307 lines
12 KiB
Python
307 lines
12 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
|
|
|
|
# --- Do not remove these libs ---
|
|
from functools import reduce
|
|
from typing import Any, Callable, Dict, List
|
|
|
|
import numpy as np # noqa
|
|
import pandas as pd # noqa
|
|
from pandas import DataFrame
|
|
from skopt.space import Categorical, Dimension, Integer, Real # noqa
|
|
|
|
from freqtrade.optimize.hyperopt_interface import IHyperOpt
|
|
|
|
# --------------------------------
|
|
# Add your lib to import here
|
|
import talib.abstract as ta # noqa
|
|
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
|
|
|
|
|
class AdvancedSampleHyperOpt(IHyperOpt):
|
|
"""
|
|
This is a sample hyperopt to inspire you.
|
|
Feel free to customize it.
|
|
|
|
More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
|
|
|
|
You should:
|
|
- Rename the class name to some unique name.
|
|
- Add any methods you want to build your hyperopt.
|
|
- Add any lib you need to build your hyperopt.
|
|
|
|
You must keep:
|
|
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
|
|
|
|
The methods roi_space, generate_roi_table and stoploss_space are not required
|
|
and are provided by default.
|
|
However, you may override them if you need the
|
|
'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
|
|
|
|
This sample illustrates how to override these methods.
|
|
"""
|
|
@staticmethod
|
|
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
This method can also be loaded from the strategy, if it doesn't exist in the hyperopt class.
|
|
"""
|
|
dataframe['adx'] = ta.ADX(dataframe)
|
|
macd = ta.MACD(dataframe)
|
|
dataframe['macd'] = macd['macd']
|
|
dataframe['macdsignal'] = macd['macdsignal']
|
|
dataframe['mfi'] = ta.MFI(dataframe)
|
|
dataframe['rsi'] = ta.RSI(dataframe)
|
|
stoch_fast = ta.STOCHF(dataframe)
|
|
dataframe['fastd'] = stoch_fast['fastd']
|
|
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']
|
|
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'] == 'bb_lower':
|
|
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']
|
|
))
|
|
|
|
# Check that volume is not 0
|
|
conditions.append(dataframe['volume'] > 0)
|
|
|
|
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 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(['bb_lower', '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
|
|
"""
|
|
# print(params)
|
|
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-bb_upper':
|
|
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']
|
|
))
|
|
|
|
# Check that volume is not 0
|
|
conditions.append(dataframe['volume'] > 0)
|
|
|
|
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-bb_upper',
|
|
'sell-macd_cross_signal',
|
|
'sell-sar_reversal'], name='sell-trigger')
|
|
]
|
|
|
|
@staticmethod
|
|
def generate_roi_table(params: Dict) -> Dict[int, float]:
|
|
"""
|
|
Generate the ROI table that will be used by Hyperopt
|
|
|
|
This implementation generates the default legacy Freqtrade ROI tables.
|
|
|
|
Change it if you need different number of steps in the generated
|
|
ROI tables or other structure of the ROI tables.
|
|
|
|
Please keep it aligned with parameters in the 'roi' optimization
|
|
hyperspace defined by the roi_space method.
|
|
"""
|
|
roi_table = {}
|
|
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
|
|
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
|
|
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
|
|
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
|
|
|
|
return roi_table
|
|
|
|
@staticmethod
|
|
def roi_space() -> List[Dimension]:
|
|
"""
|
|
Values to search for each ROI steps
|
|
|
|
Override it if you need some different ranges for the parameters in the
|
|
'roi' optimization hyperspace.
|
|
|
|
Please keep it aligned with the implementation of the
|
|
generate_roi_table method.
|
|
"""
|
|
return [
|
|
Integer(10, 120, name='roi_t1'),
|
|
Integer(10, 60, name='roi_t2'),
|
|
Integer(10, 40, name='roi_t3'),
|
|
Real(0.01, 0.04, name='roi_p1'),
|
|
Real(0.01, 0.07, name='roi_p2'),
|
|
Real(0.01, 0.20, name='roi_p3'),
|
|
]
|
|
|
|
@staticmethod
|
|
def stoploss_space() -> List[Dimension]:
|
|
"""
|
|
Stoploss Value to search
|
|
|
|
Override it if you need some different range for the parameter in the
|
|
'stoploss' optimization hyperspace.
|
|
"""
|
|
return [
|
|
Real(-0.35, -0.02, name='stoploss'),
|
|
]
|
|
|
|
@staticmethod
|
|
def trailing_space() -> List[Dimension]:
|
|
"""
|
|
Create a trailing stoploss space.
|
|
|
|
You may override it in your custom Hyperopt class.
|
|
"""
|
|
return [
|
|
# It was decided to always set trailing_stop is to True if the 'trailing' hyperspace
|
|
# is used. Otherwise hyperopt will vary other parameters that won't have effect if
|
|
# trailing_stop is set False.
|
|
# This parameter is included into the hyperspace dimensions rather than assigning
|
|
# it explicitly in the code in order to have it printed in the results along with
|
|
# other 'trailing' hyperspace parameters.
|
|
Categorical([True], name='trailing_stop'),
|
|
|
|
Real(0.01, 0.35, name='trailing_stop_positive'),
|
|
|
|
# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
|
|
# so this intermediate parameter is used as the value of the difference between
|
|
# them. The value of the 'trailing_stop_positive_offset' is constructed in the
|
|
# generate_trailing_params() method.
|
|
# This is similar to the hyperspace dimensions used for constructing the ROI tables.
|
|
Real(0.001, 0.1, name='trailing_stop_positive_offset_p1'),
|
|
|
|
Categorical([True, False], name='trailing_only_offset_is_reached'),
|
|
]
|
|
|
|
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Based on TA indicators.
|
|
Can be a copy of the corresponding method from the strategy,
|
|
or will be loaded from the strategy.
|
|
Must align to populate_indicators used (either from this File, or from the strategy)
|
|
Only used when --spaces does not include buy
|
|
"""
|
|
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.
|
|
Can be a copy of the corresponding method from the strategy,
|
|
or will be loaded from the strategy.
|
|
Must align to populate_indicators used (either from this File, or from the strategy)
|
|
Only used when --spaces does not include sell
|
|
"""
|
|
dataframe.loc[
|
|
(
|
|
(qtpylib.crossed_above(
|
|
dataframe['macdsignal'], dataframe['macd']
|
|
)) &
|
|
(dataframe['fastd'] > 54)
|
|
),
|
|
'sell'] = 1
|
|
return dataframe
|