Merge pull request #157 from mablue/master
GodStra Strategy file + Hyperopt
This commit is contained in:
Matthias
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# GodStra 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 GodStraHo --hyperopt-loss SharpeHyperOptLossDaily --spaces all --strategy GodStra --config config.json -e 100
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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
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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, Real # 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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# this is your trading strategy DNA Size
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# you can change it and see the results...
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DNA_SIZE = 1
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GodGenes = ["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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class GodStraHo(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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gene = list()
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for i in range(DNA_SIZE):
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gene.append(Categorical(GodGenes, name=f'buy-indicator-{i}'))
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gene.append(Categorical(GodGenes, name=f'buy-cross-{i}'))
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gene.append(Integer(-1, 101, name=f'buy-int-{i}'))
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gene.append(Real(-1.1, 1.1, name=f'buy-real-{i}'))
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# Operations
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# CA: Crossed Above, CB: Crossed Below,
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# I: Integer, R: Real, D: Disabled
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gene.append(Categorical(["D", ">", "<", "=", "CA", "CB",
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">I", "=I", "<I", ">R", "=R", "<R"], name=f'buy-oper-{i}'))
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return gene
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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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# GUARDS AND TRENDS
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for i in range(DNA_SIZE):
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OPR = params[f'buy-oper-{i}']
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IND = params[f'buy-indicator-{i}']
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CRS = params[f'buy-cross-{i}']
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INT = params[f'buy-int-{i}']
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REAL = params[f'buy-real-{i}']
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DFIND = dataframe[IND]
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DFCRS = dataframe[CRS]
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if OPR == ">":
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conditions.append(DFIND > DFCRS)
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elif OPR == "=":
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conditions.append(np.isclose(DFIND, DFCRS))
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elif OPR == "<":
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conditions.append(DFIND < DFCRS)
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elif OPR == "CA":
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conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
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elif OPR == "CB":
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conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
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elif OPR == ">I":
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conditions.append(DFIND > INT)
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elif OPR == "=I":
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conditions.append(DFIND == INT)
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elif OPR == "<I":
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conditions.append(DFIND < INT)
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elif OPR == ">R":
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conditions.append(DFIND > REAL)
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elif OPR == "=R":
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conditions.append(np.isclose(DFIND, REAL))
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elif OPR == "<R":
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conditions.append(DFIND < REAL)
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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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gene = list()
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for i in range(DNA_SIZE):
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gene.append(Categorical(GodGenes, name=f'sell-indicator-{i}'))
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gene.append(Categorical(GodGenes, name=f'sell-cross-{i}'))
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gene.append(Integer(-1, 101, name=f'sell-int-{i}'))
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gene.append(Real(-0.01, 1.01, name=f'sell-real-{i}'))
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# Operations
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# CA: Crossed Above, CB: Crossed Below,
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# I: Integer, R: Real, D: Disabled
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gene.append(Categorical(["D", ">", "<", "=", "CA", "CB",
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">I", "=I", "<I", ">R", "=R", "<R"], name=f'sell-oper-{i}'))
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return gene
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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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# GUARDS AND TRENDS
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for i in range(DNA_SIZE):
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OPR = params[f'sell-oper-{i}']
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IND = params[f'sell-indicator-{i}']
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CRS = params[f'sell-cross-{i}']
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INT = params[f'sell-int-{i}']
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REAL = params[f'sell-real-{i}']
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DFIND = dataframe[IND]
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DFCRS = dataframe[CRS]
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if OPR == ">":
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conditions.append(DFIND > DFCRS)
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elif OPR == "=":
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conditions.append(np.isclose(DFIND, DFCRS))
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elif OPR == "<":
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conditions.append(DFIND < DFCRS)
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elif OPR == "CA":
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conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
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elif OPR == "CB":
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conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
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elif OPR == ">I":
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conditions.append(DFIND > INT)
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elif OPR == "=I":
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conditions.append(DFIND == INT)
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elif OPR == "<I":
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conditions.append(DFIND < INT)
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elif OPR == ">R":
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conditions.append(DFIND > REAL)
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elif OPR == "=R":
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conditions.append(np.isclose(DFIND, REAL))
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elif OPR == "<R":
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conditions.append(DFIND < REAL)
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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 +1,118 @@
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# Heracles 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 GodStraHo --hyperopt-loss SharpeHyperOptLossDaily --gene all --strategy GodStra --config config.json -e 100
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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
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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, Real # 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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# this is your trading strategy DNA Size
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# you can change it and see the results...
