Skip to content

Commit

Permalink
heracles in New API, Bugs fixed
Browse files Browse the repository at this point in the history
  • Loading branch information
@mablue
mablue committed Jun 27, 2021
1 parent 6158f1e commit 9880524
Show file tree
Hide file tree
Showing 2 changed files with 116 additions and 188 deletions.
118 changes: 0 additions & 118 deletions user_data/hyperopts/HeraclesHo.py
View file

This file was deleted.

186 changes: 116 additions & 70 deletions user_data/strategies/Heracles.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand All @@ -31,103 +26,154 @@
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 ######################################


# 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')

# 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'
# 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['trend_ema_fast'] = ta.trend.EMAIndicator(
close=dataframe['close'], window=12, fillna=False
).ema_indicator()
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'] =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

0 comments on commit 9880524

Please sign in to comment.