Brain Stra added
This commit is contained in:
Masoud Azizi
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# brain Strategy Hyperopt
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# Author: @Mablue (Masoud Azizi)
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# github: https://github.com/mablue/
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# IMPORTANT: INSTALL TA BEFOUR RUN:
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# :~$ pip install ta
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# freqtrade hyperopt --hyperopt brainHo --hyperopt-loss SharpeHyperOptLossDaily --spaces buy sell roi --strategy brain -j 3 -e 700
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# --- Do not remove these libs ---
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from functools import reduce
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from typing import Any, Callable, Dict, List, Reversible
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import numpy as np # noqa
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import pandas as pd # noqa
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from pandas import DataFrame
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from skopt.space import Categorical, Dimension, Integer # noqa
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from freqtrade.optimize.hyperopt_interface import IHyperOpt
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# --------------------------------
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# Add your lib to import here
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# import talib.abstract as ta # noqa
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from ta import add_all_ta_features
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from ta.utils import dropna
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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##################### SETTINGS #########################
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# this is your trading brain nodes count
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# you can change it and see the results...
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# Importand will same with brain.py
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nodes = 4
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decimals = 2
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#################### END SETTINGS ######################
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# do not edit this line:
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decimals = 10 ** decimals
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PastKnowledges = ["open", "high", "low", "close", "volume", "volume_adi", "volume_obv",
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"volume_cmf", "volume_fi", "volume_mfi", "volume_em", "volume_sma_em", "volume_vpt",
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"volume_nvi", "volume_vwap", "volatility_atr", "volatility_bbm", "volatility_bbh",
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"volatility_bbl", "volatility_bbw", "volatility_bbp", "volatility_bbhi",
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"volatility_bbli", "volatility_kcc", "volatility_kch", "volatility_kcl",
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"volatility_kcw", "volatility_kcp", "volatility_kchi", "volatility_kcli",
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"volatility_dcl", "volatility_dch", "volatility_dcm", "volatility_dcw",
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"volatility_dcp", "volatility_ui", "trend_macd", "trend_macd_signal",
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"trend_macd_diff", "trend_sma_fast", "trend_sma_slow", "trend_ema_fast",
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"trend_ema_slow", "trend_adx", "trend_adx_pos", "trend_adx_neg", "trend_vortex_ind_pos",
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"trend_vortex_ind_neg", "trend_vortex_ind_diff", "trend_trix",
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"trend_mass_index", "trend_cci", "trend_dpo", "trend_kst",
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"trend_kst_sig", "trend_kst_diff", "trend_ichimoku_conv",
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"trend_ichimoku_base", "trend_ichimoku_a", "trend_ichimoku_b",
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"trend_visual_ichimoku_a", "trend_visual_ichimoku_b", "trend_aroon_up",
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"trend_aroon_down", "trend_aroon_ind", "trend_psar_up", "trend_psar_down",
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"trend_psar_up_indicator", "trend_psar_down_indicator", "trend_stc",
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"momentum_rsi", "momentum_stoch_rsi", "momentum_stoch_rsi_k",
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"momentum_stoch_rsi_d", "momentum_tsi", "momentum_uo", "momentum_stoch",
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"momentum_stoch_signal", "momentum_wr", "momentum_ao", "momentum_kama",
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"momentum_roc", "momentum_ppo", "momentum_ppo_signal", "momentum_ppo_hist",
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"others_dr", "others_dlr", "others_cr"]
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print(decimals)
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class brainHo(IHyperOpt):
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@staticmethod
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def indicator_space() -> List[Dimension]:
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"""
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Define your Hyperopt space for searching buy strategy parameters.
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"""
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brain = list()
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for i in range(nodes):
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brain.append(Categorical(PastKnowledges, name=f'buy-node-input-{i}'))
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brain.append(Categorical([0, 1], name=f'buy-node-enabled-{i}'))
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brain.append(Categorical([-1, 1], name=f'buy-node-reversed-{i}'))
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brain.append(Integer(0, decimals, name=f'buy-node-wight-{i}'))
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return brain
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@staticmethod
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def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
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"""
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Define the buy strategy parameters to be used by Hyperopt.
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"""
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def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Buy strategy Hyperopt will build and use.
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"""
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conditions = []
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RESULT = 0
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for i in range(nodes):
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DFINP = dataframe[params[f'buy-node-input-{i}']]
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ENABLED = params[f'buy-node-enabled-{i}']
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REVERSE = params[f'buy-node-reversed-{i}']
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WIGHT = params[f'buy-node-wight-{i}']/decimals
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RESULT += DFINP*ENABLED*REVERSE*WIGHT
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conditions.append(RESULT > 0)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'buy'] = 1
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return dataframe
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return populate_buy_trend
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@ staticmethod
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def sell_indicator_space() -> List[Dimension]:
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"""
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Define your Hyperopt space for searching sell strategy parameters.
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"""
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brain = list()
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for i in range(nodes):
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brain.append(Categorical(PastKnowledges, name=f'sell-node-input-{i}'))
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brain.append(Categorical([0, 1], name=f'sell-node-enabled-{i}'))
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brain.append(Categorical([-1, 1], name=f'sell-node-reversed-{i}'))
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brain.append(Integer(0, decimals, name=f'sell-node-wight-{i}'))
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return brain
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@ staticmethod
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def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
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"""
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Define the sell strategy parameters to be used by Hyperopt.
