# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/QBitrain.py from freqtrade.strategy.hyper import CategoricalParameter, DecimalParameter, IntParameter from freqtrade.strategy.interface import IStrategy from pandas import DataFrame import talib.abstract as ta from functools import reduce AllLearntKnowledges = { 'Overlap Studies': { 'BBANDS-0', # Bollinger Bands 'BBANDS-1', # Bollinger Bands 'BBANDS-2', # Bollinger Bands 'DEMA', # Double Exponential Moving Average 'EMA', # Exponential Moving Average 'HT_TRENDLINE', # Hilbert Transform - Instantaneous Trendline 'KAMA', # Kaufman Adaptive Moving Average 'MA', # Moving average 'MAMA-0', # MESA Adaptive Moving Average 'MAMA-1', # MESA Adaptive Moving Average 'MIDPOINT', # MidPoint over period 'MIDPRICE', # Midpoint Price over period 'SAR', # Parabolic SAR 'SAREXT', # Parabolic SAR - Extended 'SMA', # Simple Moving Average 'T3', # Triple Exponential Moving Average (T3) 'TEMA', # Triple Exponential Moving Average 'TRIMA', # Triangular Moving Average 'WMA', # Weighted Moving Average }, 'Momentum Indicators': { 'ADX', # Average Directional Movement Index 'ADXR', # Average Directional Movement Index Rating 'APO', # Absolute Price Oscillator 'AROON-0', # Aroon 'AROON-1', # Aroon 'AROONOSC', # Aroon Oscillator 'BOP', # Balance Of Power 'CCI', # Commodity Channel Index 'CMO', # Chande Momentum Oscillator 'DX', # Directional Movement Index 'MACD-0', # Moving Average Convergence/Divergence 'MACD-1', # Moving Average Convergence/Divergence 'MACD-2', # Moving Average Convergence/Divergence 'MACDEXT-0', # MACD with controllable MA type 'MACDEXT-1', # MACD with controllable MA type 'MACDEXT-2', # MACD with controllable MA type 'MACDFIX-0', # Moving Average Convergence/Divergence Fix 12/26 'MACDFIX-1', # Moving Average Convergence/Divergence Fix 12/26 'MACDFIX-2', # Moving Average Convergence/Divergence Fix 12/26 'MFI', # Money Flow Index 'MINUS_DI', # Minus Directional Indicator 'MINUS_DM', # Minus Directional Movement 'MOM', # Momentum 'PLUS_DI', # Plus Directional Indicator 'PLUS_DM', # Plus Directional Movement 'PPO', # Percentage Price Oscillator 'ROC', # Rate of change : ((price/prevPrice)-1)*100 'ROCP', 'ROCR', # Rate of change ratio: (price/prevPrice) 'ROCR100', 'RSI', # Relative Strength Index 'STOCH-0', # Stochastic 'STOCH-1', # Stochastic 'STOCHF-0', # Stochastic Fast 'STOCHF-1', # Stochastic Fast 'STOCHRSI-0', # Stochastic Relative Strength Index 'STOCHRSI-1', # Stochastic Relative Strength Index 'TRIX', 'ULTOSC', # Ultimate Oscillator 'WILLR', # Williams' %R }, 'Volume Indicators': { 'AD', # Chaikin A/D Line 'ADOSC', # Chaikin A/D Oscillator 'OBV', # On Balance Volume }, 'Volatility Indicators': { 'ATR', # Average True Range 'NATR', # Normalized Average True Range 'TRANGE', # True Range }, 'Price Transform': { 'AVGPRICE', # Average Price 'MEDPRICE', # Median Price 'TYPPRICE', # Typical Price 'WCLPRICE', # Weighted Close Price }, 'Cycle Indicators': { 'HT_DCPERIOD', # Hilbert Transform - Dominant Cycle Period 'HT_DCPHASE', # Hilbert Transform - Dominant Cycle Phase 'HT_PHASOR-0', # Hilbert Transform - Phasor Components 'HT_PHASOR-1', # Hilbert Transform - Phasor Components 'HT_SINE-0', # Hilbert Transform - SineWave 'HT_SINE-1', # Hilbert Transform - SineWave 'HT_TRENDMODE', # Hilbert Transform - Trend vs Cycle Mode }, 'Pattern Recognition': { 'CDL2CROWS', # Two Crows 'CDL3BLACKCROWS', # Three Black Crows 'CDL3INSIDE', # Three Inside Up/Down 'CDL3LINESTRIKE', # Three-Line Strike 'CDL3OUTSIDE', # Three Outside Up/Down 'CDL3STARSINSOUTH', # Three Stars In The South 'CDL3WHITESOLDIERS', # Three Advancing White Soldiers 'CDLABANDONEDBABY', # Abandoned Baby 'CDLADVANCEBLOCK', # Advance Block 'CDLBELTHOLD', # Belt-hold 'CDLBREAKAWAY', # Breakaway 'CDLCLOSINGMARUBOZU', # Closing Marubozu 'CDLCONCEALBABYSWALL', # Concealing Baby Swallow 'CDLCOUNTERATTACK', # Counterattack 