def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # # Heikin Ashi Candles heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] # Set Up Bollinger Bands mid, lower = bollinger_bands(ha_typical_price(dataframe), window_size=40, num_of_std=2) dataframe['lower'] = lower dataframe['mid'] = mid dataframe['bbdelta'] = (mid - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs() dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs() dataframe['bb_lowerband'] = dataframe['lower'] dataframe['bb_middleband'] = dataframe['mid'] dataframe['ema_fast'] = ta.EMA(dataframe['ha_close'], timeperiod=3) dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50) dataframe['volume_mean_slow'] = dataframe['volume'].rolling( window=30).mean() dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28) rsi = ta.RSI(dataframe) dataframe["rsi"] = rsi rsi = 0.1 * (rsi - 50) dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) inf_tf = '1h' informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf) inf_heikinashi = qtpylib.heikinashi(informative) informative['ha_close'] = inf_heikinashi['close'] informative['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) return dataframe
def populate_indicators(self, dataframe: DataFrame) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. """ preprocessed = self.ML_parse_ticker_dataframe(dataframe) test_ratio = 0.2 data = ml_utils.run_pipeline(preprocessed, test_ratio) data['date'] = data.index # Chart type # ------------------------------------ # Heikinashi stategy heikinashi = qtpylib.heikinashi(data) data['ha_open'] = heikinashi['open'] data['ha_close'] = heikinashi['close'] data['ha_high'] = heikinashi['high'] data['ha_low'] = heikinashi['low'] return data
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. """ dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Heikin Ashi Candles heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] # Set Up Bollinger Bands upper_bb1, mid_bb1, lower_bb1 = ta.BBANDS(dataframe['ha_close'], timeperiod=40) upper_bb2, mid_bb2, lower_bb2 = ta.BBANDS( qtpylib.typical_price(heikinashi), timeperiod=20) # only putting some bands into dataframe as the others are not used elsewhere in the strategy dataframe['lower-bb1'] = lower_bb1 dataframe['lower-bb2'] = lower_bb2 dataframe['mid-bb2'] = mid_bb2 dataframe['bb1-delta'] = (mid_bb1 - dataframe['lower-bb1']).abs() dataframe['closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs() dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs() dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=48) dataframe['volume_mean_slow'] = dataframe['volume'].rolling( window=24).mean() dataframe['rsi'] = ta.RSI(heikinashi, timeperiod=14) dataframe['tema'] = ta.TEMA(heikinashi, timeperiod=9) dataframe['adx'] = ta.ADX(heikinashi) dataframe['rmi'] = RMI(heikinashi) dataframe['sar'] = ta.SAR(heikinashi) return dataframe
def populate_indicators(self, dataframe: DataFrame) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. """ print('ML_STRATEGY Dataframe', dataframe) # RSI dataframe['rsi'] = ta.RSI(dataframe) # Chart type # ------------------------------------ # Heikinashi stategy heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] return dataframe
def populate_indicators(self, dataframe: DataFrame) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. """ # Momentum Indicator # ------------------------------------ # ADX dataframe['adx'] = ta.ADX(dataframe) # Awesome oscillator dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) """ # Commodity Channel Index: values Oversold:<-100, Overbought:>100 dataframe['cci'] = ta.CCI(dataframe) """ # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # MFI dataframe['mfi'] = ta.MFI(dataframe) # Minus Directional Indicator / Movement dataframe['minus_dm'] = ta.MINUS_DM(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) # Plus Directional Indicator / Movement dataframe['plus_dm'] = ta.PLUS_DM(dataframe) dataframe['plus_di'] = ta.PLUS_DI(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) """ # ROC dataframe['roc'] = ta.ROC(dataframe) """ # RSI dataframe['rsi'] = ta.RSI(dataframe) # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy) dataframe['fisher_rsi'] = fishers_inverse(dataframe['rsi']) # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy) dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # Stoch