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Implement More triggers and guards from PR#394

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This commit is contained in:
Gerald Lonlas 2018-01-17 21:44:37 -08:00
parent c46d78b4b9
commit dfd61bbf1d
1 changed files with 127 additions and 16 deletions
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143
freqtrade/strategy/default_strategy.py
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@ -45,7 +45,10 @@ class DefaultStrategy(IStrategy):
# Awesome oscillator # Awesome oscillator
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
"""
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
dataframe['cci'] = ta.CCI(dataframe)
"""
# MACD # MACD
macd = ta.MACD(dataframe) macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd'] dataframe['macd'] = macd['macd']
@ -62,14 +65,35 @@ class DefaultStrategy(IStrategy):
# Plus Directional Indicator / Movement # Plus Directional Indicator / Movement
dataframe['plus_dm'] = ta.PLUS_DM(dataframe) dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe) dataframe['plus_di'] = ta.PLUS_DI(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
"""
# ROC
dataframe['roc'] = ta.ROC(dataframe)
"""
# RSI # RSI
dataframe['rsi'] = ta.RSI(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
stoch_fast = ta.STOCHF(dataframe) stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk'] dataframe['fastk'] = stoch_fast['fastk']
"""
# Stoch RSI
stoch_rsi = ta.STOCHRSI(dataframe)
dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
"""
# Overlap Studies # Overlap Studies
# ------------------------------------ # ------------------------------------
@ -79,15 +103,15 @@ class DefaultStrategy(IStrategy):
# returns middle band for all the three bands. Switch to qtpylib.bollinger_bands # returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
# and use middle band instead. # and use middle band instead.
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband'] dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
"""
# Bollinger bands # Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_upperband'] = bollinger['upper']
"""
# EMA - Exponential Moving Average # EMA - Exponential Moving Average
dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
@ -109,6 +133,66 @@ class DefaultStrategy(IStrategy):
dataframe['htsine'] = hilbert['sine'] dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine'] 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 return dataframe
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame: def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
@ -159,6 +243,10 @@ class DefaultStrategy(IStrategy):
Define your Hyperopt space for the strategy Define your Hyperopt space for the strategy
""" """
space = { space = {
'macd_below_zero': hp.choice('macd_below_zero', [
{'enabled': False},
{'enabled': True}
]),
'mfi': hp.choice('mfi', [ 'mfi': hp.choice('mfi', [
{'enabled': False}, {'enabled': False},
{'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)} {'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)}
@ -197,13 +285,15 @@ class DefaultStrategy(IStrategy):
]), ]),
'trigger': hp.choice('trigger', [ 'trigger': hp.choice('trigger', [
{'type': 'lower_bb'}, {'type': 'lower_bb'},
{'type': 'lower_bb_tema'},
{'type': 'faststoch10'}, {'type': 'faststoch10'},
{'type': 'ao_cross_zero'}, {'type': 'ao_cross_zero'},
{'type': 'ema5_cross_ema10'}, {'type': 'ema3_cross_ema10'},
{'type': 'macd_cross_signal'}, {'type': 'macd_cross_signal'},
{'type': 'sar_reversal'}, {'type': 'sar_reversal'},
{'type': 'stochf_cross'},
{'type': 'ht_sine'}, {'type': 'ht_sine'},
{'type': 'heiken_reversal_bull'},
{'type': 'di_cross'},
]), ]),
'stoploss': hp.uniform('stoploss', -0.5, -0.02), 'stoploss': hp.uniform('stoploss', -0.5, -0.02),
} }
@ -218,6 +308,8 @@ class DefaultStrategy(IStrategy):
# GUARDS AND TRENDS # GUARDS AND TRENDS
if params['uptrend_long_ema']['enabled']: if params['uptrend_long_ema']['enabled']:
conditions.append(dataframe['ema50'] > dataframe['ema100']) conditions.append(dataframe['ema50'] > dataframe['ema100'])
if params['macd_below_zero']['enabled']:
conditions.append(dataframe['macd'] < 0)
if params['uptrend_short_ema']['enabled']: if params['uptrend_short_ema']['enabled']:
conditions.append(dataframe['ema5'] > dataframe['ema10']) conditions.append(dataframe['ema5'] > dataframe['ema10'])
if params['mfi']['enabled']: if params['mfi']['enabled']:
@ -238,18 +330,37 @@ class DefaultStrategy(IStrategy):
# TRIGGERS # TRIGGERS
triggers = { triggers = {
'lower_bb': dataframe['tema'] <= dataframe['blower'], 'lower_bb': (
'faststoch10': (qtpylib.crossed_above(dataframe['fastd'], 10.0)), dataframe['close'] < dataframe['bb_lowerband']
'ao_cross_zero': (qtpylib.crossed_above(dataframe['ao'], 0.0)),
'ema5_cross_ema10': (
qtpylib.crossed_above(dataframe['ema5'], dataframe['ema10'])
), ),
'macd_cross_signal': ( 'lower_bb_tema': (
qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']) dataframe['tema'] < dataframe['bb_lowerband']
), ),
'sar_reversal': (qtpylib.crossed_above(dataframe['close'], dataframe['sar'])), 'faststoch10': (qtpylib.crossed_above(
'stochf_cross': (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])), dataframe['fastd'], 10.0
'ht_sine': (qtpylib.crossed_above(dataframe['htleadsine'], dataframe['htsine'])), )),
'ao_cross_zero': (qtpylib.crossed_above(
dataframe['ao'], 0.0
)),
'ema3_cross_ema10': (qtpylib.crossed_above(
dataframe['ema3'], dataframe['ema10']
)),
'macd_cross_signal': (qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
)),
'sar_reversal': (qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
)),
'ht_sine': (qtpylib.crossed_above(
dataframe['htleadsine'], dataframe['htsine']
)),
'heiken_reversal_bull': (
(qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
(dataframe['ha_low'] == dataframe['ha_open'])
),
'di_cross': (qtpylib.crossed_above(
dataframe['plus_di'], dataframe['minus_di']
)),
} }
conditions.append(triggers.get(params['trigger']['type'])) conditions.append(triggers.get(params['trigger']['type']))