282 lines
11 KiB
Python
282 lines
11 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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# --- Do not remove these libs ---
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from functools import reduce
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from typing import Any, Callable, Dict, List
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import numpy # noqa
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import pandas # noqa
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from pandas import DataFrame
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from skopt.space import Categorical, Dimension, Integer, Real # noqa
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from freqtrade.optimize.hyperopt_interface import IHyperOpt
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# --------------------------------
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# Add your lib to import here
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import talib.abstract as ta # noqa
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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class Examplestrategy2opt(IHyperOpt):
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@staticmethod
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def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
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# Stoch
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stoch = ta.STOCH(dataframe)
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dataframe['slowk'] = stoch['slowk']
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
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rsi = 0.1 * (dataframe['rsi'] - 50)
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dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
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# Bollinger bands
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bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1)
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dataframe['bb_lowerband1'] = bollinger1['lower']
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dataframe['bb_middleband'] = bollinger1['mid']
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dataframe['bb_upperband1'] = bollinger1['upper']
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bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband2'] = bollinger2['lower']
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dataframe['bb_upperband2'] = bollinger2['upper']
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bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
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dataframe['bb_lowerband3'] = bollinger3['lower']
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dataframe['bb_upperband3'] = bollinger3['upper']
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bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=4)
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dataframe['bb_lowerband4'] = bollinger4['lower']
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dataframe['bb_upperband4'] = bollinger4['upper']
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# SAR Parabol
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dataframe['sar'] = ta.SAR(dataframe)
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# Hammer: values [0, 100]
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dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
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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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"""
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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 params.get('rsi-enabled'):
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conditions.append(dataframe['rsi'] < params['rsi-value'])
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if params.get('slowk-enabled'):
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conditions.append(dataframe['slowk'] < params['slowk-value'])
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if params.get('Hammer-enabled'):
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conditions.append(dataframe['CDLHAMMER'] < params['Hammer-value'])
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# TRIGGERS
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if 'trigger' in params:
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if params['trigger'] == 'bb_lower1':
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conditions.append(dataframe['close'] < dataframe['bb_lowerband1'])
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if params['trigger'] == 'bb_lower2':
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conditions.append(dataframe['close'] < dataframe['bb_lowerband2'])
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if params['trigger'] == 'bb_lower3':
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conditions.append(dataframe['close'] < dataframe['bb_lowerband3'])
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if params['trigger'] == 'bb_lower4':
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conditions.append(dataframe['close'] < dataframe['bb_lowerband4'])
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# Check that the candle had volume
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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(0, 100, name='slowk-value'),
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Integer(20, 80, name='rsi-value'),
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Integer(0, 100, name='Hammer-value'),
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Categorical([True, False], name='slowk-enabled'),
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Categorical([True, False], name='rsi-enabled'),
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Categorical([True, False], name='Hammer-enabled'),
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Categorical(['bb_lower1', 'bb_lower2', 'bb_lower3', 'bb_lower4'], 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 params.get('sell-fisher-enabled'):
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conditions.append(dataframe['fisher_rsi'] > params['sell-fisher-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_middle':
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conditions.append(dataframe['close'] > dataframe['bb_middleband'])
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if params['sell-trigger'] == 'sell-bb_upper1':
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conditions.append(dataframe['close'] > dataframe['bb_upperband1'])
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if params['sell-trigger'] == 'sell-bb_upper2':
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conditions.append(dataframe['close'] > dataframe['bb_upperband2'])
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if params['sell-trigger'] == 'sell-bb_upper3':
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conditions.append(dataframe['close'] > dataframe['bb_upperband3'])
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if params['sell-trigger'] == 'sell-bb_upper4':
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conditions.append(dataframe['close'] > dataframe['bb_upperband4'])
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if params['sell-trigger'] == 'sell-sar':
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conditions.append(dataframe['close'] < dataframe['sar'])
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# Check that the candle had volume
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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(-1.0, 1.0, name='sell-fisher-value'),
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Categorical([True, False], name='sell-fisher-enabled'),
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Categorical(['sell-sar'
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'sell-bb_middle',
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'sell-bb_upper1',
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'sell-bb_upper2',
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'sell-bb_upper3',
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'sell-bb_upper4', ], name='sell-trigger')
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]
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@staticmethod
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def generate_roi_table(params: Dict) -> Dict[int, float]:
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"""
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Generate the ROI table that will be used by Hyperopt
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This implementation generates the default legacy Freqtrade ROI tables.
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Change it if you need different number of steps in the generated
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ROI tables or other structure of the ROI tables.
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Please keep it aligned with parameters in the 'roi' optimization
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hyperspace defined by the roi_space method.
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"""
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roi_table = {}
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roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
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roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
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roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
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roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
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return roi_table
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@staticmethod
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def roi_space() -> List[Dimension]:
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"""
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Values to search for each ROI steps
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Override it if you need some different ranges for the parameters in the
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'roi' optimization hyperspace.
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Please keep it aligned with the implementation of the
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generate_roi_table method.
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"""
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return [
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Integer(10, 120, name='roi_t1'),
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Integer(10, 60, name='roi_t2'),
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Integer(10, 40, name='roi_t3'),
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Real(0.01, 0.04, name='roi_p1'),
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Real(0.01, 0.07, name='roi_p2'),
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Real(0.01, 0.20, name='roi_p3'),
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]
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@staticmethod
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def stoploss_space() -> List[Dimension]:
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"""
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Stoploss Value to search
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Override it if you need some different range for the parameter in the
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'stoploss' optimization hyperspace.
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"""
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return [
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Real(-0.35, -0.02, name='stoploss'),
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]
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@staticmethod
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def trailing_space() -> List[Dimension]:
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"""
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Create a trailing stoploss space.
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You may override it in your custom Hyperopt class.
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"""
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return [
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# It was decided to always set trailing_stop is to True if the 'trailing' hyperspace
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# is used. Otherwise hyperopt will vary other parameters that won't have effect if
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# trailing_stop is set False.
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# This parameter is included into the hyperspace dimensions rather than assigning
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# it explicitly in the code in order to have it printed in the results along with
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# other 'trailing' hyperspace parameters.
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Categorical([True], name='trailing_stop'),
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Real(0.01, 0.35, name='trailing_stop_positive'),
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# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
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# so this intermediate parameter is used as the value of the difference between
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# them. The value of the 'trailing_stop_positive_offset' is constructed in the
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# generate_trailing_params() method.
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# This is similar to the hyperspace dimensions used for constructing the ROI tables.
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Real(0.001, 0.1, name='trailing_stop_positive_offset_p1'),
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Categorical([True, False], name='trailing_only_offset_is_reached'),
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]
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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:param dataframe: DataFrame
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(
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(dataframe['rsi'] < 30) &
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(dataframe['slowk'] < 20) &
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(dataframe['bb_lowerband'] > dataframe['close']) &
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(dataframe['CDLHAMMER'] == 100)
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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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"""
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Based on TA indicators, populates the sell signal for the given dataframe
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:param dataframe: DataFrame
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(
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(dataframe['sar'] > dataframe['close']) &
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(dataframe['fisher_rsi'] > 0.3)
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),
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'sell'] = 1
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return dataframe
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