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"""
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def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Sell strategy Hyperopt will build and use.
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"""
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conditions = []
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RESULT = 0
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for i in range(nodes):
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DFINP = dataframe[params[f'sell-node-input-{i}']]
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ENABLED = params[f'sell-node-enabled-{i}']
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REVERSE = params[f'sell-node-reversed-{i}']
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WIGHT = params[f'sell-node-wight-{i}']/decimals
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RESULT += DFINP*ENABLED*REVERSE*WIGHT
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conditions.append(RESULT > 0)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'sell']=1
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return dataframe
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return populate_sell_trend
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@@ -0,0 +1,144 @@
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# brain Strategy
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# Author: @Mablue (Masoud Azizi)
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# github: https://github.com/mablue/
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# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
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# freqtrade hyperopt --hyperopt brainHo --hyperopt-loss SharpeHyperOptLossDaily --spaces buy sell roi --strategy brain -j 3 -e 700
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# --- Do not remove these libs ---
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import logging
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from numpy.lib import math
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from freqtrade.strategy.interface import IStrategy
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from pandas import DataFrame
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# --------------------------------
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# Add your lib to import here
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# import talib.abstract as ta
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import pandas as pd
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# import talib.abstract as ta
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from ta import add_all_ta_features
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from ta.utils import dropna
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from functools import reduce
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import numpy as np
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class brain(IStrategy):
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##################### SETTINGS #########################
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# this is your trading brain nodes count
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# you can change it and see the results...
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# Importand will same with brainHo.py
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nodes = 4
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# 1 means 1, 10 means 0.1, 100 means 0.01
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decimals = 2
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#################### END SETTINGS ######################
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##################### HYPEROPT RESULTS PASTE PLACE #########################
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# * 10/700: 178 trades. 103/59/16 Wins/Draws/Losses. Avg profit 1.35%. Median profit 2.30%. Total profit 0.02400559 BTC ( 24.01Σ%). Avg duration 1 day, 18:31:00 min. Objective: -5.31583
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# Buy hyperspace params:
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buy_params = {
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"buy-node-input-0": "trend_mass_index",
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"buy-node-enabled-0": 0,
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"buy-node-reversed-0": 1,
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"buy-node-wight-0": 81,
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"buy-node-input-1": "momentum_ao",
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"buy-node-enabled-1": 0,
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"buy-node-reversed-1": -1,
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"buy-node-wight-1": 36,
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"buy-node-input-2": "volatility_ui",
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"buy-node-enabled-2": 1,
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"buy-node-reversed-2": -1,
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"buy-node-wight-2": 59,
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"buy-node-input-3": "volatility_kcp",
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"buy-node-enabled-3": 1,
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"buy-node-reversed-3": 1,
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"buy-node-wight-3": 76,
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}
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# Sell hyperspace params:
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sell_params = {
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"sell-node-input-0": "volatility_bbp",
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"sell-node-enabled-0": 0,
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"sell-node-reversed-0": -1,
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"sell-node-wight-0": 25,
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"sell-node-input-1": "trend_vortex_ind_diff",
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"sell-node-enabled-1": 0,
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"sell-node-reversed-1": 1,
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"sell-node-wight-1": 1,
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"sell-node-input-2": "trend_macd",
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"sell-node-enabled-2": 0,
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"sell-node-reversed-2": -1,
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"sell-node-wight-2": 4,
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"sell-node-input-3": "momentum_ppo_hist",
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"sell-node-enabled-3": 0,
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"sell-node-reversed-3": -1,
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"sell-node-wight-3": 72,
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}
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# ROI table:
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minimal_roi = {
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"0": 0.347,
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"392": 0.126,
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"727": 0.023,
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"1411": 0
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}
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# Stoploss:
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stoploss = -0.256
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#################### END HYPEROPT RESULTS PASTE PLACE #######################
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# Buy hypers
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timeframe = '1h'
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# do not edit this line:
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decimals = 10 ** decimals
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# Add all ta features
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# dataframe = dropna(dataframe)
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dataframe = add_all_ta_features(
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dataframe, open="open", high="high", low="low", close="close", volume="volume", fillna=False)
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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conditions = []
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RESULT = 0
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for i in range(self.nodes):
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DFINP = dataframe[self.buy_params[f'buy-node-input-{i}']]
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ENABLED = self.buy_params[f'buy-node-enabled-{i}']
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REVERSE = self.buy_params[f'buy-node-reversed-{i}']
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WIGHT = self.buy_params[f'buy-node-wight-{i}']/self.decimals
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RESULT += DFINP*ENABLED*REVERSE*WIGHT
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conditions.append(RESULT > 0)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'buy'] = 1
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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conditions = []
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RESULT = 0
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for i in range(self.nodes):
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DFINP = dataframe[self.sell_params[f'sell-node-input-{i}']]
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ENABLED = self.sell_params[f'sell-node-enabled-{i}']
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REVERSE = self.sell_params[f'sell-node-reversed-{i}']
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WIGHT = self.sell_params[f'sell-node-wight-{i}']/self.decimals
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RESULT += DFINP*ENABLED*REVERSE*WIGHT
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conditions.append(RESULT > 0)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'sell']=1
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return dataframe
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