import logging from operator import itemgetter from typing import Any, Dict, List from colorama import init as colorama_init from freqtrade.configuration import setup_utils_configuration from freqtrade.exceptions import OperationalException from freqtrade.state import RunMode from freqtrade.data.btanalysis import get_latest_hyperopt_file logger = logging.getLogger(__name__) def start_hyperopt_list(args: Dict[str, Any]) -> None: """ List hyperopt epochs previously evaluated """ from freqtrade.optimize.hyperopt import Hyperopt config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE) print_colorized = config.get('print_colorized', False) print_json = config.get('print_json', False) export_csv = config.get('export_csv', None) no_details = config.get('hyperopt_list_no_details', False) no_header = False filteroptions = { 'only_best': config.get('hyperopt_list_best', False), 'only_profitable': config.get('hyperopt_list_profitable', False), 'filter_min_trades': config.get('hyperopt_list_min_trades', 0), 'filter_max_trades': config.get('hyperopt_list_max_trades', 0), 'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None), 'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None), 'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None), 'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None), 'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None), 'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None), 'filter_min_objective': config.get('hyperopt_list_min_objective', None), 'filter_max_objective': config.get('hyperopt_list_max_objective', None), } results_file = get_latest_hyperopt_file(config['user_data_dir'] / 'hyperopt_results') # Previous evaluations epochs = Hyperopt.load_previous_results(results_file) total_epochs = len(epochs) epochs = hyperopt_filter_epochs(epochs, filteroptions) if print_colorized: colorama_init(autoreset=True) if not export_csv: try: print(Hyperopt.get_result_table(config, epochs, total_epochs, not filteroptions['only_best'], print_colorized, 0)) except KeyboardInterrupt: print('User interrupted..') if epochs and not no_details: sorted_epochs = sorted(epochs, key=itemgetter('loss')) results = sorted_epochs[0] Hyperopt.print_epoch_details(results, total_epochs, print_json, no_header) if epochs and export_csv: Hyperopt.export_csv_file( config, epochs, total_epochs, not filteroptions['only_best'], export_csv ) def start_hyperopt_show(args: Dict[str, Any]) -> None: """ Show details of a hyperopt epoch previously evaluated """ from freqtrade.optimize.hyperopt import Hyperopt config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE) print_json = config.get('print_json', False) no_header = config.get('hyperopt_show_no_header', False) results_file = get_latest_hyperopt_file(config['user_data_dir'] / 'hyperopt_results') n = config.get('hyperopt_show_index', -1) filteroptions = { 'only_best': config.get('hyperopt_list_best', False), 'only_profitable': config.get('hyperopt_list_profitable', False), 'filter_min_trades': config.get('hyperopt_list_min_trades', 0), 'filter_max_trades': config.get('hyperopt_list_max_trades', 0), 'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None), 'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None), 'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None), 'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None), 'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None), 'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None), 'filter_min_objective': config.get('hyperopt_list_min_objective', None), 'filter_max_objective': config.get('hyperopt_list_max_objective', None) } # Previous evaluations epochs = Hyperopt.load_previous_results(results_file) total_epochs = len(epochs) epochs = hyperopt_filter_epochs(epochs, filteroptions) filtered_epochs = len(epochs) if n > filtered_epochs: raise OperationalException( f"The index of the epoch to show should be less than {filtered_epochs + 1}.") if n < -filtered_epochs: raise OperationalException( f"The index of the epoch to show should be greater than {-filtered_epochs - 1}.") # Translate epoch index from human-readable format to pythonic if n > 0: n -= 1 if epochs: val = epochs[n] Hyperopt.print_epoch_details(val, total_epochs, print_json, no_header, header_str="Epoch details") def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List: """ Filter our items from the list of hyperopt results """ if filteroptions['only_best']: epochs = [x for x in epochs if x['is_best']] if filteroptions['only_profitable']: epochs = [x for x in epochs if x['results_metrics']['profit'] > 0] epochs = _hyperopt_filter_epochs_trade_count(epochs, filteroptions) epochs = _hyperopt_filter_epochs_duration(epochs, filteroptions) epochs = _hyperopt_filter_epochs_profit(epochs, filteroptions) epochs = _hyperopt_filter_epochs_objective(epochs, filteroptions) logger.info(f"{len(epochs)} " + ("best " if filteroptions['only_best'] else "") + ("profitable " if filteroptions['only_profitable'] else "") + "epochs found.") return epochs def _hyperopt_filter_epochs_trade_count(epochs: List, filteroptions: dict) -> List: if filteroptions['filter_min_trades'] > 0: epochs = [ x for x in epochs if x['results_metrics']['trade_count'] > filteroptions['filter_min_trades'] ] if filteroptions['filter_max_trades'] > 0: epochs = [ x for x in epochs if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades'] ] return epochs def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List: if filteroptions['filter_min_avg_time'] is not None: epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0] epochs = [ x for x in epochs if x['results_metrics']['duration'] > filteroptions['filter_min_avg_time'] ] if filteroptions['filter_max_avg_time'] is not None: epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0] epochs = [ x for x in epochs if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time'] ] return epochs def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List: if filteroptions['filter_min_avg_profit'] is not None: epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0] epochs = [ x for x in epochs if x['results_metrics']['avg_profit'] > filteroptions['filter_min_avg_profit'] ] if filteroptions['filter_max_avg_profit'] is not None: epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0] epochs = [ x for x in epochs if x['results_metrics']['avg_profit'] < filteroptions['filter_max_avg_profit'] ] if filteroptions['filter_min_total_profit'] is not None: epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0] epochs = [ x for x in epochs if x['results_metrics']['profit'] > filteroptions['filter_min_total_profit'] ] if filteroptions['filter_max_total_profit'] is not None: epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0] epochs = [ x for x in epochs if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit'] ] return epochs def _hyperopt_filter_epochs_objective(epochs: List, filteroptions: dict) -> List: if filteroptions['filter_min_objective'] is not None: epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0] epochs = [x for x in epochs if x['loss'] < filteroptions['filter_min_objective']] if filteroptions['filter_max_objective'] is not None: epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0] epochs = [x for x in epochs if x['loss'] > filteroptions['filter_max_objective']] return epochs