# 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) # # 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.value 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] """