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Merge pull request #180 from mablue/mabStra

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Mab stra
This commit is contained in:
Matthias
2021-05-19 06:31:45 +02:00
committed by GitHub
parent 054ef8178e 4c193a05ca
commit ba6faa7620
2 changed files with 43 additions and 134 deletions
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user_data/hyperopts/mabStraHo.py
-95
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@@ -1,95 +0,0 @@
# by: Mablue (Masoud Azizi)
# --- 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
import freqtrade.vendor.qtpylib.indicators as qtpylib
class mabStraHo(IHyperOpt):
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching buy strategy parameters.
"""
return [
Real(0, 1, name='buy-div-min'),
Real(0, 1, name='buy-div-max'),
]
@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
# div result limited to 0~1 so allways in buy position slowMa is lower than fastMa
# optimum number will calculate by this two lines
conditions.append(dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
> params['buy-div-min'])
conditions.append(dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
< params['buy-div-max'])
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(0, 1, name='sell-div-min'),
Real(0, 1, name='sell-div-max'),
]
@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 = []
# GUARDS AND TRENDS
conditions.append(dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
> params['sell-div-min'])
conditions.append(dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
< params['sell-div-max'])
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe
return populate_sell_trend
user_data/strategies/mabStra.py
+43 -39
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@@ -1,6 +1,10 @@
# author: Masoud Azizi @mablue
# Author: @Mablue (Masoud Azizi)
# github: https://github.com/mablue/
# IMPORTANT: DO NOT USE IT WITHOUT HYPEROPT:
# freqtrade hyperopt --hyperopt mabStraHo --hyperopt-loss SharpeHyperOptLoss --spaces all --strategy mabStra --config config.json -e 100
# --- Do not remove these libs ---
from freqtrade.strategy.hyper import IntParameter, DecimalParameter
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------
@@ -9,58 +13,54 @@ from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
FTF, STF = 5, 10
class mabStra(IStrategy):
# buy params
buy_mojo_ma_timeframe = IntParameter(2, 100, default=7, space='buy')
buy_fast_ma_timeframe = IntParameter(2, 100, default=14, space='buy')
buy_slow_ma_timeframe = IntParameter(2, 100, default=28, space='buy')
buy_div_max = DecimalParameter(0, 2, decimals=4, default=2.25446, space='buy')
buy_div_min = DecimalParameter(0, 2, decimals=4, default=0.29497, space='buy')
# sell params
sell_mojo_ma_timeframe = IntParameter(2, 100, default=7, space='sell')
sell_fast_ma_timeframe = IntParameter(2, 100, default=14, space='sell')
sell_slow_ma_timeframe = IntParameter(2, 100, default=28, space='sell')
sell_div_max = DecimalParameter(0, 2, decimals=4, default=1.54593, space='sell')
sell_div_min = DecimalParameter(0, 2, decimals=4, default=2.81436, space='sell')
# 100/100: 727 trades. 486/191/50 Wins/Draws/Losses. Avg profit 3.53 % . Median profit 5.97 % . Total profit 1502.52014358 USDT (2566.80Σ %). Avg duration 1396.1 min. Objective: -15.62092
stoploss = -0.1
# Buy hyperspace params:
buy_params = {
'buy-div-max': 0.96451, 'buy-div-min': 0.22313
}
# Sell hyperspace params:
sell_params = {
'sell-div-max': 0.75476, 'sell-div-min': 0.16599
}
# ROI table:
minimal_roi = {
"0": 0.45574,
"307": 0.21971,
"428": 0.06762,
"1387": 0
}
# Stoploss:
stoploss = -0.34773
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.01573
trailing_stop_positive_offset = 0.06651
trailing_only_offset_is_reached = True
# Optimal timeframe use it in your config
timeframe = '1h'
timeframe = '4h'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# SMA - ex Moving Average
dataframe['buy-fastMA'] = ta.SMA(dataframe, timeperiod=FTF)
dataframe['buy-slowMA'] = ta.SMA(dataframe, timeperiod=STF)
dataframe['sell-fastMA'] = ta.SMA(dataframe, timeperiod=FTF)
dataframe['sell-slowMA'] = ta.SMA(dataframe, timeperiod=STF)
dataframe['buy-mojoMA'] = ta.SMA(dataframe,
timeperiod=self.buy_mojo_ma_timeframe.value)
dataframe['buy-fastMA'] = ta.SMA(dataframe,
timeperiod=self.buy_fast_ma_timeframe.value)
dataframe['buy-slowMA'] = ta.SMA(dataframe,
timeperiod=self.buy_slow_ma_timeframe.value)
dataframe['sell-mojoMA'] = ta.SMA(dataframe,
timeperiod=self.sell_mojo_ma_timeframe.value)
dataframe['sell-fastMA'] = ta.SMA(dataframe,
timeperiod=self.sell_fast_ma_timeframe.value)
dataframe['sell-slowMA'] = ta.SMA(dataframe,
timeperiod=self.sell_slow_ma_timeframe.value)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['buy-mojoMA'].div(dataframe['buy-fastMA'])
> self.buy_div_min.value) &
(dataframe['buy-mojoMA'].div(dataframe['buy-fastMA'])
< self.buy_div_max.value) &
(dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
> self.buy_params['buy-div-min']) &
> self.buy_div_min.value) &
(dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
< self.buy_params['buy-div-max'])
< self.buy_div_max.value)
),
'buy'] = 1
@@ -69,10 +69,14 @@ class mabStra(IStrategy):
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['sell-fastMA'].div(dataframe['sell-mojoMA'])
> self.sell_div_min.value) &
(dataframe['sell-fastMA'].div(dataframe['sell-mojoMA'])
< self.sell_div_max.value) &
(dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
> self.sell_params['sell-div-min']) &
> self.sell_div_min.value) &
(dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
< self.sell_params['sell-div-max'])
< self.sell_div_max.value)
),
'sell'] = 1
return dataframe