stoch = ta.STOCH(dataframe) dataframe['slowd'] = stoch['slowd'] dataframe['slowk'] = stoch['slowk'] # Stoch fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] """ # Stoch RSI stoch_rsi = ta.STOCHRSI(dataframe) dataframe['fastd_rsi'] = stoch_rsi['fastd'] dataframe['fastk_rsi'] = stoch_rsi['fastk'] """ # Overlap Studies # ------------------------------------ # Previous Bollinger bands # Because ta.BBANDS implementation is broken with small numbers, it actually # returns middle band for all the three bands. Switch to qtpylib.bollinger_bands # and use middle band instead. dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband'] # Bollinger bands 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'] # EMA - Exponential Moving Average dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) # SAR Parabol dataframe['sar'] = ta.SAR(dataframe) # SMA - Simple Moving Average dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) # TEMA - Triple Exponential Moving Average dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) # Cycle Indicator # ------------------------------------ # Hilbert Transform Indicator - SineWave hilbert = ta.HT_SINE(dataframe) dataframe['htsine'] = hilbert['sine'] dataframe['htleadsine'] = hilbert['leadsine'] # Pattern Recognition - Bullish candlestick patterns # ------------------------------------ """ # Hammer: values [0, 100] dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # Inverted Hammer: values [0, 100] dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) # Dragonfly Doji: values [0, 100] dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) # Piercing Line: values [0, 100] dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] # Morningstar: values [0, 100] dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] # Three White Soldiers: values [0, 100] dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] """ # Pattern Recognition - Bearish candlestick patterns # ------------------------------------ """ # Hanging Man: values [0, 100] dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) # Shooting Star: values [0, 100] dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) # Gravestone Doji: values [0, 100] dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) # Dark Cloud Cover: values [0, 100] dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) # Evening Doji Star: values [0, 100] dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) # Evening Star: values [0, 100] dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) """ # Pattern Recognition - Bullish/Bearish candlestick patterns # ------------------------------------ """ # Three Line Strike: values [0, -100, 100] dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) # Spinning Top: values [0, -100, 100] dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] # Engulfing: values [0, -100, 100] dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] # Harami: values [0, -100, 100] dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] # Three Outside Up/Down: values [0, -100, 100] dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] # Three Inside Up/Down: values [0, -100, 100] dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] """ # Chart type # ------------------------------------ # Heikinashi stategy heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe() :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ # Momentum Indicators # ------------------------------------ # ADX dataframe['adx'] = ta.ADX(dataframe) # Plus Directional Indicator / Movement dataframe['plus_dm'] = ta.PLUS_DM(dataframe) dataframe['plus_di'] = ta.PLUS_DI(dataframe) # # Minus Directional Indicator / Movement dataframe['minus_dm'] = ta.MINUS_DM(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) # Aroon, Aroon Oscillator aroon = ta.AROON(dataframe) dataframe['aroonup'] = aroon['aroonup'] dataframe['aroondown'] = aroon['aroondown'] dataframe['aroonosc'] = ta.AROONOSC(dataframe) # Awesome Oscillator dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) # Keltner Channel keltner = qtpylib.keltner_channel(dataframe) dataframe["kc_upperband"] = keltner["upper"] dataframe["kc_lowerband"] = keltner["lower"] dataframe["kc_middleband"] = keltner["mid"] dataframe["kc_percent"] = ( (dataframe["close"] - dataframe["kc_lowerband"]) / (dataframe["kc_upperband"] - dataframe["kc_lowerband"])) dataframe["kc_width"] = ( (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]) # Ultimate Oscillator dataframe['uo'] = ta.ULTOSC(dataframe) # Commodity Channel Index: values [Oversold:-100, Overbought:100] dataframe['cci'] = ta.CCI(dataframe) # RSI dataframe['rsi'] = ta.RSI(dataframe) # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy) dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # Stochastic Slow stoch = ta.STOCH(dataframe) dataframe['slowd'] = stoch['slowd'] dataframe['slowk'] = stoch['slowk'] # Stochastic Fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # Stochastic RSI stoch_rsi = ta.STOCHRSI(dataframe) dataframe['fastd_rsi'] = stoch_rsi['fastd'] dataframe['fastk_rsi'] = stoch_rsi['fastk'] # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # MFI dataframe['mfi'] = ta.MFI(dataframe) # # ROC dataframe['roc'] = ta.ROC(dataframe) # Overlap Studies # ------------------------------------ # Bollinger Bands 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["bb_percent"] = ( (dataframe["close"] - dataframe["bb_lowerband"]) / (dataframe["bb_upperband"] - dataframe["bb_lowerband"])) dataframe["bb_width"] = ( (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]) # Bollinger Bands - Weighted (EMA based instead of SMA) weighted_bollinger = qtpylib.weighted_bollinger_bands( qtpylib.typical_price(dataframe), window=20, stds=2) dataframe["wbb_upperband"] = weighted_bollinger["upper"] dataframe["wbb_lowerband"] = weighted_bollinger["lower"] dataframe["wbb_middleband"] = weighted_bollinger["mid"] dataframe["wbb_percent"] = ( (dataframe["close"] - dataframe["wbb_lowerband"]) / (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])) dataframe["wbb_width"] = ( (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / dataframe["wbb_middleband"]) # EMA - Exponential Moving Average dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) # SMA - Simple Moving Average dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3) dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5) dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10) dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21) dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50) dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100) # Parabolic SAR dataframe['sar'] = ta.SAR(dataframe) # TEMA - Triple Exponential Moving Average dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) # Cycle Indicator # ------------------------------------ # Hilbert Transform Indicator - SineWave hilbert = ta.HT_SINE(dataframe) dataframe['htsine'] = hilbert['sine'] dataframe['htleadsine'] = hilbert['leadsine'] # Pattern Recognition - Bullish candlestick patterns # ------------------------------------ # Hammer: values [0, 100] dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # Inverted Hammer: values [0, 100] dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) # Dragonfly Doji: values [0, 100] dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) # Piercing Line: values [0, 100] dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] # Morningstar: values [0, 100] dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR( dataframe) # values [0, 100] # Three White Soldiers: values [0, 100] dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS( dataframe) # values [0, 100] # Pattern Recognition - Bearish candlestick patterns # ------------------------------------ # Hanging Man: values [0, 100] dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) # Shooting Star: values [0, 100] dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) # Gravestone Doji: values [0, 100] dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) # Dark Cloud Cover: values [0, 100] dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) # Evening Doji Star: values [0, 100] dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) # Evening Star: values [0, 100] dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) # Pattern Recognition - Bullish/Bearish candlestick patterns # ------------------------------------ # Three Line Strike: values [0, -100, 100] dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) # Spinning