From c400d15ed1e5c8468f3ff94c6a0929819b507a22 Mon Sep 17 00:00:00 2001 From: Janne Sinivirta Date: Tue, 23 Jan 2018 16:56:12 +0200 Subject: [PATCH] rip out hyperopt things from strategy, add indicator populating to hyperopt --- freqtrade/optimize/hyperopt.py | 285 +++++++++++++++++- freqtrade/strategy/default_strategy.py | 137 --------- freqtrade/strategy/interface.py | 13 - freqtrade/strategy/strategy.py | 12 - .../tests/strategy/test_default_strategy.py | 4 - freqtrade/tests/strategy/test_strategy.py | 8 - 6 files changed, 272 insertions(+), 187 deletions(-) diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py index 3e174d892..62dcdf68f 100644 --- a/freqtrade/optimize/hyperopt.py +++ b/freqtrade/optimize/hyperopt.py @@ -3,21 +3,28 @@ import json import logging -import sys +import os import pickle import signal -import os +import sys +from functools import reduce from math import exp from operator import itemgetter +from typing import Dict, List -from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, space_eval, tpe +import numpy +import talib.abstract as ta +from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe from hyperopt.mongoexp import MongoTrials from pandas import DataFrame -from freqtrade import main, misc # noqa -from freqtrade import exchange, optimize +import freqtrade.vendor.qtpylib.indicators as qtpylib +# Monkey patch config +from freqtrade import main # noqa; noqa +from freqtrade import exchange, misc, optimize from freqtrade.exchange import Bittrex from freqtrade.misc import load_config +from freqtrade.optimize import backtesting from freqtrade.optimize.backtesting import backtest from freqtrade.strategy.strategy import Strategy from user_data.hyperopt_conf import hyperopt_optimize_conf @@ -51,11 +58,129 @@ OPTIMIZE_CONFIG = hyperopt_optimize_conf() TRIALS_FILE = os.path.join('user_data', 'hyperopt_trials.pickle') TRIALS = Trials() -# Monkey patch config -from freqtrade import main # noqa main._CONF = OPTIMIZE_CONFIG +def populate_indicators(dataframe: DataFrame) -> DataFrame: + """ + Adds several different TA indicators to the given DataFrame + """ + dataframe['adx'] = ta.ADX(dataframe) + dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) + dataframe['cci'] = ta.CCI(dataframe) + macd = ta.MACD(dataframe) + dataframe['macd'] = macd['macd'] + dataframe['macdsignal'] = macd['macdsignal'] + dataframe['macdhist'] = macd['macdhist'] + dataframe['mfi'] = ta.MFI(dataframe) + dataframe['minus_dm'] = ta.MINUS_DM(dataframe) + dataframe['minus_di'] = ta.MINUS_DI(dataframe) + dataframe['plus_dm'] = ta.PLUS_DM(dataframe) + dataframe['plus_di'] = ta.PLUS_DI(dataframe) + dataframe['roc'] = ta.ROC(dataframe) + dataframe['rsi'] = ta.RSI(dataframe) + # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy) + rsi = 0.1 * (dataframe['rsi'] - 50) + dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) + # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy) + dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) + # Stoch + stoch = ta.STOCH(dataframe) + dataframe['slowd'] = stoch['slowd'] + dataframe['slowk'] = stoch['slowk'] + # Stoch fast + stoch_fast = ta.STOCHF(dataframe) + dataframe['fastd'] = stoch_fast['fastd'] + dataframe['fastk'] = stoch_fast['fastk'] + # Stoch RSI + stoch_rsi = ta.STOCHRSI(dataframe) + dataframe['fastd_rsi'] = stoch_rsi['fastd'] + dataframe['fastk_rsi'] = stoch_rsi['fastk'] + # Bollinger bands + bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) + dataframe['bb_lowerband'] = bollinger['lower'] + dataframe['bb_middleband'] = bollinger['mid'] + dataframe['bb_upperband'] = bollinger['upper'] + # EMA - Exponential Moving Average + dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) + dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) + dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) + dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) + dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) + # SAR Parabolic + dataframe['sar'] = ta.SAR(dataframe) + # SMA - Simple Moving