Masoud AziziandGitHub
179282b0be
# Heracles Strategy Hyperopt # Author: @Mablue (Masoud Azizi) # github: https://github.com/mablue/ # IMPORTANT: INSTALL TA BEFOUR RUN: # :~$ pip install ta # freqtrade hyperopt --hyperopt HerculesHo --hyperopt-loss SharpeHyperOptLossDaily --spaces all --strategy Hercules --config config.json -e 100
119 lines
3.5 KiB
Python
119 lines
3.5 KiB
Python
# Heracles Strategy Hyperopt
|
|
# Author: @Mablue (Masoud Azizi)
|
|
# github: https://github.com/mablue/
|
|
# IMPORTANT: INSTALL TA BEFOUR RUN:
|
|
# :~$ pip install ta
|
|
# freqtrade hyperopt --hyperopt GodStraHo --hyperopt-loss SharpeHyperOptLossDaily --gene all --strategy GodStra --config config.json -e 100
|
|
|
|
# --- 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
|
|
from ta import add_all_ta_features
|
|
from ta.utils import dropna
|
|
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
|
# this is your trading strategy DNA Size
|
|
# you can change it and see the results...
|
|
|
|
|
|
class HeraclesHo(IHyperOpt):
|
|
|
|
@staticmethod
|
|
def indicator_space() -> List[Dimension]:
|
|
"""
|
|
Define your Hyperopt space for searching buy strategy parameters.
|
|
"""
|
|
|
|
return [
|
|
Real(-0.1, 1.1, name='buy-div'),
|
|
Integer(0, 5, name='DFINDShift'),
|
|
Integer(0, 5, name='DFCRSShift'),
|
|
]
|
|
|
|
@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 = []
|
|
|
|
IND = 'volatility_dcp'
|
|
CRS = 'volatility_kcw'
|
|
DFIND = dataframe[IND]
|
|
DFCRS = dataframe[CRS]
|
|
|
|
conditions.append(
|
|
DFIND.shift(params['DFINDShift']).div(
|
|
DFCRS.shift(params['DFCRSShift'])
|
|
) <= params['buy-div']
|
|
)
|
|
|
|
if conditions:
|
|
dataframe.loc[
|
|
reduce(lambda x, y: x & y, conditions),
|
|
'buy'] = 1
|
|
|
|
return dataframe
|
|
|
|
return populate_buy_trend
|
|
|
|
@ staticmethod
|
|
def sell_indicator_space() -> List[Dimension]:
|
|
"""
|
|
Define your Hyperopt space for searching sell strategy parameters.
|
|
"""
|
|
return [
|
|
Real(1.e-10, 1.e-0, name='sell-rtol'),
|
|
Real(1.e-16, 1.e-0, name='sell-atol'),
|
|
Integer(0, 5, name='DFINDShift'),
|
|
Integer(0, 5, name='DFCRSShift'),
|
|
]
|
|
|
|
@ 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 = []
|
|
|
|
IND = 'trend_ema_fast'
|
|
CRS = 'trend_macd_signal'
|
|
DFIND = dataframe[IND]
|
|
DFCRS = dataframe[CRS]
|
|
|
|
conditions.append(
|
|
np.isclose(
|
|
DFIND.shift(params['DFINDShift']),
|
|
DFCRS.shift(params['DFCRSShift']),
|
|
rtol=params['sell-rtol'],
|
|
atol=params['sell-atol']
|
|
)
|
|
)
|
|
|
|
if conditions:
|
|
dataframe.loc[
|
|
reduce(lambda x, y: x & y, conditions),
|
|
'sell']=1
|
|
|
|
return dataframe
|
|
|
|
return populate_sell_trend
|