Top: values [0, -100, 100] dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP( dataframe) # values [0, -100, 100] # Engulfing: values [0, -100, 100] dataframe['CDLENGULFING'] = ta.CDLENGULFING( dataframe) # values [0, -100, 100] # Harami: values [0, -100, 100] dataframe['CDLHARAMI'] = ta.CDLHARAMI( dataframe) # values [0, -100, 100] # Three Outside Up/Down: values [0, -100, 100] dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE( dataframe) # values [0, -100, 100] # Three Inside Up/Down: values [0, -100, 100] dataframe['CDL3INSIDE'] = ta.CDL3INSIDE( dataframe) # values [0, -100, 100] # Chart type # ------------------------------------ # Heikin Ashi Strategy heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] # Retrieve best bid and best ask from the orderbook # ------------------------------------ """ # first check if dataprovider is available if self.dp: if self.dp.runmode in ('live', 'dry_run'): ob = self.dp.orderbook(metadata['pair'], 1) dataframe['best_bid'] = ob['bids'][0][0] dataframe['best_ask'] = ob['asks'][0][0] """ return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. """ dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['adx'] = ta.ADX(dataframe) dataframe['rsi'] = ta.RSI(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] bollinger = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0) dataframe['bb_lowerband'] = bollinger['lowerband'] dataframe['bb_middleband'] = bollinger['middleband'] dataframe['bb_upperband'] = bollinger['upperband'] # Stoch stoch = ta.STOCH(dataframe) dataframe['slowk'] = stoch['slowk'] # Commodity Channel Index: values Oversold:<-100, Overbought:>100 dataframe['cci'] = ta.CCI(dataframe) # Stoch stoch = ta.STOCHF(dataframe, 5) dataframe['fastd'] = stoch['fastd'] dataframe['fastk'] = stoch['fastk'] dataframe['fastk-previous'] = dataframe.fastk.shift(1) dataframe['fastd-previous'] = dataframe.fastd.shift(1) # Slow Stoch slowstoch = ta.STOCHF(dataframe, 50) dataframe['slowfastd'] = slowstoch['fastd'] dataframe['slowfastk'] = slowstoch['fastk'] dataframe['slowfastk-previous'] = dataframe.slowfastk.shift(1) dataframe['slowfastd-previous'] = dataframe.slowfastd.shift(1) # RSI dataframe['rsi'] = ta.RSI(dataframe) # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) # Bollinger bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] # SAR Parabol dataframe['sar'] = ta.SAR(dataframe) # Hammer: values [0, 100] dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # SMA - Simple Moving Average dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) return dataframe
def populate_indicators(dataframe: DataFrame) -> DataFrame: """ Adds several different TA indicators to the given DataFrame """ dataframe['adx'] = ta.ADX(dataframe) dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) dataframe['cci'] = ta.CCI(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] dataframe['mfi'] = ta.MFI(dataframe) dataframe['minus_dm'] = ta.MINUS_DM(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) dataframe['plus_dm'] = ta.PLUS_DM(dataframe) dataframe['plus_di'] = ta.PLUS_DI(dataframe) dataframe['roc'] = ta.ROC(dataframe) dataframe['rsi'] = ta.RSI(dataframe) # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy) dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # Stoch stoch = ta.STOCH(dataframe) dataframe['slowd'] = stoch['slowd'] dataframe['slowk'] = stoch['slowk'] # Stoch fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # Stoch RSI stoch_rsi = ta.STOCHRSI(dataframe) dataframe['fastd_rsi'] = stoch_rsi['fastd'] dataframe['fastk_rsi'] = stoch_rsi['fastk'] # Bollinger bands 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'] # EMA - Exponential Moving Average dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) # SAR Parabolic dataframe['sar'] = ta.SAR(dataframe) # SMA - Simple Moving Average dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) # TEMA - Triple Exponential Moving Average dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) # Hilbert Transform Indicator - SineWave hilbert = ta.HT_SINE(dataframe) dataframe['htsine'] = hilbert['sine'] dataframe['htleadsine'] = hilbert['leadsine'] # Pattern Recognition - Bullish candlestick patterns # ------------------------------------ """ # Hammer: values [0, 100] dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # Inverted Hammer: values [0, 100] dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) # Dragonfly Doji: values [0, 100] dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) # Piercing Line: values [0, 100] dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] # Morningstar: values [0, 100] dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] # Three White Soldiers: values [0, 100] dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] """ # Pattern Recognition - Bearish candlestick patterns # ------------------------------------ """ # Hanging Man: values [0, 100] dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) # Shooting Star: values [0, 100] dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) # Gravestone Doji: values [0, 100] dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) # Dark Cloud Cover: values [0, 100] dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) # Evening Doji Star: values [0, 100] dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) # Evening Star: values [0, 100] dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) """ # Pattern Recognition - Bullish/Bearish candlestick patterns # ------------------------------------ """ # Three Line Strike: values [0, -100, 100] dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) # Spinning Top: values [0, -100, 100] dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] # Engulfing: values [0, -100, 100] dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] # Harami: values [0, -100, 100] dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] # Three Outside Up/Down: values [0, -100, 100] dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] # Three Inside Up/Down: values [0, -100, 100] dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] """ # Chart type # ------------------------------------ # Heikinashi stategy heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Dataframe with data from the exchange :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ #divergences # - - - - # - # 4 3 2 1 0 #src[4] > src[2] and src[3] > src[2] and src[2] < src[1] and src[2] < src[0] dataframe['bullish_div'] = ( ( dataframe['close'].shift(4) > dataframe['close'].shift(2) ) & ( dataframe['close'].shift(3) > dataframe['close'].shift(2) ) & ( dataframe['close'].shift(2) < dataframe['close'].shift(1) ) & ( dataframe['close'].shift(2) < dataframe['close'] ) ) #queremos el volumen medio de las ultimas 24 velas, si es mayor queremos comprar, si es que no es volumen a la baja, esto habria que compararlo tomando el precio unas horas antes dataframe['mean24volume'] = dataframe.volume.rolling(24).mean() dataframe['mean68close'] = dataframe.close.rolling(68).mean() # - # - - - - # 4 3 2 1 0 #src[4] < src[2] and src[3] < src[2] and src[2] > src[1] and src[2] > src[0] dataframe['bearish_div'] = ( ( dataframe['close'].shift(4) < dataframe['close'].shift(2) ) & ( dataframe['close'].shift(3) < dataframe['close'].shift(2) ) & ( dataframe['close'].shift(2) > dataframe['close'].shift(1) ) & ( dataframe['close'].shift(2) > dataframe['close'] ) ) dataframe['cci_one'] = ta.CCI(dataframe, timeperiod=170) dataframe['cci_two'] = ta.CCI(dataframe, timeperiod=34) # Momentum Indicators # ------------------------------------ # ADX dataframe['adx'] = ta.ADX(dataframe) # # Plus Directional Indicator / Movement # dataframe['plus_dm'] = ta.PLUS_DM(dataframe) # dataframe['plus_di'] = ta.PLUS_DI(dataframe) # # Minus Directional Indicator / Movement # dataframe['minus_dm'] = ta.MINUS_DM(dataframe) # dataframe['minus_di'] = ta.MINUS_DI(dataframe) # # Aroon, Aroon Oscillator # aroon = ta.AROON(dataframe) # dataframe['aroonup'] = aroon['aroonup'] # dataframe['aroondown'] = aroon['aroondown'] # dataframe['aroonosc'] = ta.AROONOSC(dataframe) # # Awesome Oscillator # dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) # # Keltner Channel # keltner = qtpylib.keltner_channel(dataframe) # dataframe["kc_upperband"] = keltner["upper"] # dataframe["kc_lowerband"] = keltner["lower"] # dataframe["kc_middleband"] = keltner["mid"] # dataframe["kc_percent"] = ( # (dataframe["close"] - dataframe["kc_lowerband"]) / # (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) # ) # dataframe["kc_width"] = ( # (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"] # ) # # Ultimate