Average + dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) + # TEMA - Triple Exponential Moving Average + dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) + # Hilbert Transform Indicator - SineWave + hilbert = ta.HT_SINE(dataframe) + dataframe['htsine'] = hilbert['sine'] + dataframe['htleadsine'] = hilbert['leadsine'] + + # Pattern Recognition - Bullish candlestick patterns + # ------------------------------------ + """ + # Hammer: values [0, 100] + dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) + # Inverted Hammer: values [0, 100] + dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) + # Dragonfly Doji: values [0, 100] + dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) + # Piercing Line: values [0, 100] + dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] + # Morningstar: values [0, 100] + dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] + # Three White Soldiers: values [0, 100] + dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] + """ + + # Pattern Recognition - Bearish candlestick patterns + # ------------------------------------ + """ + # Hanging Man: values [0, 100] + dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) + # Shooting Star: values [0, 100] + dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) + # Gravestone Doji: values [0, 100] + dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) + # Dark Cloud Cover: values [0, 100] + dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) + # Evening Doji Star: values [0, 100] + dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) + # Evening Star: values [0, 100] + dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) + """ + + # Pattern Recognition - Bullish/Bearish candlestick patterns + # ------------------------------------ + """ + # Three Line Strike: values [0, -100, 100] + dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) + # Spinning Top: values [0, -100, 100] + dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] + # Engulfing: values [0, -100, 100] + dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] + # Harami: values [0, -100, 100] + dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] + # Three Outside Up/Down: values [0, -100, 100] + dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] + # Three Inside Up/Down: values [0, -100, 100] + dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] + """ + + # Chart type + # ------------------------------------ + # Heikinashi stategy + heikinashi = qtpylib.heikinashi(dataframe) + dataframe['ha_open'] = heikinashi['open'] + dataframe['ha_close'] = heikinashi['close'] + dataframe['ha_high'] = heikinashi['high'] + dataframe['ha_low'] = heikinashi['low'] + + return dataframe + + def save_trials(trials, trials_path=TRIALS_FILE): """Save hyperopt trials to file""" logger.info('Saving Trials to \'{}\''.format(trials_path)) @@ -100,13 +225,146 @@ def calculate_loss(total_profit: float, trade_count: int, trade_duration: float) return trade_loss + profit_loss + duration_loss +def hyperopt_space() -> List[Dict]: + """ + Define your Hyperopt space for searching strategy parameters + """ + space = { + 'macd_below_zero': hp.choice('macd_below_zero', [ + {'enabled': False}, + {'enabled': True} + ]), + 'mfi': hp.choice('mfi', [ + {'enabled': False}, + {'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)} + ]), + 'fastd': hp.choice('fastd', [ + {'enabled': False}, + {'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)} + ]), + 'adx': hp.choice('adx', [ + {'enabled': False}, + {'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)} + ]), + 'rsi': hp.choice('rsi', [ + {'enabled': False}, + {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)} + ]), + 'uptrend_long_ema': hp.choice('uptrend_long_ema', [ + {'enabled': False}, + {'enabled': True} + ]), + 'uptrend_short_ema': hp.choice('uptrend_short_ema', [ + {'enabled': False}, + {'enabled': True} + ]), + 'over_sar': hp.choice('over_sar', [ + {'enabled': False}, + {'enabled': True} + ]), + 