optimizing strategies
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@ -33,7 +33,7 @@ class Long(IStrategy):
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stoploss = -0.15
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stoploss = -0.15
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# Optimal ticker interval for the strategy
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# Optimal ticker interval for the strategy
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ticker_interval = 5
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ticker_interval = 60
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def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
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def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
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@ -22,10 +22,10 @@ class Quickie(IStrategy):
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# Minimal ROI designed for the strategy.
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# Minimal ROI designed for the strategy.
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# This attribute will be overridden if the config file contains "minimal_roi"
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# This attribute will be overridden if the config file contains "minimal_roi"
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minimal_roi = {
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minimal_roi = {
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"60": 0.01,
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"60": 0.01,
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"30": 0.03,
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"30": 0.03,
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"20": 0.04,
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"20": 0.04,
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"0": 0.05
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"0": 0.05
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}
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}
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# Optimal stoploss designed for the strategy
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# Optimal stoploss designed for the strategy
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@ -33,7 +33,7 @@ class Quickie(IStrategy):
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stoploss = -0.3
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stoploss = -0.3
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# Optimal ticker interval for the strategy
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# Optimal ticker interval for the strategy
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ticker_interval = 1
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ticker_interval = 5
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def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
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def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
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macd = ta.MACD(dataframe)
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macd = ta.MACD(dataframe)
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@ -44,8 +44,8 @@ class Quickie(IStrategy):
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dataframe['cci'] = ta.CCI(dataframe)
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dataframe['cci'] = ta.CCI(dataframe)
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dataframe['willr'] = ta.WILLR(dataframe)
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dataframe['willr'] = ta.WILLR(dataframe)
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dataframe['smaSlow'] = ta.EMA(dataframe, timeperiod=12)
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dataframe['smaSlow'] = ta.TEMA(dataframe, timeperiod=30)
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dataframe['smaFast'] = ta.EMA(dataframe, timeperiod=26)
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dataframe['smaFast'] = ta.TEMA(dataframe, timeperiod=20)
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# required for graphing
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# required for graphing
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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@ -53,10 +53,6 @@ class Quickie(IStrategy):
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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dataframe['bb_upperband'] = bollinger['upper']
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bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1.5)
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dataframe['bb_lowerband_2'] = bollinger['lower']
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dataframe['bb_middleband_2'] = bollinger['mid']
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dataframe['bb_upperband_2'] = bollinger['upper']
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return dataframe
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return dataframe
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@ -69,15 +65,9 @@ class Quickie(IStrategy):
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dataframe.loc[
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dataframe.loc[
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(
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(
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# we want to buy oversold assets
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# we want to buy oversold assets
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# (dataframe['cci'] <= -50)
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# some basic trend should have been established
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# some basic trend should have been established
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# & (dataframe['macd'] > dataframe['macdsignal'])
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(qtpylib.crossed_above(dataframe['smaFast'], dataframe['smaSlow']))
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# which starts inside the band
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# & (dataframe['open'] > dataframe['bb_lowerband'])
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qtpylib.crossed_above(dataframe['smaFast'], dataframe['smaSlow'])
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)
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)
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,
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,
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@ -92,8 +82,7 @@ class Quickie(IStrategy):
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:return: DataFrame with buy column
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:return: DataFrame with buy column
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"""
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"""
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dataframe.loc[
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dataframe.loc[
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qtpylib.crossed_above(dataframe['smaSlow'], dataframe['smaFast'])
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(qtpylib.crossed_above(dataframe['smaSlow'], dataframe['smaFast']))
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,
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,
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'sell'] = 1
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'sell'] = 1
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return dataframe
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return dataframe
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