Oscillator # dataframe['uo'] = ta.ULTOSC(dataframe) # # Commodity Channel Index: values [Oversold:-100, Overbought:100] dataframe['cci'] = ta.CCI(dataframe) # RSI dataframe['rsi'] = ta.RSI(dataframe) # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy) # rsi = 0.1 * (dataframe['rsi'] - 50) # dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) # # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy) # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # # Stochastic Slow # stoch = ta.STOCH(dataframe) # dataframe['slowd'] = stoch['slowd'] # dataframe['slowk'] = stoch['slowk'] # Stochastic Fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # # Stochastic RSI # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this. # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results. # stoch_rsi = ta.STOCHRSI(dataframe) # dataframe['fastd_rsi'] = stoch_rsi['fastd'] # dataframe['fastk_rsi'] = stoch_rsi['fastk'] # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # MFI dataframe['mfi'] = ta.MFI(dataframe) # # ROC # dataframe['roc'] = ta.ROC(dataframe) # Overlap Studies # ------------------------------------ # Bollinger Bands 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["bb_percent"] = ( (dataframe["close"] - dataframe["bb_lowerband"]) / (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) ) dataframe["bb_width"] = ( (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"] ) # Bollinger Bands - Weighted (EMA based instead of SMA) # weighted_bollinger = qtpylib.weighted_bollinger_bands( # qtpylib.typical_price(dataframe), window=20, stds=2 # ) # dataframe["wbb_upperband"] = weighted_bollinger["upper"] # dataframe["wbb_lowerband"] = weighted_bollinger["lower"] # dataframe["wbb_middleband"] = weighted_bollinger["mid"] # dataframe["wbb_percent"] = ( # (dataframe["close"] - dataframe["wbb_lowerband"]) / # (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) # ) # dataframe["wbb_width"] = ( # (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / # dataframe["wbb_middleband"] # ) # # EMA - Exponential Moving Average dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200) # # SMA - Simple Moving Average # dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3) # dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5) # dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10) # dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21) # dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50) # dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100) # Parabolic SAR dataframe['sar'] = ta.SAR(dataframe) # TEMA - Triple Exponential Moving Average dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) # Cycle Indicator # ------------------------------------ # Hilbert Transform Indicator - SineWave hilbert = ta.HT_SINE(dataframe) dataframe['htsine'] = hilbert['sine'] dataframe['htleadsine'] = hilbert['leadsine'] # Pattern Recognition - Bullish candlestick patterns # ------------------------------------ # # Hammer: values [0, 100] # dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # # Inverted Hammer: values [0, 100] # dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) # # Dragonfly Doji: values [0, 100] # dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) # # Piercing Line: values [0, 100] # dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] # # Morningstar: values [0, 100] # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] # # Three White Soldiers: values [0, 100] # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] # Pattern Recognition - Bearish candlestick patterns # ------------------------------------ # # Hanging Man: values [0, 100] # dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) # # Shooting Star: values [0, 100] # dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) # # Gravestone Doji: values [0, 100] # dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) # # Dark Cloud Cover: values [0, 100] # dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) # # Evening Doji Star: values [0, 100] # dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) # # Evening Star: values [0, 100] # dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) # Pattern