'CDLDARKCLOUDCOVER', # Dark Cloud Cover 'CDLDOJI', # Doji 'CDLDOJISTAR', # Doji Star 'CDLDRAGONFLYDOJI', # Dragonfly Doji 'CDLENGULFING', # Engulfing Pattern 'CDLEVENINGDOJISTAR', # Evening Doji Star 'CDLEVENINGSTAR', # Evening Star 'CDLGAPSIDESIDEWHITE', # Up/Down-gap side-by-side white lines 'CDLGRAVESTONEDOJI', # Gravestone Doji 'CDLHAMMER', # Hammer 'CDLHANGINGMAN', # Hanging Man 'CDLHARAMI', # Harami Pattern 'CDLHARAMICROSS', # Harami Cross Pattern 'CDLHIGHWAVE', # High-Wave Candle 'CDLHIKKAKE', # Hikkake Pattern 'CDLHIKKAKEMOD', # Modified Hikkake Pattern 'CDLHOMINGPIGEON', # Homing Pigeon 'CDLIDENTICAL3CROWS', # Identical Three Crows 'CDLINNECK', # In-Neck Pattern 'CDLINVERTEDHAMMER', # Inverted Hammer 'CDLKICKING', # Kicking 'CDLKICKINGBYLENGTH', # Kicking - bull/bear determined by the longer marubozu 'CDLLADDERBOTTOM', # Ladder Bottom 'CDLLONGLEGGEDDOJI', # Long Legged Doji 'CDLLONGLINE', # Long Line Candle 'CDLMARUBOZU', # Marubozu 'CDLMATCHINGLOW', # Matching Low 'CDLMATHOLD', # Mat Hold 'CDLMORNINGDOJISTAR', # Morning Doji Star 'CDLMORNINGSTAR', # Morning Star 'CDLONNECK', # On-Neck Pattern 'CDLPIERCING', # Piercing Pattern 'CDLRICKSHAWMAN', # Rickshaw Man 'CDLRISEFALL3METHODS', # Rising/Falling Three Methods 'CDLSEPARATINGLINES', # Separating Lines 'CDLSHOOTINGSTAR', # Shooting Star 'CDLSHORTLINE', # Short Line Candle 'CDLSPINNINGTOP', # Spinning Top 'CDLSTALLEDPATTERN', # Stalled Pattern 'CDLSTICKSANDWICH', # Stick Sandwich 'CDLTAKURI', 'CDLTASUKIGAP', # Tasuki Gap 'CDLTHRUSTING', # Thrusting Pattern 'CDLTRISTAR', # Tristar Pattern 'CDLUNIQUE3RIVER', # Unique 3 River 'CDLUPSIDEGAP2CROWS', # Upside Gap Two Crows 'CDLXSIDEGAP3METHODS', # Upside/Downside Gap Three Methods }, 'Statistic Functions': { 'BETA', # Beta 'CORREL', # Pearson's Correlation Coefficient (r) 'LINEARREG', # Linear Regression 'LINEARREG_ANGLE', # Linear Regression Angle 'LINEARREG_INTERCEPT', # Linear Regression Intercept 'LINEARREG_SLOPE', # Linear Regression Slope 'STDDEV', # Standard Deviation 'TSF', # Time Series Forecast 'VAR', # Variance } } LearntKnowledges = set() LearntKnowledges |= AllLearntKnowledges['Overlap Studies'] LearntKnowledges |= AllLearntKnowledges['Momentum Indicators'] LearntKnowledges |= AllLearntKnowledges['Volume Indicators'] LearntKnowledges |= AllLearntKnowledges['Volatility Indicators'] LearntKnowledges |= AllLearntKnowledges['Price Transform'] LearntKnowledges |= AllLearntKnowledges['Cycle Indicators'] LearntKnowledges |= AllLearntKnowledges['Pattern Recognition'] LearntKnowledges |= AllLearntKnowledges['Statistic Functions'] timeperiods = [6, 12, 24] TREND_CHECK_CANDLES = 4 DECIMALS = 2 DUALFIT = False LearntKnowledges = list(LearntKnowledges) LearntKnowledges_with_timeperiod = list() for LearntKnowledge in LearntKnowledges: for timeperiod in timeperiods: LearntKnowledges_with_timeperiod.append( f'{LearntKnowledge}-{timeperiod}') if len(LearntKnowledges) == 1: LearntKnowledges = LearntKnowledges*2 if len(timeperiods) == 1: timeperiods = timeperiods*2 def knowledge_calculator(dataframe, indicator): if 'CDL' in indicator: splited_indicator = indicator.split('-') splited_indicator[1] = "0" new_indicator = "-".join(splited_indicator) indicator = new_indicator knowledge = indicator.split("-") knowledge_name = knowledge[0] knowledge_len = len(knowledge) if indicator in dataframe.keys(): return dataframe[indicator] else: result = None if knowledge_len == 1: result = getattr(ta, knowledge_name)( dataframe ) return result elif knowledge_len == 2: knowledge_timeperiod = int(knowledge[1]) result = getattr(ta, knowledge_name)( dataframe, timeperiod=knowledge_timeperiod, ) return result elif knowledge_len == 3: knowledge_timeperiod = int(knowledge[2]) knowledge_index = int(knowledge[1]) result = getattr(ta, knowledge_name)( dataframe, timeperiod=knowledge_timeperiod, ).iloc[:, knowledge_index] return