# source: https://raw.githubusercontent.com/jaredrsommer/freqtradestrategies/9d66f71ce21f2f65feaad6bb967215be22a3cfd6/TwoCandle.py from freqtrade.strategy import IStrategy from pandas import DataFrame import pandas_ta as ta from technical import qtpylib class Github_jaredrsommer_freqtradestrategies__TwoCandle__20260518_195114(IStrategy): # Strategy parameters timeframe = "1h" # 5-minute timeframe as per the video example minimal_roi = {"0": 0.015, "15": 0.012, "30": 0.05, "60": 0.025} # 1.5% ROI (1:1.5 risk-reward ratio) stoploss = -0.03 # 1% stop loss (adjustable) # Trailing stop trailing_stop = True trailing_stop_positive = 0.013 trailing_stop_positive_offset = 0.035 trailing_only_offset_is_reached = True plot_config = { "main_plot": { "support": { "color": "green", "type": "line" }, "resistance": { "color": "red", "type": "line" } }, "subplots": { "close": { "close_position": { "color": "#76f639", "type": "bar" }, "close_comparison": { "color": "#dedcf9", "type": "line" } }, "pattern": { "pattern": { "color": "#165e70", "type": "bar" }, "pattern diff": { "color": "#744bce", "type": "bar" } } } } # Define custom variables def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] # Calculate candle range (high - low) dataframe['range'] = dataframe['high'] - dataframe['low'] dataframe['range_third'] = dataframe['range'] / 3 # Calculate candle body (close - open) dataframe['body'] = abs(dataframe['close'] - dataframe['open']) dataframe['body_third'] = dataframe['body'] / 3 # Simple support/resistance levels using rolling min/max dataframe['support'] = dataframe['low'].rolling(window=80).min().shift(80) dataframe['resistance'] = dataframe['high'].rolling(window=80).max().shift(80) dataframe['midpoint'] = ((dataframe['resistance']-dataframe['support'])/2) + dataframe['support'] dataframe['trading_range'] = abs(dataframe['support'] - dataframe['resistance']) / 8 # 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['hl_close_comparison'] = 0 # Default: range dataframe.loc[dataframe['close'] > dataframe['prev_high'], 'hl_close_comparison'] = 1 # Bull candle dataframe.loc[dataframe['close'] < dataframe['prev_low'], 'hl_close_comparison'] = -1 # Bear candle # Close position: Determine if close is in upper, mid, or lower third dataframe['close_body_position'] = 0 # Default: mid dataframe.loc[dataframe['close'] > (dataframe['high'] - dataframe['range_third']), 'close_body_position'] = 1 # High close dataframe.loc[dataframe['close'] < (dataframe['low'] + dataframe['range_third']), 'close_body_position'] = -1 # Low close # Close comparison: Compare current close to previous candle's range dataframe['prev_close'] = dataframe['close'].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['hl_close_comparison'] + 4 # Maps to 0-8 (9 patterns) dataframe['body_pattern'] = dataframe['close_body_position'] * 3 + dataframe['close_comparison'] + 4 # Maps to 0-8 (9 patterns) dataframe['pattern_mean'] = dataframe['pattern'].rolling(2).mean() dataframe['pattern_trend'] = dataframe['pattern'].rolling(9).mean() dataframe['pattern_trend_ma'] = dataframe['support'] + (dataframe['pattern_trend'] * dataframe['trading_range']) dataframe['pattern diff'] = dataframe['pattern'].diff() 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'] == 4) & # # Breakout above resistance # (dataframe['close'] > dataframe['resistance'].shift(1)) & # (dataframe['pattern_mean'] >= 4) & # # Previous candle was not bearish (avoid false breakouts) # (dataframe['pattern'].shift(1) != 0) # Not low close bear # ), # ['enter_long', 'enter_tag']] = (1, 'long 1') # dataframe.loc[ # ( # # High close bull candle (pattern 4: most bullish) # (dataframe['pattern'] == 8) & # # Breakout above resistance # (dataframe['pattern_mean'] >= 4) & # # Previous candle was not bearish (avoid false breakouts) # (dataframe['pattern'].shift(1) == 0) # Not low close bear # ), # ['enter_long', 'enter_tag']] = (1, 'long 3') # dataframe.loc[ # ( # # High close bull candle (pattern 4: most bullish) # (dataframe['pattern'] <= 7) & # # Breakout