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advanced sample hyperopt added; changes to helpstrings

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hroff-1902 2019-08-08 22:45:37 +03:00
parent c6444a10a8
commit 0d4a2c6c3a
3 changed files with 271 additions and 9 deletions
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3
freqtrade/optimize/default_hyperopt.py
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@ -13,10 +13,9 @@ from freqtrade.optimize.hyperopt_interface import IHyperOpt
class DefaultHyperOpts(IHyperOpt):
"""
Default hyperopt provided by freqtrade bot.
Default hyperopt provided by the Freqtrade bot.
You can override it with your own hyperopt
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe)

16
user_data/hyperopts/sample_hyperopt.py
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@ -14,25 +14,27 @@ import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
# This class is a sample. Feel free to customize it.
class SampleHyperOpts(IHyperOpt):
"""
This is a test hyperopt to inspire you.
This is a sample hyperopt to inspire you.
Feel free to customize it.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
You can:
- Rename the class name.
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 roi_space, generate_roi_table, stoploss_space methods were moved to the parent class, you may
override them here if you need it.
The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
copied in every custom hyperopt. However, you may override them if you need the
'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
Sample implementation of these methods can be found in
https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe)

261
user_data/hyperopts/sample_hyperopt_advanced.py Normal file
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@ -0,0 +1,261 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
from functools import reduce
from math import exp
from typing import Any, Callable, Dict, List
from datetime import datetime
import numpy as np# noqa F401
import talib.abstract as ta
from pandas import DataFrame
from skopt.space import Categorical, Dimension, Integer, Real
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
class AdvancedSampleHyperOpts(IHyperOpt):
"""
This is a sample hyperopt to inspire you.
Feel free to customize it.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
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 roi_space, generate_roi_table, stoploss_space methods are no longer required to be
copied in every custom hyperopt. 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:
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']
))
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']
))
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(-0.5, -0.02, name='stoploss'),
]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of from strategy
must align to populate_indicators in this file
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. Should be a copy of from strategy
must align to populate_indicators in this file
Only used when --spaces does not include sell
"""
dataframe.loc[
(
(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
)) &
(dataframe['fastd'] > 54)
),
'sell'] = 1
return dataframe