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optimizing method

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Gert Wohlgemuth 2018-04-26 14:42:43 -07:00
parent 0d24aac1fb
commit 76c25d2d52
1 changed files with 13 additions and 31 deletions
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44
user_data/strategies/Quickie.py
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@ -22,15 +22,12 @@ class Quickie(IStrategy):
# Minimal ROI designed for the strategy. # Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi" # This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = { minimal_roi = {
"60": 0.01, "0": 0.01
"30": 0.03,
"20": 0.04,
"0": 0.05
} }
# Optimal stoploss designed for the strategy # Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss" # This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.3 stoploss = -0.25
# Optimal ticker interval for the strategy # Optimal ticker interval for the strategy
ticker_interval = 5 ticker_interval = 5
@ -41,11 +38,8 @@ class Quickie(IStrategy):
dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist'] dataframe['macdhist'] = macd['macdhist']
dataframe['cci'] = ta.CCI(dataframe) dataframe['tema'] = ta.TEMA(dataframe, timeperiod=100)
dataframe['willr'] = ta.WILLR(dataframe) dataframe['adx'] = ta.ADX(dataframe)
dataframe['smaSlow'] = ta.TEMA(dataframe, timeperiod=30)
dataframe['smaFast'] = ta.TEMA(dataframe, timeperiod=20)
# required for graphing # required for graphing
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
@ -53,36 +47,24 @@ class Quickie(IStrategy):
dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_upperband'] = bollinger['upper']
return dataframe return dataframe
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame: def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[ dataframe.loc[
( (
# we want to buy oversold assets (dataframe['adx'] > 30) &
(dataframe['tema'] < dataframe['bb_middleband']) &
# some basic trend should have been established (dataframe['tema'] > dataframe['tema'].shift(1))
(qtpylib.crossed_above(dataframe['smaFast'], dataframe['smaSlow'])) ),
)
,
'buy'] = 1 'buy'] = 1
return dataframe return dataframe
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame: def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[ dataframe.loc[
(qtpylib.crossed_above(dataframe['smaSlow'], dataframe['smaFast'])) (
, (dataframe['adx'] > 70) &
(dataframe['tema'] > dataframe['bb_middleband']) &
(dataframe['tema'] < dataframe['tema'].shift(1))
),
'sell'] = 1 'sell'] = 1
return dataframe return dataframe