above resistance # (dataframe['pattern_mean'] >= 4) & # # Previous candle was not bearish (avoid false breakouts) # (dataframe['pattern'].shift(1) <= 0) # Not low close bear # ), # ['enter_long', 'enter_tag']] = (1, 'long 4') # dataframe.loc[ # ( # # High close bull candle (pattern 4: most bullish) # (dataframe['pattern'] <= 6) & # # Breakout above resistance # (dataframe['pattern_mean'] >= 4) & # # Previous candle was not bearish (avoid false breakouts) # (dataframe['pattern'].shift(1) <= 0) # Not low close bear # ), # ['enter_long', 'enter_tag']] = (1, 'long 5') # Alternative buy: High close bull after support bounce dataframe.loc[ ( (dataframe['pattern_mean'].shift() < 4) & (dataframe['pattern_mean'].shift() < 4) & (dataframe['pattern_mean'] > 4) # (dataframe['close'] > dataframe['support']) & # (dataframe['pattern'].shift(1) == 0) # Previous was low close bear ), ['enter_long', 'enter_tag']] = (1, 'long 2') # Track bias (simplified as a rolling score) dataframe['bias'] = 0 # Neutral dataframe.loc[dataframe['pattern'] == 4, 'bias'] = 1 # Bullish on high close bull dataframe.loc[dataframe['pattern'] == 0, 'bias'] = -1 # Bearish on low close bear dataframe['bias'] = dataframe['bias'].rolling(window=3).mean().fillna(0) # 3-candle bias # # Bullish entries # dataframe.loc[ # # High close bull at breakout # ((dataframe['pattern'] == 4) & (dataframe['close'] > dataframe['resistance'].shift(1))) | # # High close bull after support bounce # ((dataframe['pattern'] == 4) & (dataframe['pattern'].shift(1) == 0) & (dataframe['close'] > dataframe['support'])) | # # Two consecutive high close bull candles # ((dataframe['pattern'] == 4) & (dataframe['pattern'].shift(1) == 4) & (dataframe['bias'] > 0)) | # # High close range after pullback in uptrend # ((dataframe['pattern'] == 1) & (dataframe['bias'] > 0) & (dataframe['close'] > dataframe['support'])), # 'enter_long'] = 1 # # Bearish entries # dataframe.loc[ # # Low close bear at breakdown # ((dataframe['pattern'] == 0) & (dataframe['close'] < dataframe['support'].shift(1))) | # # Two consecutive low close bear candles # ((dataframe['pattern'] == 0) & (dataframe['pattern'].shift(1) == 0) & (dataframe['bias'] < 0)) | # # Low close bear after resistance rejection # ((dataframe['pattern'] == 0) & (dataframe['close'] < dataframe['resistance']) & (dataframe['bias'] < 0)), # 'enter_short'] = 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[ ( # High close bull candle (pattern 4: most bullish) (dataframe['pattern'] >= 0) & # Breakout above resistance (dataframe['pattern_mean'] < 3.5) & # Previous candle was not bearish (avoid false breakouts) (dataframe['pattern'].shift(1) == 8) # Not low close bear ), ['exit_long', 'exit_tag']] = (1, 'short1') dataframe.loc[ ( # High close bull candle (pattern 4: most bullish) (dataframe['pattern'] >= 0) & # Breakout above resistance3.5 (dataframe['pattern_mean'] < 3.5) & # Previous candle was not bearish (avoid false breakouts) (dataframe['pattern'].shift(1) == 7) # Not low close bear ), ['exit_long', 'exit_tag']] = (1, 'short2') dataframe.loc[ ( # High close bull candle (pattern 4: most bullish) (dataframe['pattern'] >= 0) & # Breakout above resistance (dataframe['pattern_mean'] < 3.5) & # Previous candle was not bearish (avoid false breakouts) (dataframe['pattern'].shift(1) == 6) # Not low close bear ), ['exit_long', 'exit_tag']] = (1, 'short1') dataframe.loc[ ( (dataframe['pattern_mean'].shift() > 4) & (dataframe['pattern_mean'] < 4) ), ['exit_long', 'exit_tag']] = (1, 'exit 2') # # Bearish entries # dataframe.loc[ # # Low close bear at breakdown # ((dataframe['pattern'] == 0) & (dataframe['close'] < dataframe['support'].shift(1))) | # # Two consecutive low close bear candles # ((dataframe['pattern'] == 0) & (dataframe['pattern'].shift(1) == 0) & (dataframe['bias'] < 0)) | # # Low close bear after resistance rejection # ((dataframe['pattern'] == 0) & (dataframe['close'] < dataframe['resistance']) & (dataframe['bias'] < 0)), # '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 return dataframe