'green_candle': hp.choice('green_candle', [ + {'enabled': False}, + {'enabled': True} + ]), + 'uptrend_sma': hp.choice('uptrend_sma', [ + {'enabled': False}, + {'enabled': True} + ]), + 'trigger': hp.choice('trigger', [ + {'type': 'lower_bb'}, + {'type': 'lower_bb_tema'}, + {'type': 'faststoch10'}, + {'type': 'ao_cross_zero'}, + {'type': 'ema3_cross_ema10'}, + {'type': 'macd_cross_signal'}, + {'type': 'sar_reversal'}, + {'type': 'ht_sine'}, + {'type': 'heiken_reversal_bull'}, + {'type': 'di_cross'}, + ]), + 'stoploss': hp.uniform('stoploss', -0.5, -0.02), + } + return space + + +def buy_strategy_generator(params) -> None: + """ + Define the buy strategy parameters to be used by hyperopt + """ + def populate_buy_trend(dataframe: DataFrame) -> DataFrame: + conditions = [] + # GUARDS AND TRENDS + if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']: + conditions.append(dataframe['ema50'] > dataframe['ema100']) + if 'macd_below_zero' in params and params['macd_below_zero']['enabled']: + conditions.append(dataframe['macd'] < 0) + if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']: + conditions.append(dataframe['ema5'] > dataframe['ema10']) + if 'mfi' in params and params['mfi']['enabled']: + conditions.append(dataframe['mfi'] < params['mfi']['value']) + if 'fastd' in params and params['fastd']['enabled']: + conditions.append(dataframe['fastd'] < params['fastd']['value']) + if 'adx' in params and params['adx']['enabled']: + conditions.append(dataframe['adx'] > params['adx']['value']) + if 'rsi' in params and params['rsi']['enabled']: + conditions.append(dataframe['rsi'] < params['rsi']['value']) + if 'over_sar' in params and params['over_sar']['enabled']: + conditions.append(dataframe['close'] > dataframe['sar']) + if 'green_candle' in params and params['green_candle']['enabled']: + conditions.append(dataframe['close'] > dataframe['open']) + if 'uptrend_sma' in params and params['uptrend_sma']['enabled']: + prevsma = dataframe['sma'].shift(1) + conditions.append(dataframe['sma'] > prevsma) + + # TRIGGERS + triggers = { + 'lower_bb': ( + dataframe['close'] < dataframe['bb_lowerband'] + ), + 'lower_bb_tema': ( + dataframe['tema'] < dataframe['bb_lowerband'] + ), + 'faststoch10': (qtpylib.crossed_above( + dataframe['fastd'], 10.0 + )), + 'ao_cross_zero': (qtpylib.crossed_above( + dataframe['ao'], 0.0 + )), + 'ema3_cross_ema10': (qtpylib.crossed_above( + dataframe['ema3'], dataframe['ema10'] + )), + 'macd_cross_signal': (qtpylib.crossed_above( + dataframe['macd'], dataframe['macdsignal'] + )), + 'sar_reversal': (qtpylib.crossed_above( + dataframe['close'], dataframe['sar'] + )), + 'ht_sine': (qtpylib.crossed_above( + dataframe['htleadsine'], dataframe['htsine'] + )), + 'heiken_reversal_bull': ( + (qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) & + (dataframe['ha_low'] == dataframe['ha_open']) + ), + 'di_cross': (qtpylib.crossed_above( + dataframe['plus_di'], dataframe['minus_di'] + )), + } + conditions.append(triggers.get(params['trigger']['type'])) + + dataframe.loc[ + reduce(lambda x, y: x & y, conditions), + 'buy'] = 1 + + return dataframe + + return populate_buy_trend + + def optimizer(params): global _CURRENT_TRIES - from freqtrade.optimize import backtesting - - strategy = Strategy() - backtesting.populate_buy_trend = strategy.buy_strategy_generator(params) + backtesting.populate_buy_trend = buy_strategy_generator(params) results = backtest({'stake_amount': OPTIMIZE_CONFIG['stake_amount'], 'processed': PROCESSED, @@ -179,6 +437,7 @@ def start(args): data = optimize.load_data(args.datadir, pairs=pairs, ticker_interval=args.ticker_interval, timerange=timerange) + optimize.populate_indicators = populate_indicators PROCESSED = optimize.tickerdata_to_dataframe(data) if args.mongodb: @@ -203,7 +462,7 @@ def start(args): try: best_parameters = fmin( fn=optimizer, - space=strategy.hyperopt_space(), + space=hyperopt_space(), algo=tpe.suggest, max_evals=TOTAL_TRIES, trials=TRIALS @@ -220,7 +479,7 @@ def start(args): # Improve best parameter logging display if