mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-14 12:13:57 +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 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]
|
|
"""
|