mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-10 10:21:59 +00:00
207 lines
7.6 KiB
Django/Jinja
207 lines
7.6 KiB
Django/Jinja
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# Momentum Indicators
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# ------------------------------------
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# ADX
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dataframe["adx"] = ta.ADX(dataframe)
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# # Plus Directional Indicator / Movement
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# dataframe["plus_dm"] = ta.PLUS_DM(dataframe)
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# dataframe["plus_di"] = ta.PLUS_DI(dataframe)
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# # Minus Directional Indicator / Movement
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# dataframe["minus_dm"] = ta.MINUS_DM(dataframe)
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# dataframe["minus_di"] = ta.MINUS_DI(dataframe)
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# # Aroon, Aroon Oscillator
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# aroon = ta.AROON(dataframe)
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# dataframe["aroonup"] = aroon["aroonup"]
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# dataframe["aroondown"] = aroon["aroondown"]
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# dataframe["aroonosc"] = ta.AROONOSC(dataframe)
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# # Awesome Oscillator
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# dataframe["ao"] = qtpylib.awesome_oscillator(dataframe)
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# # Keltner Channel
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# keltner = qtpylib.keltner_channel(dataframe)
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# dataframe["kc_upperband"] = keltner["upper"]
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# dataframe["kc_lowerband"] = keltner["lower"]
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# dataframe["kc_middleband"] = keltner["mid"]
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# dataframe["kc_percent"] = (
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# (dataframe["close"] - dataframe["kc_lowerband"]) /
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# (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
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# )
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# dataframe["kc_width"] = (
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# (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
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# )
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# # Ultimate Oscillator
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# dataframe["uo"] = ta.ULTOSC(dataframe)
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# # Commodity Channel Index: values [Oversold:-100, Overbought:100]
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# dataframe["cci"] = ta.CCI(dataframe)
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# RSI
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dataframe["rsi"] = ta.RSI(dataframe)
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# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
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# rsi = 0.1 * (dataframe["rsi"] - 50)
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# dataframe["fisher_rsi"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
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# # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
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# dataframe["fisher_rsi_norma"] = 50 * (dataframe["fisher_rsi"] + 1)
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# # Stochastic Slow
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# stoch = ta.STOCH(dataframe)
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# dataframe["slowd"] = stoch["slowd"]
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# dataframe["slowk"] = stoch["slowk"]
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# Stochastic Fast
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stoch_fast = ta.STOCHF(dataframe)
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dataframe["fastd"] = stoch_fast["fastd"]
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dataframe["fastk"] = stoch_fast["fastk"]
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# # Stochastic RSI
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# Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
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# STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
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# stoch_rsi = ta.STOCHRSI(dataframe)
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# dataframe["fastd_rsi"] = stoch_rsi["fastd"]
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# dataframe["fastk_rsi"] = stoch_rsi["fastk"]
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# MACD
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macd = ta.MACD(dataframe)
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dataframe["macd"] = macd["macd"]
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dataframe["macdsignal"] = macd["macdsignal"]
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dataframe["macdhist"] = macd["macdhist"]
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# MFI
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dataframe["mfi"] = ta.MFI(dataframe)
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# # ROC
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# dataframe["roc"] = ta.ROC(dataframe)
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# Overlap Studies
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# ------------------------------------
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# Bollinger Bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe["bb_lowerband"] = bollinger["lower"]
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dataframe["bb_middleband"] = bollinger["mid"]
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dataframe["bb_upperband"] = bollinger["upper"]
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dataframe["bb_percent"] = (
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(dataframe["close"] - dataframe["bb_lowerband"]) /
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
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)
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dataframe["bb_width"] = (
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
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)
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# Bollinger Bands - Weighted (EMA based instead of SMA)
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# weighted_bollinger = qtpylib.weighted_bollinger_bands(
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# qtpylib.typical_price(dataframe), window=20, stds=2
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# )
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# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
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# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
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# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
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# dataframe["wbb_percent"] = (
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# (dataframe["close"] - dataframe["wbb_lowerband"]) /
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# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
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# )
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# dataframe["wbb_width"] = (
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# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / dataframe["wbb_middleband"]
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# )
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# # EMA - Exponential Moving Average
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# dataframe["ema3"] = ta.EMA(dataframe, timeperiod=3)
