mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-09-20 09:31:12 +00:00
207 lines
7.6 KiB
Django/Jinja
207 lines
7.6 KiB
Django/Jinja
|
|
# 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]
|
|
"""
|