Recognition - Bullish/Bearish candlestick patterns # ------------------------------------ # # Three Line Strike: values [0, -100, 100] # dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) # # Spinning Top: values [0, -100, 100] # dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] # # Engulfing: values [0, -100, 100] # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] # # Harami: values [0, -100, 100] # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] # # Three Outside Up/Down: values [0, -100, 100] # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] # # Three Inside Up/Down: values [0, -100, 100] # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] # # Chart type # # ------------------------------------ # Heikin Ashi Strategy heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] dataframe['haclosestrat'] = (dataframe['ha_open'] + dataframe['ha_high'] + dataframe['ha_low'] + dataframe['close']) / 4 dataframe['haopenstrat'] = (dataframe['ha_open'] + dataframe['close']) / 2 dataframe['highstrat'] = max(dataframe['ha_high'] , max(dataframe['ha_open'], dataframe['close'] )) dataframe['lowstrat'] = min(dataframe['haLow'] , min(dataframe['ha_open'], dataframe['close'] )) # Retrieve best bid and best ask from the orderbook # ------------------------------------ return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conversion_line_period = 9 base_line_periods = 26 laggin_span = 52 displacement = 26 df = dataframe.copy() # Heikin Ashi Strategy heikinashi = qtpylib.heikinashi(df) df['ha_open'] = heikinashi['open'] df['ha_close'] = heikinashi['close'] df['ha_high'] = heikinashi['high'] df['ha_low'] = heikinashi['low'] df['tenkan'] = ( df['ha_high'].rolling(window=conversion_line_period).max() + df['ha_low'].rolling(window=conversion_line_period).min()) / 2 df['kijun'] = ( df['ha_high'].rolling(window=base_line_periods).max() + df['ha_low'].rolling(window=base_line_periods).min()) / 2 df['senkou_leading_a'] = (df['tenkan'] + df['kijun']) / 2 df['senkou_leading_b'] = ( df['ha_high'].rolling(window=laggin_span).max() + df['ha_low'].rolling(window=laggin_span).min()) / 2 df['senkou_span_a'] = df['senkou_leading_a'].shift(displacement) df['senkou_span_b'] = df['senkou_leading_b'].shift(displacement) df['chikou_span'] = df['ha_close'].shift(displacement) df['tenkan1'] = df['tenkan'].shift(+1) df['kijun1'] = df['kijun'].shift(+1) df['senkou_leading_a1'] = df['senkou_leading_a'].shift(+1) df['senkou_leading_b1'] = df['senkou_leading_b'].shift(+1) df['senkou_span_a1'] = df['senkou_span_a'].shift(+1) df['senkou_span_b1'] = df['senkou_span_b'].shift(+1) df['chikou_span1'] = df['chikou_span'].shift(+1) df['ha_close1'] = df['ha_close'].shift(+1) # // == Price and Kijun Sen (standard line) Cross == def calcTkCross( tenkan, kijun, tenkan1, kijun1, senkou_span_a, senkou_span_b, ): if (tenkan > kijun) and (tenkan1 <= kijun1): intersect = (tenkan1 * (kijun - kijun1) - kijun1 * (tenkan - tenkan1)) / ((kijun - kijun1) - (tenkan - tenkan1)) if (intersect > senkou_span_a) and (intersect > senkou_span_b): return 2 elif (intersect < senkou_span_a) and (intersect < senkou_span_b): return 0.5 else: return 1 elif (tenkan < kijun) and (tenkan1 >= kijun1): intersect = (tenkan1 * (kijun - kijun1) - kijun1 * (tenkan - tenkan1)) / ((kijun - kijun1) - (tenkan - tenkan1)) if (intersect > senkou_span_a) and (intersect > senkou_span_b): return -0.5 elif (intersect < senkou_span_a) and (intersect < senkou_span_b): return -2 else: return -1 else: return 0 # df['tkCrossScore'] = np.vectorize(calcTkCross)() df['tkCrossScore'] = np.vectorize(calcTkCross)( df['tenkan'], df['kijun'], df['tenkan1'], df['kijun1'], df['senkou_span_a'], df['senkou_span_b'], ) # // == Price and Kijun Sen (standard line) Cross == def calcPkCross( ha_close, kijun, ha_close1, kijun1, senkou_span_a, senkou_span_b, ): if (ha_close > kijun) and (ha_close1 <= kijun1): intersect = (ha_close1 * (kijun - kijun1) - kijun1 * (ha_close - ha_close1)) / ((kijun - kijun1) - (ha_close - ha_close1)) if (intersect > senkou_span_a) and (intersect > senkou_span_b): return 2 elif (intersect < senkou_span_a) and (intersect < senkou_span_b): return 0.5 else: return 1 elif (ha_close < kijun) and (ha_close1 >= kijun1): intersect = (ha_close1 * (kijun - kijun1) - kijun1 * (ha_close - ha_close1)) / ((kijun - kijun1) - (ha_close - ha_close1)) if (intersect > senkou_span_a) and (intersect > senkou_span_b): return -0.5 elif (intersect < senkou_span_a) and (intersect < senkou_span_b): return -2 else: return -1 else: return 0 df['pkCrossScore'] = np.vectorize(calcPkCross)( df['ha_close'], df['kijun'], df['ha_close1'], df['kijun1'], df['senkou_span_a'], df['senkou_span_b'], ) # // == Kumo Breakouts == def calcKumoBreakout( ha_close, senkou_span_a, ha_close1, senkou_span_a1, senkou_span_b, senkou_span_b1, ): if (((ha_close > senkou_span_a) and (ha_close1 <= senkou_span_a1) and (senkou_span_a > senkou_span_b)) or ((ha_close > senkou_span_b) and (ha_close1 <= senkou_span_b1) and (senkou_span_a < senkou_span_b))): return 2 elif (((ha_close < senkou_span_a) and (ha_close1 >= senkou_span_a1) and (senkou_span_a < senkou_span_b)) or ((ha_close < senkou_span_b) and (ha_close1 >= senkou_span_b1) and (senkou_span_a > senkou_span_b))): return -2 else: return 0 df['kumoBreakoutScore'] = np.vectorize(calcKumoBreakout)( df['ha_close'], df['senkou_span_a'], df['ha_close1'], df['senkou_span_a1'], df['senkou_span_b'], df['senkou_span_b1'], ) # // == Senkou Span Cross == def calcSenkouCross( senkou_leading_a, senkou_leading_b, senkou_leading_a1, senkou_leading_b1, ha_close, senkou_span_a, senkou_span_b, ): if (senkou_leading_a > senkou_leading_b) and (senkou_leading_a1 <= senkou_leading_b1): if (ha_close > senkou_span_a) and (ha_close > senkou_span_b): return 2 elif (ha_close < senkou_span_a) and (ha_close < senkou_span_b): return 0.5 else: return 1 elif (senkou_leading_a < senkou_leading_b) and ( senkou_leading_a1 >= senkou_leading_b1): if (ha_close > senkou_span_a) and (ha_close > senkou_span_b): return -0.5 elif (ha_close < senkou_span_a) and (ha_close < senkou_span_b): return -2 else: return -1 else: return 0 df['senkouCrossScore'] = np.vectorize(calcSenkouCross)( df['senkou_leading_a'], df['senkou_leading_b'], df['senkou_leading_a1'], df['senkou_leading_b1'], df['ha_close'], df['senkou_span_a'], df['senkou_span_b'], ) # // == Chikou Span Cross == def calcChikouCross( ha_close, chikou_span, ha_close1, chikou_span1, senkou_span_a, senkou_span_b, ): if (ha_close > chikou_span) and (ha_close1 <= chikou_span1): intersect = (ha_close1 * (chikou_span - chikou_span1) - chikou_span1 * (ha_close - ha_close1)) / ( (chikou_span - chikou_span1) - (ha_close - ha_close1)) if (intersect > senkou_span_a) and (intersect > senkou_span_b): return 2 elif (intersect < senkou_span_a) and (intersect < senkou_span_b): return 0.5 else: return 1 elif (ha_close < chikou_span) and (ha_close1 >= chikou_span1): intersect = (ha_close1 * (chikou_span - chikou_span1) - chikou_span1 * (ha_close - ha_close1)) / ( (chikou_span - chikou_span1) - (ha_close - ha_close1)) if (intersect > senkou_span_a) and (intersect > senkou_span_b): return -0.5 elif (intersect < senkou_span_a) and (intersect < senkou_span_b): return -2 else: return -1 else: return 0 df['chikouCrossScore'] = np.vectorize(calcChikouCross)( df['ha_close'], df['chikou_span'], df['ha_close1'], df['chikou_span1'], df['senkou_span_a'], df['senkou_span_b'], ) # // == price relative to cloud == def calcPricePlacement( ha_close, senkou_span_a, senkou_span_b, ): if (ha_close > senkou_span_a) and (ha_close > senkou_span_b): return 2 elif (ha_close < senkou_span_a) and (ha_close < senkou_span_b): return -2 else: return 0 df['pricePlacementScore'] = np.vectorize(calcPricePlacement)( df['ha_close'], df['senkou_span_a'], df['senkou_span_b'], ) # // == lag line releative to cloud == def calcChikouPlacement( ha_close, senkou_leading_a, senkou_leading_b, ): if (ha_close > senkou_leading_a) and (ha_close > senkou_leading_b): return 2 elif (ha_close < senkou_leading_a) and (ha_close < senkou_leading_b): return -2 else: return 0 df['chikouPlacementScore'] = np.vectorize(calcChikouPlacement)( df['ha_close'], df['senkou_leading_a'], df['senkou_leading_b'], ) # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # // dataframe['Ichimoku_Score'] = ( df['tkCrossScore'] + df['pkCrossScore'] + df['kumoBreakoutScore'] + df['senkouCrossScore'] + df['chikouCrossScore']).rolling(1).sum().cumsum() print(metadata['pair'], dataframe['Ichimoku_Score'].median()) return dataframe