208 lines
7.9 KiB
Python
208 lines
7.9 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
|
|
|
|
from functools import reduce
|
|
from typing import Any, Callable, Dict, List
|
|
|
|
import talib.abstract as ta
|
|
from pandas import DataFrame
|
|
from skopt.space import Categorical, Dimension, Integer
|
|
|
|
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
|
from freqtrade.optimize.hyperopt_interface import IHyperOpt
|
|
|
|
|
|
class HyperoptTestSepFile(IHyperOpt):
|
|
"""
|
|
Default hyperopt provided by the Freqtrade bot.
|
|
You can override it with your own Hyperopt
|
|
"""
|
|
@staticmethod
|
|
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Add several indicators needed for buy and sell strategies defined below.
|
|
"""
|
|
# ADX
|
|
dataframe['adx'] = ta.ADX(dataframe)
|
|
# MACD
|
|
macd = ta.MACD(dataframe)
|
|
dataframe['macd'] = macd['macd']
|
|
dataframe['macdsignal'] = macd['macdsignal']
|
|
# MFI
|
|
dataframe['mfi'] = ta.MFI(dataframe)
|
|
# RSI
|
|
dataframe['rsi'] = ta.RSI(dataframe)
|
|
# Stochastic Fast
|
|
stoch_fast = ta.STOCHF(dataframe)
|
|
dataframe['fastd'] = stoch_fast['fastd']
|
|
# Minus-DI
|
|
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
|
|
# Bollinger bands
|
|
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
|
|
dataframe['bb_lowerband'] = bollinger['lower']
|
|
dataframe['bb_upperband'] = bollinger['upper']
|
|
# SAR
|
|
dataframe['sar'] = ta.SAR(dataframe)
|
|
|
|
return dataframe
|
|
|
|
@staticmethod
|
|
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
|
|
"""
|
|
Define the buy strategy parameters to be used by Hyperopt.
|
|
"""
|
|
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Buy strategy Hyperopt will build and use.
|
|
"""
|
|
conditions = []
|
|
|
|
# GUARDS AND TRENDS
|
|
if 'mfi-enabled' in params and params['mfi-enabled']:
|
|
conditions.append(dataframe['mfi'] < params['mfi-value'])
|
|
if 'fastd-enabled' in params and params['fastd-enabled']:
|
|
conditions.append(dataframe['fastd'] < params['fastd-value'])
|
|
if 'adx-enabled' in params and params['adx-enabled']:
|
|
conditions.append(dataframe['adx'] > params['adx-value'])
|
|
if 'rsi-enabled' in params and params['rsi-enabled']:
|
|
conditions.append(dataframe['rsi'] < params['rsi-value'])
|
|
|
|
# TRIGGERS
|
|
if 'trigger' in params:
|
|
if params['trigger'] == 'boll':
|
|
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
|
|
if params['trigger'] == 'macd_cross_signal':
|
|
conditions.append(qtpylib.crossed_above(
|
|
dataframe['macd'],
|
|
dataframe['macdsignal']
|
|
))
|
|
if params['trigger'] == 'sar_reversal':
|
|
conditions.append(qtpylib.crossed_above(
|
|
dataframe['close'],
|
|
dataframe['sar']
|
|
))
|
|
|
|
if conditions:
|
|
dataframe.loc[
|
|
reduce(lambda x, y: x & y, conditions),
|
|
'buy'] = 1
|
|
|
|
return dataframe
|
|
|
|
return populate_buy_trend
|
|
|
|
@staticmethod
|
|
def indicator_space() -> List[Dimension]:
|
|
"""
|
|
Define your Hyperopt space for searching buy strategy parameters.
|
|
"""
|
|
return [
|
|
Integer(10, 25, name='mfi-value'),
|
|
Integer(15, 45, name='fastd-value'),
|
|
Integer(20, 50, name='adx-value'),
|
|
Integer(20, 40, name='rsi-value'),
|
|
Categorical([True, False], name='mfi-enabled'),
|
|
Categorical([True, False], name='fastd-enabled'),
|
|
Categorical([True, False], name='adx-enabled'),
|
|
Categorical([True, False], name='rsi-enabled'),
|
|
Categorical(['boll', 'macd_cross_signal', 'sar_reversal'], name='trigger')
|
|
]
|
|
|
|
@staticmethod
|
|
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
|
|
"""
|
|
Define the sell strategy parameters to be used by Hyperopt.
|
|
"""
|
|
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Sell strategy Hyperopt will build and use.
|
|
"""
|
|
conditions = []
|
|
|
|
# GUARDS AND TRENDS
|
|
if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
|
|
conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
|
|
if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
|
|
conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
|
|
if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
|
|
conditions.append(dataframe['adx'] < params['sell-adx-value'])
|
|
if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
|
|
conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
|
|
|
|
# TRIGGERS
|
|
if 'sell-trigger' in params:
|
|
if params['sell-trigger'] == 'sell-boll':
|
|
conditions.append(dataframe['close'] > dataframe['bb_upperband'])
|
|
if params['sell-trigger'] == 'sell-macd_cross_signal':
|
|
conditions.append(qtpylib.crossed_above(
|
|
dataframe['macdsignal'],
|
|
dataframe['macd']
|
|
))
|
|
if params['sell-trigger'] == 'sell-sar_reversal':
|
|
conditions.append(qtpylib.crossed_above(
|
|
dataframe['sar'],
|
|
dataframe['close']
|
|
))
|
|
|
|
if conditions:
|
|
dataframe.loc[
|
|
reduce(lambda x, y: x & y, conditions),
|
|
'sell'] = 1
|
|
|
|
return dataframe
|
|
|
|
return populate_sell_trend
|
|
|
|
@staticmethod
|
|
def sell_indicator_space() -> List[Dimension]:
|
|
"""
|
|
Define your Hyperopt space for searching sell strategy parameters.
|
|
"""
|
|
return [
|
|
Integer(75, 100, name='sell-mfi-value'),
|
|
Integer(50, 100, name='sell-fastd-value'),
|
|
Integer(50, 100, name='sell-adx-value'),
|
|
Integer(60, 100, name='sell-rsi-value'),
|
|
Categorical([True, False], name='sell-mfi-enabled'),
|
|
Categorical([True, False], name='sell-fastd-enabled'),
|
|
Categorical([True, False], name='sell-adx-enabled'),
|
|
Categorical([True, False], name='sell-rsi-enabled'),
|
|
Categorical(['sell-boll',
|
|
'sell-macd_cross_signal',
|
|
'sell-sar_reversal'],
|
|
name='sell-trigger')
|
|
]
|
|
|
|
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Based on TA indicators. Should be a copy of same method from strategy.
|
|
Must align to populate_indicators in this file.
|
|
Only used when --spaces does not include buy space.
|
|
"""
|
|
dataframe.loc[
|
|
(
|
|
(dataframe['close'] < dataframe['bb_lowerband']) &
|
|
(dataframe['mfi'] < 16) &
|
|
(dataframe['adx'] > 25) &
|
|
(dataframe['rsi'] < 21)
|
|
),
|
|
'buy'] = 1
|
|
|
|
return dataframe
|
|
|
|
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Based on TA indicators. Should be a copy of same method from strategy.
|
|
Must align to populate_indicators in this file.
|
|
Only used when --spaces does not include sell space.
|
|
"""
|
|
dataframe.loc[
|
|
(
|
|
(qtpylib.crossed_above(
|
|
dataframe['macdsignal'], dataframe['macd']
|
|
)) &
|
|
(dataframe['fastd'] > 54)
|
|
),
|
|
'sell'] = 1
|
|
|
|
return dataframe
|