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class HeraclesHo(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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return [
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Real(-0.1, 1.1, name='buy-div'),
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Integer(0, 5, name='DFINDShift'),
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Integer(0, 5, name='DFCRSShift'),
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]
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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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IND = 'volatility_dcp'
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CRS = 'volatility_kcw'
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DFIND = dataframe[IND]
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DFCRS = dataframe[CRS]
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conditions.append(
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DFIND.shift(params['DFINDShift']).div(
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DFCRS.shift(params['DFCRSShift'])
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) <= params['buy-div']
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)
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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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return [
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Real(1.e-10, 1.e-0, name='sell-rtol'),
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Real(1.e-16, 1.e-0, name='sell-atol'),
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Integer(0, 5, name='DFINDShift'),
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Integer(0, 5, name='DFCRSShift'),
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]
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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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IND = 'trend_ema_fast'
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CRS = 'trend_macd_signal'
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DFIND = dataframe[IND]
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DFCRS = dataframe[CRS]
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conditions.append(
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np.isclose(
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DFIND.shift(params['DFINDShift']),
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DFCRS.shift(params['DFCRSShift']),
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rtol=params['sell-rtol'],
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atol=params['sell-atol']
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)
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)
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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 +1,167 @@
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# GodStra Strategy
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# Author: @Mablue (Masoud Azizi)
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# github: https://github.com/mablue/
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# IMPORTANT:Add to your pairlists inside config.json (Under StaticPairList):
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# {
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# "method": "AgeFilter",
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# "min_days_listed": 30
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# },
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# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
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# IMPORTANT: Use Smallest "max_open_trades" for getting best results inside config.json
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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 GodStra(IStrategy):
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# 5/66: 9 trades. 8/0/1 Wins/Draws/Losses. Avg profit 21.83%. Median profit 35.52%. Total profit 1060.11476586 USDT ( 196.50Σ%). Avg duration 3440.0 min. Objective: -7.06960
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# +--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------+
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# | Best | Epoch | Trades | Win Draw Loss | Avg profit | Profit | Avg duration | Objective |
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# |--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------|
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# | * Best | 1/500 | 11 | 2 1 8 | 5.22% | 280.74230393 USDT (57.40%) | 2,421.8 m | -2.85206 |
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# | * Best | 2/500 | 10 | 7 0 3 | 18.76% | 983.46414442 USDT (187.58%) | 360.0 m | -4.32665 |
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# | * Best | 5/500 | 9 | 8 0 1 | 21.83% | 1,060.11476586 USDT (196.50%) | 3,440.0 m | -7.0696 |
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# Buy hyperspace params:
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buy_params = {
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'buy-cross-0': 'volatility_kcc',
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'buy-indicator-0': 'trend_ichimoku_base',
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'buy-int-0': 42,
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'buy-oper-0': '<R',
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'buy-real-0': 0.06295
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}
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# Sell hyperspace params:
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sell_params = {
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'sell-cross-0': 'volume_mfi',
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'sell-indicator-0': 'trend_kst_diff',
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'sell-int-0': 98,
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'sell-oper-0': '=R',
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'sell-real-0': 0.8779
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}
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# ROI table:
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minimal_roi = {
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"0": 0.3556,
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"4818": 0.21275,
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"6395": 0.09024,
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"22372": 0
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}
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# Stoploss:
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stoploss = -0.34549
