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Merge pull request #202 from mablue/heracles

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heracles in New API, Bugs fixed
This commit is contained in:
Matthias
2021-07-27 19:58:18 +02:00
committed by GitHub
parent efe10bb09b e0acc18d2c
commit 2ee75777e8
2 changed files with 44 additions and 174 deletions
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user_data/hyperopts/HeraclesHo.py
-118
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@@ -1,118 +0,0 @@
# 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
user_data/strategies/Heracles.py
+44 -56
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@@ -8,19 +8,14 @@
# "min_days_listed": 100
# },
# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
#
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces roi buy --strategy Heracles
# ######################################################################
# Optimal config settings:
# "max_open_trades": 100,
# "stake_amount": "unlimited",
# --- Do not remove these libs ---
import logging
from numpy.lib import math
from freqtrade.strategy.hyper import IntParameter, DecimalParameter
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------
# Add your lib to import here
# import talib.abstract as ta
import pandas as pd
@@ -32,47 +27,44 @@ import numpy as np
class Heracles(IStrategy):
# 65/600: 2275 trades. 1438/7/830 W/D/L.
# Avg profit 3.10%. Median profit 3.06%.
# Total profit 113171 USDT ( 7062 Σ%).
# Avg duration 345 min. Objective: -23.0
########################################## RESULT PASTE PLACE ##########################################
# 10/100: 25 trades. 18/4/3 Wins/Draws/Losses. Avg profit 5.92%. Median profit 6.33%. Total profit 0.04888306 BTC ( 48.88Σ%). Avg duration 4 days, 6:24:00 min. Objective: -11.42103
# Buy hyperspace params:
buy_params = {
'buy-cross-0': 'volatility_kcw',
'buy-indicator-0': 'volatility_dcp',
'buy-oper-0': '<',
"buy_crossed_indicator_shift": 9,
"buy_div_max": 0.75,
"buy_div_min": 0.16,
"buy_indicator_shift": 15,
}
# Sell hyperspace params:
sell_params = {
'sell-cross-0': 'trend_macd_signal',
'sell-indicator-0': 'trend_ema_fast',
'sell-oper-0': '=',
}
# ROI table:
minimal_roi = {
"0": 0.32836,
"1629": 0.17896,
"6302": 0.05372,
"10744": 0
"0": 0.598,
"644": 0.166,
"3269": 0.115,
"7289": 0
}
# Stoploss:
stoploss = -0.04655
stoploss = -0.256
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.02444
trailing_stop_positive_offset = 0.04406
trailing_only_offset_is_reached = True
# Optimal timeframe use it in your config
timeframe = '4h'
# Buy hypers
timeframe = '12h'
########################################## END RESULT PASTE PLACE ######################################
# buy params
buy_div_min = DecimalParameter(0, 1, default=0.16, decimals=2, space='buy')
buy_div_max = DecimalParameter(0, 1, default=0.75, decimals=2, space='buy')
buy_indicator_shift = IntParameter(0, 20, default=16, space='buy')
buy_crossed_indicator_shift = IntParameter(0, 20, default=9, space='buy')
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Add all ta features
dataframe = dropna(dataframe)
dataframe['volatility_kcw'] = ta.volatility.keltner_channel_wband(
@@ -84,6 +76,7 @@ class Heracles(IStrategy):
fillna=False,
original_version=True
)
dataframe['volatility_dcp'] = ta.volatility.donchian_channel_pband(
dataframe['high'],
dataframe['low'],
@@ -92,42 +85,37 @@ class Heracles(IStrategy):
offset=0,
fillna=False
)
dataframe['trend_macd_signal'] = ta.trend.macd_signal(
dataframe['close'],
window_slow=26,
window_fast=12,
window_sign=9,
fillna=False
)
dataframe['trend_ema_fast'] = ta.trend.EMAIndicator(
close=dataframe['close'], window=12, fillna=False
).ema_indicator()
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use.
"""
conditions = []
IND = self.buy_params['buy-indicator-0']
CRS = self.buy_params['buy-cross-0']
IND = 'volatility_dcp'
CRS = 'volatility_kcw'
DFIND = dataframe[IND]
DFCRS = dataframe[CRS]
dataframe.loc[
(DFIND < DFCRS),
'buy'] = 1
d = DFIND.shift(self.buy_indicator_shift.value).div(
DFCRS.shift(self.buy_crossed_indicator_shift.value))
# print(d.min(), "\t", d.max())
conditions.append(
d.between(self.buy_div_min.value, self.buy_div_max.value))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy']=1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
IND = self.sell_params['sell-indicator-0']
CRS = self.sell_params['sell-cross-0']
DFIND = dataframe[IND]
DFCRS = dataframe[CRS]
dataframe.loc[
(qtpylib.crossed_below(DFIND, DFCRS)),
'sell'] = 1
"""
Sell strategy Hyperopt will build and use.
"""
return dataframe