsi = IntParameter(4, 20, default=12, space="buy") @informative('4h') def populate_indicators_4h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Calculate candle range (high - low) dataframe['range'] = dataframe['high'] - dataframe['low'] dataframe['range_third'] = dataframe['range'] / 3 # Close position: Determine if close is in upper, mid, or lower third dataframe['close_position'] = 0 # Default: mid dataframe.loc[dataframe['close'] > (dataframe['high'] - dataframe['range_third']), 'close_position'] = 1 # High close dataframe.loc[dataframe['close'] < (dataframe['low'] + dataframe['range_third']), 'close_position'] = -1 # Low close # Close comparison: Compare current close to previous candle's range dataframe['prev_high'] = dataframe['high'].shift(1) dataframe['prev_low'] = dataframe['low'].shift(1) dataframe['close_comparison'] = 0 # Default: range dataframe.loc[dataframe['close'] > dataframe['prev_high'], 'close_comparison'] = 1 # Bull candle dataframe.loc[dataframe['close'] < dataframe['prev_low'], 'close_comparison'] = -1 # Bear candle dataframe['CO2'] = ((dataframe['close'] - dataframe['open']) / 2) + dataframe['open'] # Combine close position and close comparison into 9 patterns dataframe['pattern'] = dataframe['close_position'] * 3 + dataframe['close_comparison'] + 4 # Maps to 0-8 (9 patterns) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=6) return dataframe # Define custom variables def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Calculate candle range (high - low) dataframe['range'] = dataframe['high'] - dataframe['low'] dataframe['range_third'] = dataframe['range'] / 3 # Close position: Determine if close is in upper, mid, or lower third dataframe['close_position'] = 0 # Default: mid dataframe.loc[dataframe['close'] > (dataframe['high'] - dataframe['range_third']), 'close_position'] = 1 # High close dataframe.loc[dataframe['close'] < (dataframe['low'] + dataframe['range_third']), 'close_position'] = -1 # Low close # Close comparison: Compare current close to previous candle's range dataframe['prev_high'] = dataframe['high'].shift(1) dataframe['prev_low'] = dataframe['low'].shift(1) dataframe['close_comparison'] = 0 # Default: range dataframe.loc[dataframe['close'] > dataframe['prev_high'], 'close_comparison'] = 1 # Bull candle dataframe.loc[dataframe['close'] < dataframe['prev_low'], 'close_comparison'] = -1 # Bear candle # Combine close position and close comparison into 9 patterns dataframe['pattern'] = dataframe['close_position'] * 3 + dataframe['close_comparison'] + 4 # Maps to 0-8 (9 patterns) dataframe['pattern_avg'] = ((dataframe['pattern'] + dataframe['pattern_4h']) / 2).rolling(2).mean() # Simple support/resistance levels using rolling min/max dataframe['support'] = dataframe['low_4h'].rolling(window=self.sr_length.value).min().shift(self.sr_shift.value) dataframe['resistance'] = dataframe['high_4h'].rolling(window=self.sr_length.value).max().shift(self.sr_shift.value) # 4h S/R dataframe['range_4h'] = dataframe['resistance'] - dataframe['support'] dataframe['inflection'] = (dataframe['range_4h']/2) + dataframe['support'] dataframe['range_third_4h'] = dataframe['range_4h'] / 3 # Close position: Determine if close is in upper, mid, or lower third dataframe['CO2_position'] = 0 # Default: mid dataframe.loc[dataframe['CO2_4h'] > (dataframe['resistance'] - dataframe['range_third_4h']), 'CO2_position'] = 1 # High close dataframe.loc[dataframe['CO2_4h'] < (dataframe['support'] + dataframe['range_third_4h']), 'CO2_position'] = -1 # Low close # Close comparison: