Align hyperopt to best practices
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Matthias
+25
-41
@@ -16,27 +16,6 @@ class ReinforcedSmoothScalp(IHyperOpt):
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Default hyperopt provided by the Freqtrade bot.
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You can override it with your own Hyperopt
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"""
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@staticmethod
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def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['ema_high'] = ta.EMA(dataframe, timeperiod=5, price='high')
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dataframe['ema_close'] = ta.EMA(dataframe, timeperiod=5, price='close')
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dataframe['ema_low'] = ta.EMA(dataframe, timeperiod=5, price='low')
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stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
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dataframe['fastd'] = stoch_fast['fastd']
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dataframe['fastk'] = stoch_fast['fastk']
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dataframe['adx'] = ta.ADX(dataframe)
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dataframe['cci'] = ta.CCI(dataframe, timeperiod=20)
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dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
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dataframe['mfi'] = ta.MFI(dataframe)
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# required for graphing
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bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_upperband'] = bollinger['upper']
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dataframe['bb_middleband'] = bollinger['mid']
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return dataframe
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@staticmethod
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def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
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@@ -56,12 +35,12 @@ class ReinforcedSmoothScalp(IHyperOpt):
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conditions.append(dataframe['fastd'] < params['fastd-value'])
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if 'adx-enabled' in params and params['adx-enabled']:
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conditions.append(dataframe['adx'] > params['adx-value'])
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#if 'rsi-enabled' in params and params['rsi-enabled']:
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# conditions.append(dataframe['rsi'] < params['rsi-value'])
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# if 'rsi-enabled' in params and params['rsi-enabled']:
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# conditions.append(dataframe['rsi'] < params['rsi-value'])
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if 'fastk-enabled' in params and params['fastk-enabled']:
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conditions.append(dataframe['fastk'] < params['fastk-value'])
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# TRIGGERS
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#if 'trigger' in params:
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# if 'trigger' in params:
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# if params['trigger'] == 'bb_lower':
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# conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
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# if params['trigger'] == 'macd_cross_signal':
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@@ -73,11 +52,13 @@ class ReinforcedSmoothScalp(IHyperOpt):
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# dataframe['close'], dataframe['sar']
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# ))
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'buy'] = 1
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# Check that volume is not 0
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conditions.append(dataframe['volume'] > 0)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'buy'] = 1
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return dataframe
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@@ -93,13 +74,13 @@ class ReinforcedSmoothScalp(IHyperOpt):
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Integer(15, 45, name='fastd-value'),
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Integer(15, 45, name='fastk-value'),
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Integer(20, 50, name='adx-value'),
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#Integer(20, 40, name='rsi-value'),
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# Integer(20, 40, name='rsi-value'),
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Categorical([True, False], name='mfi-enabled'),
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Categorical([True, False], name='fastd-enabled'),
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Categorical([True, False], name='adx-enabled'),
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Categorical([True, False], name='fastk-enabled'),
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#Categorical([True, False], name='rsi-enabled'),
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#Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
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# Categorical([True, False], name='rsi-enabled'),
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# Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
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]
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@staticmethod
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@@ -126,22 +107,25 @@ class ReinforcedSmoothScalp(IHyperOpt):
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conditions.append(dataframe['cci'] > params['sell-cci-value'])
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# TRIGGERS
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if 'sell-trigger' in params:
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#if params['sell-trigger'] == 'sell-bb_upper':
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# if 'sell-trigger' in params:
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# if params['sell-trigger'] == 'sell-bb_upper':
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# conditions.append(dataframe['close'] > dataframe['bb_upperband'])
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#if params['sell-trigger'] == 'sell-macd_cross_signal':
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# if params['sell-trigger'] == 'sell-macd_cross_signal':
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# conditions.append(qtpylib.crossed_above(
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# dataframe['macdsignal'], dataframe['macd']
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# ))
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#if params['sell-trigger'] == 'sell-sar_reversal':
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# if params['sell-trigger'] == 'sell-sar_reversal':
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# conditions.append(qtpylib.crossed_above(
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# dataframe['sar'], dataframe['close']
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# ))
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'sell'] = 1
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# Check that volume is not 0
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conditions.append(dataframe['volume'] > 0)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'sell'] = 1
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return dataframe
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@@ -163,7 +147,7 @@ class ReinforcedSmoothScalp(IHyperOpt):
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Categorical([True, False], name='sell-adx-enabled'),
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Categorical([True, False], name='sell-cci-enabled'),
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Categorical([True, False], name='sell-fastk-enabled'),
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#Categorical(['sell-bb_upper',
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# Categorical(['sell-bb_upper',
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# 'sell-macd_cross_signal',
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# 'sell-sar_reversal'], name='sell-trigger')
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]
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@@ -23,7 +23,7 @@ class ReinforcedSmoothScalp(IStrategy):
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# This attribute will be overridden if the config file contains "stoploss"
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# should not be below 3% loss
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stoploss = -0.8
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stoploss = -0.1
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# Optimal ticker interval for the strategy
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# the shorter the better
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ticker_interval = '1m'
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@@ -32,7 +32,6 @@ class ReinforcedSmoothScalp(IStrategy):
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resample_factor = 5
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe = self.resample(dataframe, self.ticker_interval, self.resample_factor)
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dataframe['ema_high'] = ta.EMA(dataframe, timeperiod=5, price='high')
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dataframe['ema_close'] = ta.EMA(dataframe, timeperiod=5, price='close')
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@@ -94,25 +93,3 @@ class ReinforcedSmoothScalp(IStrategy):
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,
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'sell'] = 1
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return dataframe
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def resample(self, dataframe, interval, factor):
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# defines the reinforcement logic
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# resampled dataframe to establish if we are in an uptrend, downtrend or sideways trend
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df = dataframe.copy()
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df = df.set_index(DatetimeIndex(df['date']))
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ohlc_dict = {
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'open': 'first',
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'high': 'max',
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'low': 'min',
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'close': 'last'
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}
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df = df.resample(str(int(interval[:-1]) * factor) + 'min',
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label="right").agg(ohlc_dict).dropna(how='any')
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df['resample_sma'] = ta.SMA(df, timeperiod=50, price='close')
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df = df.drop(columns=['open', 'high', 'low', 'close'])
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df = df.resample(interval[:-1] + 'min')
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df = df.interpolate(method='time')
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df['date'] = df.index
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df.index = range(len(df))
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dataframe = merge(dataframe, df, on='date', how='left')
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return dataframe
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