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heracles in New API, Bugs fixed

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This commit is contained in:
Masoud Azizi
2021-06-27 03:12:18 +00:00
parent 6158f1e714
commit 9880524f05
2 changed files with 115 additions and 187 deletions
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user_data/hyperopts/HeraclesHo.py
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# 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
+115 -69
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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 sell --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
@@ -31,103 +26,154 @@ from functools import reduce
import numpy as np
def normalize(df):
# To enable normalization outcomment below line:
df = (df-df.min())/(df.max()-df.min())
return df
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 ##########################################
# 35/50: 129 trades. 96/15/18 Wins/Draws/Losses. Avg profit 3.57%. Median profit 4.30%. Total profit 2302.93351920 USDT ( 46.06Σ%). Avg duration 2 days, 19:04:00 min. Objective: -21.29091
# Buy hyperspace params:
buy_params = {
'buy-cross-0': 'volatility_kcw',
'buy-indicator-0': 'volatility_dcp',
'buy-oper-0': '<',
"buy_crossed_indicator_shift": -5,
"buy_div": 4.7968,
"buy_indicator_shift": 5,
}
# Sell hyperspace params:
sell_params = {
'sell-cross-0': 'trend_macd_signal',
'sell-indicator-0': 'trend_ema_fast',
'sell-oper-0': '=',
"sell_atol": 0.21256,
"sell_crossed_indicator_shift": 0,
"sell_indicator_shift": -1,
"sell_rtol": 0.11195,
}
# ROI table:
minimal_roi = {
"0": 0.32836,
"1629": 0.17896,
"6302": 0.05372,
"10744": 0
"0": 0.43,
"994": 0.076,
"2864": 0.043,
"6947": 0
}
# Stoploss:
stoploss = -0.04655
stoploss = -0.312
########################################## END RESULT PASTE PLACE ######################################
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.02444
trailing_stop_positive_offset = 0.04406
trailing_only_offset_is_reached = True
# Buy hypers
timeframe = '12h'
# buy params
buy_div = DecimalParameter(-5, 5, default=0.51844, decimals=4, space='buy')
buy_indicator_shift = IntParameter(-5, 5, default=4, space='buy')
buy_crossed_indicator_shift = IntParameter(-5, 5, default=1, space='buy')
# sell params
sell_rtol = DecimalParameter(1.e-10, 1.e-0, default=0.05468, decimals=10, space='sell')
sell_atol = DecimalParameter(1.e-16, 1.e-0, default=0.00019, decimals=10, space='sell')
sell_indicator_shift = IntParameter(-5, 5, default=4, space='sell')
sell_crossed_indicator_shift = IntParameter(-5, 5, default=1, space='sell')
# Optimal timeframe use it in your config
timeframe = '4h'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Add all ta features
dataframe = dropna(dataframe)
dataframe['volatility_kcw'] = ta.volatility.keltner_channel_wband(
dataframe['high'],
dataframe['low'],
dataframe['close'],
window=20,
window_atr=10,
fillna=False,
original_version=True
)
dataframe['volatility_dcp'] = ta.volatility.donchian_channel_pband(
dataframe['high'],
dataframe['low'],
dataframe['close'],
window=10,
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['volatility_kcw'] = normalize(ta.volatility.keltner_channel_wband(
dataframe['high'],
dataframe['low'],
dataframe['close'],
window=20,
window_atr=10,
fillna=False,
original_version=True
))
dataframe['volatility_dcp'] =normalize(ta.volatility.donchian_channel_pband(
dataframe['high'],
dataframe['low'],
dataframe['close'],
window=10,
offset=0,
fillna=False
))
dataframe['trend_macd_signal'] =normalize(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()
dataframe['trend_ema_fast'] =normalize(ta.trend.EMAIndicator(
close=dataframe['close'], window=12, fillna=False
).ema_indicator())
# for checking crossovers!
# but we dont need to crossovers we just calculate dividation
# import matplotlib.pyplot as plt
# dataframe.iloc[:,6:].plot(subplots=False)
# plt.tight_layout()
# plt.show()
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
conditions.append(
DFIND.shift(self.buy_indicator_shift.value).div(
DFCRS.shift(self.buy_crossed_indicator_shift.value)
) <= self.buy_div.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']
"""
Sell strategy Hyperopt will build and use.
"""
conditions = []
IND = 'trend_ema_fast'
CRS = 'trend_macd_signal'
DFIND = dataframe[IND]
DFCRS = dataframe[CRS]
dataframe.loc[
(qtpylib.crossed_below(DFIND, DFCRS)),
'sell'] = 1
conditions.append(
np.isclose(
DFIND.shift(self.sell_indicator_shift.value),
DFCRS.shift(self.sell_crossed_indicator_shift.value),
rtol=self.sell_rtol.value,
atol=self.sell_rtol.value
)
)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell']=1
return dataframe