Compare current close to previous candle's range dataframe['prev_high_4h'] = dataframe['resistance'].shift(1) dataframe['prev_low_4h'] = dataframe['support'].shift(1) dataframe['close_c'] = 0 dataframe.loc[dataframe['close'] > dataframe['close_4h'].shift(4), 'close_c'] = 1 # Bull candle dataframe.loc[dataframe['close'] < dataframe['close_4h'].shift(4), 'close_c'] = -1 # Bear candle dataframe['rsi'] = ta.RSI(dataframe, timeperiod=self.fast_rsi.value) # # Combine close position and close comparison into 9 patterns dataframe['pattern_CO2'] = dataframe['CO2_position'] * 3 + dataframe['close_c'] + 4 # Maps to 0-8 (9 patterns) dataframe['bull_bear'] = 0 dataframe.loc[dataframe['CO2_4h'] < dataframe['close'], 'bull_bear'] = 1 # High close dataframe.loc[dataframe['CO2_4h'] > dataframe['close'], 'bull_bear'] = -1 # Low close # timestamp = datetime.now().strftime('%Y-%m-%d_%H%M') # pair = metadata['pair'].replace('/', '_') # Replace '/' with '_' for valid filename # filename = f"{pair}_{timestamp}.csv" # dataframe.to_csv(filename, index=True) # logger.info(f"Exported DataFrame for {pair} to {filename}") return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Buy conditions based on Two Candle Theory dataframe.loc[ ( # High close bull candle (pattern 4: most bullish) (dataframe['pattern_avg'] >= self.buy_threshold.value) & (dataframe['pattern_avg'].shift() < 8) & (dataframe['rsi_4h'] < self.rsi_threshold.value) & (dataframe['rsi'] > dataframe['rsi_4h']) # Breakout above resistance # (dataframe['close'] > dataframe['resistance'].shift(1)) & # Previous candle was not bearish (avoid false breakouts) # (dataframe['pattern'].shift(1) != 0) # Not low close bear ), ['enter_long', 'enter_tag']] = (1, 'Pattern and RSI') dataframe.loc[ ( # High close bull candle (pattern 4: most bullish) (dataframe['pattern_avg'] >= self.buy_threshold.value) & (dataframe['pattern_avg'].shift() < 8) & (dataframe['CO2_4h'] > dataframe['resistance']) & (dataframe['CO2_4h'].shift() < dataframe['resistance'].shift()) & (dataframe['rsi_4h'] < self.rsi_threshold.value) & (dataframe['rsi'] > dataframe['rsi_4h']) # Breakout above resistance # (dataframe['close'] > dataframe['resistance'].shift(1)) & # Previous candle was not bearish (avoid false breakouts) # (dataframe['pattern'].shift(1) != 0) # Not low close bear ), ['enter_long', 'enter_tag']] = (1, 'Resistance Cross') dataframe.loc[ ( # High close bull candle (pattern 4: most bullish) (dataframe['pattern_avg'] >= self.buy_threshold.value) & # (dataframe['pattern_avg'].shift() < 8) & (dataframe['CO2_4h'] > dataframe['support']) & (dataframe['CO2_4h'].shift() < dataframe['support'].shift()) # (dataframe['rsi_4h'] < self.rsi_threshold.value) # (dataframe['rsi'] > dataframe['rsi_4h']) # Breakout above resistance # (dataframe['close'] > dataframe['resistance'].shift(1)) & # Previous candle was not bearish (avoid false breakouts) # (dataframe['pattern'].shift(1) != 0) # Not low close bear ), ['enter_long', 'enter_tag']] = (1, 'Support Cross') dataframe.loc[ ( # High close bull candle (pattern 4: most bullish) (dataframe['pattern_avg'] >= self.buy_threshold.value) & # (dataframe['pattern_avg'].shift() < 8) & (dataframe['CO2_4h'] < dataframe['support']) & (dataframe['CO2_4h'].shift(4) < dataframe['CO2_4h']) & # (dataframe['rsi_4h'] < self.rsi_threshold.value) (dataframe['rsi'] > dataframe['rsi_4h']) # Breakout above resistance # (dataframe['close'] > dataframe['resistance'].shift(1)) & # Previous candle was not bearish (avoid false breakouts) # (dataframe['pattern'].shift(1) != 0) # Not low close