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# Trailing stop:
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trailing_stop = True
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trailing_stop_positive = 0.22673
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trailing_stop_positive_offset = 0.2684
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trailing_only_offset_is_reached = True
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# Buy hypers
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timeframe = '12h'
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print('Add {\n\t"method": "AgeFilter",\n\t"min_days_listed": 30\n},\n to your pairlists in config (Under StaticPairList)')
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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=True)
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# dataframe.to_csv("df.csv", index=True)
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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 = list()
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# /5: Cuz We have 5 Group of variables inside buy_param
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for i in range(int(len(self.buy_params)/5)):
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OPR = self.buy_params[f'buy-oper-{i}']
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IND = self.buy_params[f'buy-indicator-{i}']
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CRS = self.buy_params[f'buy-cross-{i}']
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INT = self.buy_params[f'buy-int-{i}']
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REAL = self.buy_params[f'buy-real-{i}']
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DFIND = dataframe[IND]
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DFCRS = dataframe[CRS]
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if OPR == ">":
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conditions.append(DFIND > DFCRS)
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elif OPR == "=":
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conditions.append(np.isclose(DFIND, DFCRS))
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elif OPR == "<":
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conditions.append(DFIND < DFCRS)
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elif OPR == "CA":
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conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
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elif OPR == "CB":
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conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
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elif OPR == ">I":
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conditions.append(DFIND > INT)
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elif OPR == "=I":
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conditions.append(DFIND == INT)
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||||
elif OPR == "<I":
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||||
conditions.append(DFIND < INT)
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||||
elif OPR == ">R":
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conditions.append(DFIND > REAL)
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elif OPR == "=R":
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||||
conditions.append(np.isclose(DFIND, REAL))
|
||||
elif OPR == "<R":
|
||||
conditions.append(DFIND < REAL)
|
||||
|
||||
print(conditions)
|
||||
dataframe.loc[
|
||||
reduce(lambda x, y: x & y, conditions),
|
||||
'buy'] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
conditions = list()
|
||||
for i in range(int(len(self.sell_params)/5)):
|
||||
OPR = self.sell_params[f'sell-oper-{i}']
|
||||
IND = self.sell_params[f'sell-indicator-{i}']
|
||||
CRS = self.sell_params[f'sell-cross-{i}']
|
||||
INT = self.sell_params[f'sell-int-{i}']
|
||||
REAL = self.sell_params[f'sell-real-{i}']
|
||||
DFIND = dataframe[IND]
|
||||
DFCRS = dataframe[CRS]
|
||||
|
||||
if OPR == ">":
|
||||
conditions.append(DFIND > DFCRS)
|
||||
elif OPR == "=":
|
||||
conditions.append(np.isclose(DFIND, DFCRS))
|
||||
elif OPR == "<":
|
||||
conditions.append(DFIND < DFCRS)
|
||||
elif OPR == "CA":
|
||||
conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
|
||||
elif OPR == "CB":
|
||||
conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
|
||||
elif OPR == ">I":
|
||||
conditions.append(DFIND > INT)
|
||||
elif OPR == "=I":
|
||||
conditions.append(DFIND == INT)
|
||||
elif OPR == "<I":
|
||||
conditions.append(DFIND < INT)
|
||||
elif OPR == ">R":
|
||||
conditions.append(DFIND > REAL)
|
||||
elif OPR == "=R":
|
||||
conditions.append(np.isclose(DFIND, REAL))
|
||||
elif OPR == "<R":
|
||||
conditions.append(DFIND < REAL)
|
||||
|
||||
dataframe.loc[
|
||||
reduce(lambda x, y: x & y, conditions),
|
||||
'sell'] = 1
|
||||
|
||||
return dataframe
|
||||
@@ -0,0 +1,133 @@
|
||||
# Heracles Strategy: Strongest Son of GodStra
|
||||
# ( With just 1 Genome! its a bacteria :D )
|
||||
# Author: @Mablue (Masoud Azizi)
|
||||
# github: https://github.com/mablue/
|
||||
# IMPORTANT:Add to your pairlists inside config.json (Under StaticPairList):
|
||||
# {
|
||||
# "method": "AgeFilter",
|
||||
# "min_days_listed": 100
|
||||
# },
|
||||
# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
|
||||
# ######################################################################
|
||||
# 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.interface import IStrategy
|
||||
from pandas import DataFrame
|
||||
# --------------------------------
|
||||
|
||||
# Add your lib to import here
|
||||
# import talib.abstract as ta
|
||||
import pandas as pd
|
||||
import ta
|
||||
from ta.utils import dropna
|
||||
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
||||
from functools import reduce
|
||||
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
|
||||
|
||||
# Buy hyperspace params:
|
||||
buy_params = {
|
||||
'buy-cross-0': 'volatility_kcw',
|
||||
'buy-indicator-0': 'volatility_dcp',
|
||||
'buy-oper-0': '<',
|
||||
}
|
||||
|
||||
# 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
|
||||
}
|
||||
|
||||
# Stoploss:
|
||||
stoploss = -0.04655
|
||||
|
||||
# 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'
|
||||
|
||||
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()
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
IND = self.buy_params['buy-indicator-0']
|
||||
CRS = self.buy_params['buy-cross-0']
|
||||
DFIND = dataframe[IND]
|
||||
DFCRS = dataframe[CRS]
|
||||
|
||||
dataframe.loc[
|
||||
(DFIND < DFCRS),
|
||||
'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
|
||||
|
||||
return dataframe
|
||||
Reference in New Issue
Block a user