def __init__(self, config: Dict[str, Any]) -> None: self.config = config self.backtesting = Backtesting(self.config) self.custom_hyperopt = HyperOptResolver(self.config).hyperopt self.custom_hyperoptloss = HyperOptLossResolver( self.config).hyperoptloss self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function self.trials_file = (self.config['user_data_dir'] / 'hyperopt_results' / 'hyperopt_results.pickle') self.tickerdata_pickle = (self.config['user_data_dir'] / 'hyperopt_results' / 'hyperopt_tickerdata.pkl') self.total_epochs = config.get('epochs', 0) self.current_best_loss = 100 if not self.config.get('hyperopt_continue'): self.clean_hyperopt() else: logger.info("Continuing on previous hyperopt results.") self.num_trials_saved = 0 # Previous evaluations self.trials: List = [] # Populate functions here (hasattr is slow so should not be run during "regular" operations) if hasattr(self.custom_hyperopt, 'populate_indicators'): self.backtesting.strategy.advise_indicators = \ self.custom_hyperopt.populate_indicators # type: ignore if hasattr(self.custom_hyperopt, 'populate_buy_trend'): self.backtesting.strategy.advise_buy = \ self.custom_hyperopt.populate_buy_trend # type: ignore if hasattr(self.custom_hyperopt, 'populate_sell_trend'): self.backtesting.strategy.advise_sell = \ self.custom_hyperopt.populate_sell_trend # type: ignore # Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set if self.config.get('use_max_market_positions', True): self.max_open_trades = self.config['max_open_trades'] else: logger.debug( 'Ignoring max_open_trades (--disable-max-market-positions was used) ...' ) self.max_open_trades = 0 self.position_stacking = self.config.get('position_stacking', False) if self.has_space('sell'): # Make sure use_sell_signal is enabled if 'ask_strategy' not in self.config: self.config['ask_strategy'] = {} self.config['ask_strategy']['use_sell_signal'] = True self.print_all = self.config.get('print_all', False) self.print_colorized = self.config.get('print_colorized', False) self.print_json = self.config.get('print_json', False)
def test_loss_calculation_prefer_correct_trade_count(default_conf, hyperopt_results) -> None: hl = HyperOptLossResolver(default_conf).hyperoptloss correct = hl.hyperopt_loss_function(hyperopt_results, 600) over = hl.hyperopt_loss_function(hyperopt_results, 600 + 100) under = hl.hyperopt_loss_function(hyperopt_results, 600 - 100) assert over > correct assert under > correct
def test_hyperoptlossresolver(mocker, default_conf, caplog) -> None: hl = DefaultHyperOptLoss mocker.patch( 'freqtrade.resolvers.hyperopt_resolver.HyperOptLossResolver._load_hyperoptloss', MagicMock(return_value=hl)) x = HyperOptLossResolver(default_conf, ).hyperoptloss assert hasattr(x, "hyperopt_loss_function")
def test_loss_calculation_prefer_shorter_trades(default_conf, hyperopt_results) -> None: resultsb = hyperopt_results.copy() resultsb.loc[1, 'trade_duration'] = 20 hl = HyperOptLossResolver(default_conf).hyperoptloss longer = hl.hyperopt_loss_function(hyperopt_results, 100) shorter = hl.hyperopt_loss_function(resultsb, 100) assert shorter < longer
def test_loss_calculation_has_limited_profit(default_conf, hyperopt_results) -> None: results_over = hyperopt_results.copy() results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2 results_under = hyperopt_results.copy() results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2 hl = HyperOptLossResolver(default_conf).hyperoptloss correct = hl.hyperopt_loss_function(hyperopt_results, 600) over = hl.hyperopt_loss_function(results_over, 600) under = hl.hyperopt_loss_function(results_under, 600) assert over < correct assert under > correct
def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None: results_over = hyperopt_results.copy() results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2 results_under = hyperopt_results.copy() results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2 default_conf.update({'hyperopt_loss': 'OnlyProfitHyperOptLoss'}) hl = HyperOptLossResolver(default_conf).hyperoptloss correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results), datetime(2019, 1, 1), datetime(2019, 5, 1)) over = hl.hyperopt_loss_function(results_over, len(hyperopt_results), datetime(2019, 1, 1), datetime(2019, 5, 1)) under = hl.hyperopt_loss_function(results_under, len(hyperopt_results), datetime(2019, 1, 1), datetime(2019, 5, 1)) assert over < correct assert under > correct
def __init__(self, config: Dict[str, Any]) -> None: super().__init__(config) self.custom_hyperopt = HyperOptResolver(self.config).hyperopt self.custom_hyperoptloss = HyperOptLossResolver( self.config).hyperoptloss self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function self.total_tries = config.get('epochs', 0) self.current_best_loss = 100 if not self.config.get('hyperopt_continue'): self.clean_hyperopt() else: logger.info("Continuing on previous hyperopt results.") # Previous evaluations self.trials_file = TRIALSDATA_PICKLE self.trials: List = [] # Populate functions here (hasattr is slow so should not be run during "regular" operations) if hasattr(self.custom_hyperopt, 'populate_buy_trend'): self.advise_buy = self.custom_hyperopt.populate_buy_trend # type: ignore if hasattr(self.custom_hyperopt, 'populate_sell_trend'): self.advise_sell = self.custom_hyperopt.populate_sell_trend # type: ignore # Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set if self.config.get('use_max_market_positions', True): self.max_open_trades = self.config['max_open_trades'] else: logger.debug( 'Ignoring max_open_trades (--disable-max-market-positions was used) ...' ) self.max_open_trades = 0 self.position_stacking = self.config.get('position_stacking', False),
def test_hyperoptlossresolver_wrongname(mocker, default_conf, caplog) -> None: default_conf.update({'hyperopt_loss': "NonExistingLossClass"}) with pytest.raises(OperationalException, match=r'Impossible to load HyperoptLoss.*'): HyperOptLossResolver(default_conf, ).hyperopt