bear ), ['enter_long', 'enter_tag']] = (1, 'Below Support 4h pull up') dataframe.loc[ ( # High close bull candle (pattern 4: most bullish) # (dataframe['pattern_avg'] >= self.buy_threshold.value) & # (dataframe['pattern_avg'].shift() < 8) & (dataframe['CO2_4h'] < dataframe['support']) & (dataframe['rsi_4h'] < 10) & (dataframe['rsi'] > dataframe['rsi_4h']) # Breakout above resistance # (dataframe['close'] > dataframe['resistance'].shift(1)) & # Previous candle was not bearish (avoid false breakouts) # (dataframe['pattern'].shift(1) != 0) # Not low close bear ), ['enter_long', 'enter_tag']] = (1, '4h Extreme RSI') # # Alternative buy: High close bull after support bounce # dataframe.loc[ # ( # (dataframe['pattern'] == 4) & # (dataframe['close'] > dataframe['support']) & # (dataframe['pattern'].shift(1) == 0) # Previous was low close bear # ), # 'enter_long'] = 1 # # Sell conditions (short entry) # dataframe.loc[ # ( # # Low close bear candle (pattern 0: most bearish) # (dataframe['pattern'] == 0) & # # Breakdown below support # (dataframe['close'] < dataframe['support'].shift(1)) & # # Previous candle was not bullish (avoid false breakdowns) # (dataframe['pattern'].shift(1) != 4) # Not high close bull # ), # 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Exit long on strong bearish signal # dataframe.loc[ # # (dataframe['enter_long'].shift(1) == 1) & # (dataframe['pattern_avg'] <= self.sell_threshold.value), # Low close bear candle # 'exit_long'] = 1 # # Exit short on strong bullish signal # dataframe.loc[ # (dataframe['enter_short'].shift(1) == 1) & # (dataframe['pattern'] == 4), # High close bull candle # 'exit_short'] = 1 dataframe.loc[ ( # High close bull candle (pattern 4: most bullish) # (dataframe['pattern_avg'] >= self.buy_threshold.value) & # (dataframe['pattern_avg'].shift() < 8) & (dataframe['CO2_4h'] > dataframe['resistance']) & (dataframe['close'] > dataframe['resistance']) & (dataframe['close'] < dataframe['CO2_4h']) & (dataframe['rsi_4h'] > 85) # (dataframe['rsi'] > dataframe['rsi_4h']) # Breakout above resistance # (dataframe['close'] > dataframe['resistance'].shift(1)) & # Previous candle was not bearish (avoid false breakouts) # (dataframe['pattern'].shift(1) != 0) # Not low close bear ), ['exit_long', 'exit_tag']] = (1, 'Above Resistance 4h') # dataframe.loc[ # ( # # High close bull candle (pattern 4: most bullish) # # (dataframe['pattern_avg'] >= self.buy_threshold.value) & # # (dataframe['pattern_avg'].shift() < 8) & # (dataframe['CO2_4h'] < dataframe['resistance']) & # (dataframe['CO2_4h'].shift() > dataframe['resistance'].shift()) # # (dataframe['rsi'] > dataframe['rsi_4h']) # # Breakout above resistance # # (dataframe['close'] > dataframe['resistance'].shift(1)) & # # Previous candle was not bearish (avoid false breakouts) # # (dataframe['pattern'].shift(1) != 0) # Not low close bear # ), # ['exit_long', 'exit_tag']] = (1, 'Resistance Cross') # dataframe.loc[ # ( # # High close bull candle (pattern 4: most bullish) # # (dataframe['pattern_avg'] >= self.buy_threshold.value) & # # (dataframe['pattern_avg'].shift() < 8) & # (dataframe['CO2_4h'] < dataframe['support']) & # (dataframe['CO2_4h'].shift() > dataframe['support'].shift()) # # (dataframe['rsi'] > dataframe['rsi_4h']) # # Breakout above resistance # # (dataframe['close'] > dataframe['resistance'].shift(1)) & # # Previous candle was not bearish (avoid false breakouts) # # (dataframe['pattern'].shift(1) != 0) # Not low close bear # ), # ['exit_long', 'exit_tag']] = (1, 'Below Support 4h cross') return dataframe