Merge pull request #91 from freqtrade/reinforcedScalpHyperopt
Reinforced scalp hyperopt
This commit is contained in:
Matthias
@@ -48,6 +48,9 @@ class AverageHyperopt(IHyperOpt):
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dataframe[f"maMedium({params['trigger'][1]})"])
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)
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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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@@ -52,7 +52,8 @@ class MACDStrategy_hyperopt(IHyperOpt):
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dataframe.loc[
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(
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(dataframe['macd'] > dataframe['macdsignal']) &
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(dataframe['cci'] <= params['buy-cci-value'])
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(dataframe['cci'] <= params['buy-cci-value']) &
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(dataframe['volume'] > 0) # Make sure Volume is not 0
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),
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'buy'] = 1
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@@ -0,0 +1,195 @@
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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from functools import reduce
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from typing import Any, Callable, Dict, List
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import talib.abstract as ta
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from pandas import DataFrame
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from skopt.space import Categorical, Dimension, Integer
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from freqtrade.optimize.hyperopt_interface import IHyperOpt
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class ReinforcedSmoothScalp(IHyperOpt):
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"""
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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 buy_strategy_generator(params: Dict[str, Any]) -> Callable:
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"""
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Define the buy strategy parameters to be used by Hyperopt.
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"""
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def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Buy strategy Hyperopt will build and use.
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"""
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conditions = []
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# GUARDS AND TRENDS
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if 'mfi-enabled' in params and params['mfi-enabled']:
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conditions.append(dataframe['mfi'] < params['mfi-value'])
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if 'fastd-enabled' in params and params['fastd-enabled']:
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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 '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 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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# conditions.append(qtpylib.crossed_above(
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# dataframe['macd'], dataframe['macdsignal']
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# ))
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# if params['trigger'] == 'sar_reversal':
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# conditions.append(qtpylib.crossed_above(
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# dataframe['close'], dataframe['sar']
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# ))
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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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return populate_buy_trend
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@staticmethod
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def indicator_space() -> List[Dimension]:
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"""
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Define your Hyperopt space for searching buy strategy parameters.
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"""
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return [
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Integer(10, 25, name='mfi-value'),
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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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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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]
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@staticmethod
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def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
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"""
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Define the sell strategy parameters to be used by Hyperopt.
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"""
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def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Sell strategy Hyperopt will build and use.
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"""
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conditions = []
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# GUARDS AND TRENDS
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if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
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conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
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if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
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conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
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if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
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conditions.append(dataframe['adx'] < params['sell-adx-value'])
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if 'sell-fastk-enabled' in params and params['sell-fastk-enabled']:
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conditions.append(dataframe['fastk'] > params['sell-fastk-value'])
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if 'sell-cci-enabled' in params and params['sell-cci-enabled']:
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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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# conditions.append(dataframe['close'] > dataframe['bb_upperband'])
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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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# conditions.append(qtpylib.crossed_above(
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# dataframe['sar'], dataframe['close']
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# ))
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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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return populate_sell_trend
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@staticmethod
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def sell_indicator_space() -> List[Dimension]:
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"""
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Define your Hyperopt space for searching sell strategy parameters.
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"""
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return [
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Integer(75, 100, name='sell-mfi-value'),
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Integer(50, 100, name='sell-fastd-value'),
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Integer(50, 100, name='sell-fastk-value'),
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Integer(50, 100, name='sell-adx-value'),
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Integer(100, 200, name='sell-cci-value'),
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Categorical([True, False], name='sell-mfi-enabled'),
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Categorical([True, False], name='sell-fastd-enabled'),
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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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# 'sell-macd_cross_signal',
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# 'sell-sar_reversal'], name='sell-trigger')
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]
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(
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(dataframe['open'] < dataframe['ema_low']) &
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(dataframe['adx'] > 30) &
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(dataframe['mfi'] < 30) &
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(
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(dataframe['fastk'] < 30) &
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(dataframe['fastd'] < 30) &
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(qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']))
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) &
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(dataframe['resample_sma'] < dataframe['close'])
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)
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# |
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# # try to get some sure things independent of resample
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# ((dataframe['rsi'] - dataframe['mfi']) < 10) &
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# (dataframe['mfi'] < 30) &
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# (dataframe['cci'] < -200)
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),
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'buy'] = 1
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(
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(
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(dataframe['open'] >= dataframe['ema_high'])
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) |
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(
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(qtpylib.crossed_above(dataframe['fastk'], 70)) |
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(qtpylib.crossed_above(dataframe['fastd'], 70))
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)
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) & (dataframe['cci'] > 100)
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)
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,
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'sell'] = 1
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return dataframe
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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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