best_parameters: best_parameters = space_eval( - strategy.hyperopt_space(), + hyperopt_space(), best_parameters ) diff --git a/freqtrade/strategy/default_strategy.py b/freqtrade/strategy/default_strategy.py index 95423fe70..c89b20527 100644 --- a/freqtrade/strategy/default_strategy.py +++ b/freqtrade/strategy/default_strategy.py @@ -2,9 +2,6 @@ import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.strategy.interface import IStrategy from pandas import DataFrame -from hyperopt import hp -from functools import reduce -from typing import Dict, List class_name = 'DefaultStrategy' @@ -239,137 +236,3 @@ class DefaultStrategy(IStrategy): ), 'sell'] = 1 return dataframe - - def hyperopt_space(self) -> List[Dict]: - """ - Define your Hyperopt space for the strategy - """ - space = { - 'macd_below_zero': hp.choice('macd_below_zero', [ - {'enabled': False}, - {'enabled': True} - ]), - 'mfi': hp.choice('mfi', [ - {'enabled': False}, - {'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)} - ]), - 'fastd': hp.choice('fastd', [ - {'enabled': False}, - {'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)} - ]), - 'adx': hp.choice('adx', [ - {'enabled': False}, - {'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)} - ]), - 'rsi': hp.choice('rsi', [ - {'enabled': False}, - {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)} - ]), - 'uptrend_long_ema': hp.choice('uptrend_long_ema', [ - {'enabled': False}, - {'enabled': True} - ]), - 'uptrend_short_ema': hp.choice('uptrend_short_ema', [ - {'enabled': False}, - {'enabled': True} - ]), - 'over_sar': hp.choice('over_sar', [ - {'enabled': False}, - {'enabled': True} - ]), - 'green_candle': hp.choice('green_candle', [ - {'enabled': False}, - {'enabled': True} - ]), - 'uptrend_sma': hp.choice('uptrend_sma', [ - {'enabled': False}, - {'enabled': True} - ]), - 'trigger': hp.choice('trigger', [ - {'type': 'lower_bb'}, - {'type': 'lower_bb_tema'}, - {'type': 'faststoch10'}, - {'type': 'ao_cross_zero'}, - {'type': 'ema3_cross_ema10'}, - {'type': 'macd_cross_signal'}, - {'type': 'sar_reversal'}, - {'type': 'ht_sine'}, - {'type': 'heiken_reversal_bull'}, - {'type': 'di_cross'}, - ]), - 'stoploss': hp.uniform('stoploss', -0.5, -0.02), - } - return space - - def buy_strategy_generator(self, params) -> None: - """ - Define the buy strategy parameters to be used by hyperopt - """ - def populate_buy_trend(dataframe: DataFrame) -> DataFrame: - conditions = [] - # GUARDS AND TRENDS - if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']: - conditions.append(dataframe['ema50'] > dataframe['ema100']) - if 'macd_below_zero' in params and params['macd_below_zero']['enabled']: - conditions.append(dataframe['macd'] < 0) - if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']: - conditions.append(dataframe['ema5'] > dataframe['ema10']) - if 'mfi' in params and params['mfi']['enabled']: - conditions.append(dataframe['mfi'] < params['mfi']['value']) - if 'fastd' in params and params['fastd']['enabled']: - conditions.append(dataframe['fastd'] < params['fastd']['value']) - if 'adx' in params and params['adx']['enabled']: - conditions.append(dataframe['adx'] > params['adx']['value']) - if 'rsi' in params and params['rsi']['enabled']: - conditions.append(dataframe['rsi'] < params['rsi']['value']) - if 'over_sar' in params and params['over_sar']['enabled']: - conditions.append(dataframe['close'] > dataframe['sar']) - if 'green_candle' in params and params['green_candle']['enabled']: - conditions.append(dataframe['close'] > dataframe['open']) - if 'uptrend_sma' in params and params['uptrend_sma']['enabled']: - prevsma = dataframe['sma'].shift(1) - conditions.append(dataframe['sma'] > prevsma) - - # TRIGGERS - triggers = { - 'lower_bb': ( - dataframe['close'] < dataframe['bb_lowerband'] - ), - 'lower_bb_tema': ( - dataframe['tema'] < dataframe['bb_lowerband'] - ), - 'faststoch10': (qtpylib.crossed_above( - dataframe['fastd'], 10.0 - )), - 'ao_cross_zero': (qtpylib.crossed_above( - dataframe['ao'], 0.0 - )), - 