def STOCHRSI(self): STOCHRSI = tb.STOCHRSI(self.dataframe, fastk_period=5, fastd_period=3, fastd_matype=0) fastk = STOCHRSI["fastk"][len(STOCHRSI) - 1] # main fastd = STOCHRSI["fastd"][len(STOCHRSI) - 1] # signal oldFastk = STOCHRSI["fastk"][len(STOCHRSI) - 2] # main oldFastd = STOCHRSI["fastd"][len(STOCHRSI) - 2] # signal # sell when main line > upper band (80) and main line crosses the signal line from above-down # buy when main line < lower band (20) and main line crosses the signal line from bottom-up if (fastk > 80): #print("RSI: " + str(value) + " overbought") return "overbought" elif (fastk > 60): #print("RSI: " + str(value) + " buy") return "buy" elif (fastk < 20): #print("RSI: " + str(value) + " oversold") return "oversold" elif (fastk < 40): #print("RSI: " + str(value) + " sell") return "sell" else: #print("RSI: " + str(value) + " neutral") return "neutral"
def analyze(self, historical_data, period_count=14, hot_thresh=None, cold_thresh=None, all_data=False): """Performs a Stochastic RSI analysis on the historical data Args: historical_data (list): A matrix of historical OHCLV data. period_count (int, optional): Defaults to 14. The number of data points to consider for our simple moving average. hot_thresh (float, optional): Defaults to None. The threshold at which this might be good to purchase. cold_thresh (float, optional): Defaults to None. The threshold at which this might be good to sell. all_data (bool, optional): Defaults to False. If True, we return the Stochastic RSI associated with each data point in our historical dataset. Otherwise just return the last one. Returns: dict: A dictionary containing a tuple of indicator values and booleans for buy / sell indication. """ dataframe = self.convert_to_dataframe(historical_data) rsi_values = abstract.STOCHRSI(dataframe, period_count).iloc[:, 0] rsi_result_data = [] for rsi_value in rsi_values: if math.isnan(rsi_value): continue is_hot = False if hot_thresh is not None: is_hot = rsi_value < hot_thresh is_cold = False if cold_thresh is not None: is_cold = rsi_value > cold_thresh data_point_result = { 'values': (rsi_value, ), 'is_cold': is_cold, 'is_hot': is_hot } rsi_result_data.append(data_point_result) if all_data: return rsi_result_data else: try: return rsi_result_data[-1] except IndexError: return rsi_result_data
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_width'] = ( (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband']) dataframe['bb_bottom_cross'] = qtpylib.crossed_below( dataframe['close'], dataframe['bb_lowerband']).astype('int') dataframe['rsi'] = ta.RSI(dataframe, timeperiod=10) dataframe['plus_di'] = ta.PLUS_DI(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) dataframe['cci'] = ta.CCI(dataframe, 30) dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14) dataframe['cmf'] = chaikin_mf(dataframe) dataframe['rmi'] = RMI(dataframe, length=8, mom=4) stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2) dataframe['srsi_fk'] = stoch['fastk'] dataframe['srsi_fd'] = stoch['fastd'] dataframe['fastEMA'] = ta.EMA(dataframe['volume'], timeperiod=12) dataframe['slowEMA'] = ta.EMA(dataframe['volume'], timeperiod=26) dataframe['pvo'] = ((dataframe['fastEMA'] - dataframe['slowEMA']) / dataframe['slowEMA']) * 100 dataframe['is_dip'] = ((dataframe['rmi'] < 20) & (dataframe['cci'] <= -150) & (dataframe['srsi_fk'] < 20) # Maybe comment mfi and cmf to make more trades & (dataframe['mfi'] < 25) & (dataframe['cmf'] <= -0.1)).astype('int') dataframe['is_break'] = ( (dataframe['bb_width'] > 0.025) & (dataframe['bb_bottom_cross'].rolling(10).sum() > 1) & (dataframe['close'] < 0.99 * dataframe['bb_lowerband']) ).astype('int') dataframe['buy_signal'] = ((dataframe['is_dip'] > 0) & (dataframe['is_break'] > 0)).astype('int') 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 """ # Momentum Indicators # ------------------------------------ dataframe['rsi'] = ta.RSI(dataframe) # # Stochastic RSI stoch_rsi = ta.STOCHRSI(dataframe) # dataframe['fastd_rsi'] = stoch_rsi['fastd'] dataframe['fastk_rsi'] = stoch_rsi['fastk'] # # 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) # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] # dataframe['macdsignal'] = macd['macdsignal'] # dataframe['macdhist'] = macd['macdhist'] # Parabolic SAR dataframe['sar'] = ta.SAR(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'] # 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"] # ) return dataframe