result elif knowledge_len == 4: knowledge_timeperiod = int(knowledge[1]) sharp_indicator = f'{knowledge_name}-{knowledge_timeperiod}' dataframe[sharp_indicator] = getattr(ta, knowledge_name)( dataframe, timeperiod=knowledge_timeperiod, ) return ta.SMA(dataframe[sharp_indicator].fillna(0), TREND_CHECK_CANDLES) elif knowledge_len == 5: knowledge_timeperiod = int(knowledge[2]) knowledge_index = int(knowledge[1]) sharp_indicator = f'{knowledge_name}-{knowledge_index}-{knowledge_timeperiod}' dataframe[sharp_indicator] = getattr(ta, knowledge_name)( dataframe, timeperiod=knowledge_timeperiod, ).iloc[:, knowledge_index] return ta.SMA(dataframe[sharp_indicator].fillna(0), TREND_CHECK_CANDLES) def normalize(df): df = 2*(df-df.min())/(df.max()-df.min()) - 1 return df class Github_remiotore_freqtrade__QBitrain__20260111_210550(IStrategy): buy_params = { "buy_indicator_0": "CDLTRISTAR-24", "buy_indicator_1": "CDLEVENINGDOJISTAR-12", "buy_indicator_2": "MACD-2-12", "buy_indicator_3": "STOCH-1-12", "buy_indicator_4": "PPO-6", "buy_indicator_5": "MOM-6", "buy_node_quantum_state_0": -0.5, "buy_node_quantum_state_1": -0.24, "buy_node_quantum_state_2": 0.51, "buy_node_quantum_state_3": -0.27, "buy_node_quantum_state_4": 0.35, "buy_node_quantum_state_5": 0.99, } sell_params = { "sell_indicator_0": "BBANDS-2-6", "sell_indicator_1": "EMA-24", "sell_indicator_2": "BBANDS-2-12", "sell_indicator_3": "BBANDS-0-24", "sell_indicator_4": "MACD-1-12", "sell_indicator_5": "HT_PHASOR-1-12", "sell_node_quantum_state_0": -0.15, "sell_node_quantum_state_1": -0.4, "sell_node_quantum_state_2": -0.56, "sell_node_quantum_state_3": -0.64, "sell_node_quantum_state_4": 0.64, "sell_node_quantum_state_5": 0.36, } minimal_roi = { "0": 0.636, "1133": 0.112, "2179": 0.049, "4307": 0 } stoploss = -0.256 timeframe = '5m' buy_indicator_0 = CategoricalParameter( LearntKnowledges_with_timeperiod, default="CDLTRISTAR-24", space='buy') buy_indicator_1 = CategoricalParameter( LearntKnowledges_with_timeperiod, default="CDLEVENINGDOJISTAR-12", space='buy') buy_indicator_2 = CategoricalParameter( LearntKnowledges_with_timeperiod, default="MACD-2-12", space='buy') buy_indicator_3 = CategoricalParameter( LearntKnowledges_with_timeperiod, default="STOCH-1-12", space='buy') buy_indicator_4 = CategoricalParameter( LearntKnowledges_with_timeperiod, default="PPO-6", space='buy') buy_indicator_5 = CategoricalParameter( LearntKnowledges_with_timeperiod, default="MOM-6", space='buy') buy_node_quantum_state_0 = DecimalParameter( -1, 1, decimals=DECIMALS, default=-0.5, space='buy') buy_node_quantum_state_1 = DecimalParameter(-1, 1, decimals=DECIMALS, default=-0.24, space='buy') buy_node_quantum_state_2 = DecimalParameter( -1, 1, decimals=DECIMALS, default=0.51, space='buy') buy_node_quantum_state_3 = DecimalParameter(-1, 1, decimals=DECIMALS, default=-0.27, space='buy') buy_node_quantum_state_4 = DecimalParameter( -1, 1, decimals=DECIMALS, default=0.35, space='buy') buy_node_quantum_state_5 = DecimalParameter( -1, 1, decimals=DECIMALS, default=0.99, space='buy') if DUALFIT == False: sell_indicator_0 = CategoricalParameter( LearntKnowledges_with_timeperiod, default="BBANDS-2-6", space='sell') sell_indicator_1 = CategoricalParameter( LearntKnowledges_with_timeperiod, default="EMA-24", space='sell') sell_indicator_2 = CategoricalParameter( LearntKnowledges_with_timeperiod, default="BBANDS-2-12", space='sell') sell_indicator_3 = CategoricalParameter( LearntKnowledges_with_timeperiod, default="BBANDS-0-24", space='sell') sell_indicator_4 = CategoricalParameter( LearntKnowledges_with_timeperiod, default="MACD-1-12", space='sell') sell_indicator_5 = CategoricalParameter( LearntKnowledges_with_timeperiod, default="HT_PHASOR-1-12", space='sell') sell_node_quantum_state_0 = DecimalParameter(-1, 1, decimals=DECIMALS, default=-0.15, space='sell') sell_node_quantum_state_1 = DecimalParameter(-1, 1, decimals=DECIMALS, default=-0.4, space='sell') sell_node_quantum_state_2 = DecimalParameter(-1, 1, decimals=DECIMALS, default=-0.56, space='sell') sell_node_quantum_state_3 = DecimalParameter(-1, 1, decimals=DECIMALS, default=-0.64, space='sell') sell_node_quantum_state_4 = DecimalParameter(-1, 1, decimals=DECIMALS, default=0.64, space='sell') sell_node_quantum_state_5 = DecimalParameter(-1, 1, decimals=DECIMALS, default=0.36, space='sell') def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: ''' It's good to calculate all indicators in all time periods here and so optimize the strategy. But this strategy can take much time to generate anything that may not use in his optimization. I just calculate the specific indicators in specific time period inside buy and sell strategy populator methods if needed. Also, this method (populate_indicators) just calculates default value of hyperoptable params so using this method have not big benefits instade of calculating useable things inside buy and sell trand populators ''' return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] RESULT = 0 IND = self.buy_indicator_0.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.buy_node_quantum_state_0.value RESULT += DFINP*QS IND = self.buy_indicator_1.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.buy_node_quantum_state_1.value RESULT += DFINP*QS IND = self.buy_indicator_2.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.buy_node_quantum_state_2.value RESULT += DFINP*QS IND = self.buy_indicator_3.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.buy_node_quantum_state_3.value RESULT += DFINP*QS IND = self.buy_indicator_4.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.buy_node_quantum_state_4.value RESULT += DFINP*QS IND = self.buy_indicator_5.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.buy_node_quantum_state_5.value RESULT += DFINP*QS RESULT = normalize(RESULT*dataframe['close']) conditions.append(RESULT > 0.333) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] RESULT = 0 if DUALFIT == True: IND = self.buy_indicator_0.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.buy_node_quantum_state_0.value RESULT += DFINP*QS IND = self.buy_indicator_1.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.buy_node_quantum_state_1.value RESULT += DFINP*QS IND = self.buy_indicator_2.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.buy_node_quantum_state_2.value RESULT += DFINP*QS IND = self.buy_indicator_3.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.buy_node_quantum_state_3.value RESULT += DFINP*QS IND = self.buy_indicator_4.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.buy_node_quantum_state_4.value RESULT += DFINP*QS IND = self.buy_indicator_5.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.buy_node_quantum_state_5.value RESULT += DFINP*QS else: IND = self.sell_indicator_0.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.sell_node_quantum_state_0.value RESULT += DFINP*QS IND = self.sell_indicator_1.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.sell_node_quantum_state_1.value RESULT += DFINP*QS IND = self.sell_indicator_2.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.sell_node_quantum_state_2.value RESULT += DFINP*QS IND = self.sell_indicator_3.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.sell_node_quantum_state_3.value RESULT += DFINP*QS IND = self.sell_indicator_4.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.sell_node_quantum_state_4.value RESULT += DFINP*QS IND = self.sell_indicator_5.value DFINP = dataframe[IND] = knowledge_calculator(dataframe, IND) QS = self.sell_node_quantum_state_5.value RESULT += DFINP*QS RESULT = normalize(RESULT*dataframe['close']) conditions.append(RESULT < -0.333) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell']=1 return dataframe