'ema3_cross_ema10': (qtpylib.crossed_above( - dataframe['ema3'], dataframe['ema10'] - )), - 'macd_cross_signal': (qtpylib.crossed_above( - dataframe['macd'], dataframe['macdsignal'] - )), - 'sar_reversal': (qtpylib.crossed_above( - dataframe['close'], dataframe['sar'] - )), - 'ht_sine': (qtpylib.crossed_above( - dataframe['htleadsine'], dataframe['htsine'] - )), - 'heiken_reversal_bull': ( - (qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) & - (dataframe['ha_low'] == dataframe['ha_open']) - ), - 'di_cross': (qtpylib.crossed_above( - dataframe['plus_di'], dataframe['minus_di'] - )), - } - conditions.append(triggers.get(params['trigger']['type'])) - - dataframe.loc[ - reduce(lambda x, y: x & y, conditions), - 'buy'] = 1 - - return dataframe - - return populate_buy_trend diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index 70ea43a15..ce5f08cd2 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -1,6 +1,5 @@ from abc import ABC, abstractmethod from pandas import DataFrame -from typing import Dict class IStrategy(ABC): @@ -43,15 +42,3 @@ class IStrategy(ABC): :param dataframe: DataFrame :return: DataFrame with buy column """ - - @abstractmethod - def hyperopt_space(self) -> Dict: - """ - Define your Hyperopt space for the strategy - """ - - @abstractmethod - def buy_strategy_generator(self, params) -> None: - """ - Define the buy strategy parameters to be used by hyperopt - """ diff --git a/freqtrade/strategy/strategy.py b/freqtrade/strategy/strategy.py index 859a52797..2545e378c 100644 --- a/freqtrade/strategy/strategy.py +++ b/freqtrade/strategy/strategy.py @@ -164,15 +164,3 @@ class Strategy(object): :return: DataFrame with buy column """ return self.custom_strategy.populate_sell_trend(dataframe) - - def hyperopt_space(self) -> Dict: - """ - Define your Hyperopt space for the strategy - """ - return self.custom_strategy.hyperopt_space() - - def buy_strategy_generator(self, params) -> None: - """ - Define the buy strategy parameters to be used by hyperopt - """ - return self.custom_strategy.buy_strategy_generator(params) diff --git a/freqtrade/tests/strategy/test_default_strategy.py b/freqtrade/tests/strategy/test_default_strategy.py index 669e1ad84..f23c1fa48 100644 --- a/freqtrade/tests/strategy/test_default_strategy.py +++ b/freqtrade/tests/strategy/test_default_strategy.py @@ -22,8 +22,6 @@ def test_default_strategy_structure(): assert hasattr(DefaultStrategy, 'populate_indicators') assert hasattr(DefaultStrategy, 'populate_buy_trend') assert hasattr(DefaultStrategy, 'populate_sell_trend') - assert hasattr(DefaultStrategy, 'hyperopt_space') - assert hasattr(DefaultStrategy, 'buy_strategy_generator') def test_default_strategy(result): @@ -36,5 +34,3 @@ def test_default_strategy(result): assert type(indicators) is DataFrame assert type(strategy.populate_buy_trend(indicators)) is DataFrame assert type(strategy.populate_sell_trend(indicators)) is DataFrame - assert type(strategy.hyperopt_space()) is dict - assert callable(strategy.buy_strategy_generator({})) diff --git a/freqtrade/tests/strategy/test_strategy.py b/freqtrade/tests/strategy/test_strategy.py index 2655800d5..79f045a6d 100644 --- a/freqtrade/tests/strategy/test_strategy.py +++ b/freqtrade/tests/strategy/test_strategy.py @@ -33,8 +33,6 @@ def test_strategy_structure(): assert hasattr(Strategy, 'populate_indicators') assert hasattr(Strategy, 'populate_buy_trend') assert hasattr(Strategy, 'populate_sell_trend') - assert hasattr(Strategy, 'hyperopt_space') - assert hasattr(Strategy, 'buy_strategy_generator') def test_load_strategy(result): @@ -71,12 +69,6 @@ def test_strategy(result): dataframe = strategy.populate_sell_trend(strategy.populate_indicators(result)) assert 'sell' in dataframe.columns - assert hasattr(strategy.custom_strategy, 'hyperopt_space') - assert 'adx' in strategy.hyperopt_space() - - assert hasattr(strategy.custom_strategy, 'buy_strategy_generator') - assert callable(strategy.buy_strategy_generator({})) - def test_strategy_override_minimal_roi(caplog): config = {