def get_ticker_dataframe(pair: str) -> DataFrame: """ Analyses the trend for the given pair :param pair: pair as str in format BTC_ETH or BTC-ETH :return: DataFrame """ minimum_date = arrow.now() - timedelta(hours=6) url = 'https://bittrex.com/Api/v2.0/pub/market/GetTicks' headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36', } params = { 'marketName': pair.replace('_', '-'), 'tickInterval': 'OneMin', '_': minimum_date.timestamp * 1000 } data = requests.get(url, params=params, headers=headers).json() if not data['success']: raise RuntimeError('BITTREX: {}'.format(data['message'])) data = [{ 'close': t['C'], 'volume': t['V'], 'open': t['O'], 'high': t['H'], 'low': t['L'], 'date': t['T'], } for t in sorted(data['result'], key=lambda k: k['T']) if arrow.get(t['T']) > minimum_date] dataframe = DataFrame(json_normalize(data)) dataframe['close_30_ema'] = ta.EMA(dataframe, timeperiod=30) dataframe['close_90_ema'] = ta.EMA(dataframe, timeperiod=90) dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.2) # calculate StochRSI stochrsi = ta.STOCHRSI(dataframe) dataframe['stochrsi'] = stochrsi['fastd'] # values between 0-100, not 0-1 macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macds'] = macd['macdsignal'] dataframe['macdh'] = macd['macdhist'] return dataframe
def stochrsi(self): stochrsi = tb.STOCHRSI(self.dataframe, fastk_period=5, fastd_period=3, fastd_matype=0) fastk = stochrsi["fastk"][len(stochrsi) - 1] if fastk > 80: return "overbought" elif fastk > 60: return "buy" elif fastk < 20: return "oversold" elif fastk < 40: return "sell" else: return "neutral"
def populate_indicators(dataframe: DataFrame) -> DataFrame: """ Adds several different TA indicators to the given DataFrame """ dataframe['close_30_ema'] = ta.EMA(dataframe, timeperiod=30) dataframe['close_90_ema'] = ta.EMA(dataframe, timeperiod=90) dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.2) # calculate StochRSI stochrsi = ta.STOCHRSI(dataframe) dataframe['stochrsi'] = stochrsi['fastd'] # values between 0-100, not 0-1 macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macds'] = macd['macdsignal'] dataframe['macdh'] = macd['macdhist'] return dataframe
def populate_indicators_and_buy_signal(dataframe): dataframe['close_30_ema'] = ta.EMA(dataframe, timeperiod=30) dataframe['close_90_ema'] = ta.EMA(dataframe, timeperiod=90) dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.2) stochrsi = ta.STOCHRSI(dataframe) dataframe['stochrsi'] = stochrsi['fastd'] # values between 0-100, not 0-1 macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macds'] = macd['macdsignal'] dataframe['macdh'] = macd['macdhist'] dataframe.loc[(dataframe['stochrsi'] < 20) & (dataframe['macd'] > dataframe['macds']) & (dataframe['close'] > dataframe['sar']), 'buy'] = 1 dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close'] return dataframe, info_msg % (dataframe.iloc[-1]['stochrsi'])
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 """ # Momentum Indicators # ------------------------------------ # Stochastic Fast stoch_fast = ta.STOCHF(dataframe, 5, 3, 3) 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, 12, 26, 1) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # # EMA - Exponential Moving Average dataframe['ema5c'] = ta.EMA(dataframe['close'], timeperiod=5) dataframe['ema5o'] = ta.EMA(dataframe['open'], timeperiod=5) return dataframe
def analyze(self, historical_data, period_count=14, hot_thresh=None, cold_thresh=None, all_data=False): """Performs a Stochastic RSI analysis on the historical data Args: historical_data (list): A matrix of historical OHCLV data. period_count (int, optional): Defaults to 14. The number of data points to consider for our simple moving average. hot_thresh (float, optional): Defaults to None. The threshold at which this might be good to purchase. cold_thresh (float, optional): Defaults to None. The threshold at which this might be good to sell. all_data (bool, optional): Defaults to False. If True, we return the Stochastic RSI associated with each data point in our historical dataset. Otherwise just return the last one. Returns: dict: A dictionary containing a tuple of indicator values and booleans for buy / sell indication. """ dataframe = self.convert_to_dataframe(historical_data) rsi_values = abstract.STOCHRSI(dataframe, period_count).iloc[:, 0] analyzed_data = [(value, ) for value in rsi_values] return self.analyze_results(analyzed_data, is_hot=lambda v: v < hot_thresh if hot_thresh else False, is_cold=lambda v: v > cold_thresh if cold_thresh else False, all_data=all_data)
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # 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(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