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# dataframe["ema5"] = ta.EMA(dataframe, timeperiod=5)
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# dataframe["ema10"] = ta.EMA(dataframe, timeperiod=10)
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# dataframe["ema21"] = ta.EMA(dataframe, timeperiod=21)
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# dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50)
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# dataframe["ema100"] = ta.EMA(dataframe, timeperiod=100)
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# # SMA - Simple Moving Average
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# dataframe["sma3"] = ta.SMA(dataframe, timeperiod=3)
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# dataframe["sma5"] = ta.SMA(dataframe, timeperiod=5)
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# dataframe["sma10"] = ta.SMA(dataframe, timeperiod=10)
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# dataframe["sma21"] = ta.SMA(dataframe, timeperiod=21)
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# dataframe["sma50"] = ta.SMA(dataframe, timeperiod=50)
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# dataframe["sma100"] = ta.SMA(dataframe, timeperiod=100)
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# Parabolic SAR
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dataframe["sar"] = ta.SAR(dataframe)
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# TEMA - Triple Exponential Moving Average
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dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
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# Cycle Indicator
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# ------------------------------------
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# Hilbert Transform Indicator - SineWave
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hilbert = ta.HT_SINE(dataframe)
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dataframe["htsine"] = hilbert["sine"]
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dataframe["htleadsine"] = hilbert["leadsine"]
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# Pattern Recognition - Bullish candlestick patterns
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# ------------------------------------
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# # Hammer: values [0, 100]
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# dataframe["CDLHAMMER"] = ta.CDLHAMMER(dataframe)
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# # Inverted Hammer: values [0, 100]
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# dataframe["CDLINVERTEDHAMMER"] = ta.CDLINVERTEDHAMMER(dataframe)
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# # Dragonfly Doji: values [0, 100]
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# dataframe["CDLDRAGONFLYDOJI"] = ta.CDLDRAGONFLYDOJI(dataframe)
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# # Piercing Line: values [0, 100]
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# dataframe["CDLPIERCING"] = ta.CDLPIERCING(dataframe) # values [0, 100]
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# # Morningstar: values [0, 100]
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# dataframe["CDLMORNINGSTAR"] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
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# # Three White Soldiers: values [0, 100]
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# dataframe["CDL3WHITESOLDIERS"] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
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# Pattern Recognition - Bearish candlestick patterns
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# ------------------------------------
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# # Hanging Man: values [0, 100]
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# dataframe["CDLHANGINGMAN"] = ta.CDLHANGINGMAN(dataframe)
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# # Shooting Star: values [0, 100]
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# dataframe["CDLSHOOTINGSTAR"] = ta.CDLSHOOTINGSTAR(dataframe)
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# # Gravestone Doji: values [0, 100]
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# dataframe["CDLGRAVESTONEDOJI"] = ta.CDLGRAVESTONEDOJI(dataframe)
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# # Dark Cloud Cover: values [0, 100]
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# dataframe["CDLDARKCLOUDCOVER"] = ta.CDLDARKCLOUDCOVER(dataframe)
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# # Evening Doji Star: values [0, 100]
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# dataframe["CDLEVENINGDOJISTAR"] = ta.CDLEVENINGDOJISTAR(dataframe)
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# # Evening Star: values [0, 100]
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# dataframe["CDLEVENINGSTAR"] = ta.CDLEVENINGSTAR(dataframe)
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# Pattern Recognition - Bullish/Bearish candlestick patterns
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# ------------------------------------
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# # Three Line Strike: values [0, -100, 100]
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# dataframe["CDL3LINESTRIKE"] = ta.CDL3LINESTRIKE(dataframe)
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# # Spinning Top: values [0, -100, 100]
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# dataframe["CDLSPINNINGTOP"] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
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# # Engulfing: values [0, -100, 100]
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# dataframe["CDLENGULFING"] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
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# # Harami: values [0, -100, 100]
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# dataframe["CDLHARAMI"] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
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# # Three Outside Up/Down: values [0, -100, 100]
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# dataframe["CDL3OUTSIDE"] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
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# # Three Inside Up/Down: values [0, -100, 100]
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# dataframe["CDL3INSIDE"] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
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# # Chart type
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# # ------------------------------------
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# # Heikin Ashi Strategy
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# heikinashi = qtpylib.heikinashi(dataframe)
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# dataframe["ha_open"] = heikinashi["open"]
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# dataframe["ha_close"] = heikinashi["close"]
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# dataframe["ha_high"] = heikinashi["high"]
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# dataframe["ha_low"] = heikinashi["low"]
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# Retrieve best bid and best ask from the orderbook
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# ------------------------------------
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"""
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# first check if dataprovider is available
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if self.dp:
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if self.dp.runmode.value in ("live", "dry_run"):
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ob = self.dp.orderbook(metadata["pair"], 1)
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dataframe["best_bid"] = ob["bids"][0][0]
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dataframe["best_ask"] = ob["asks"][0][0]
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"""
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