Do not use 'trials' in commands
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hroff-1902
parent
c26835048c
commit
c6787d7e9f
@ -38,33 +38,33 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
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'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None)
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}
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trials_file = (config['user_data_dir'] /
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'hyperopt_results' / 'hyperopt_results.pickle')
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results_file = (config['user_data_dir'] /
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'hyperopt_results' / 'hyperopt_results.pickle')
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# Previous evaluations
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trials = Hyperopt.load_previous_results(trials_file)
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total_epochs = len(trials)
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epochs = Hyperopt.load_previous_results(results_file)
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total_epochs = len(epochs)
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trials = _hyperopt_filter_trials(trials, filteroptions)
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epochs = _hyperopt_filter_epochs(epochs, filteroptions)
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if print_colorized:
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colorama_init(autoreset=True)
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if not export_csv:
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try:
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print(Hyperopt.get_result_table(config, trials, total_epochs,
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print(Hyperopt.get_result_table(config, epochs, total_epochs,
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not filteroptions['only_best'], print_colorized, 0))
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except KeyboardInterrupt:
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print('User interrupted..')
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if trials and not no_details:
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sorted_trials = sorted(trials, key=itemgetter('loss'))
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results = sorted_trials[0]
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if epochs and not no_details:
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sorted_epochs = sorted(epochs, key=itemgetter('loss'))
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results = sorted_epochs[0]
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Hyperopt.print_epoch_details(results, total_epochs, print_json, no_header)
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if trials and export_csv:
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if epochs and export_csv:
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Hyperopt.export_csv_file(
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config, trials, total_epochs, not filteroptions['only_best'], export_csv
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config, epochs, total_epochs, not filteroptions['only_best'], export_csv
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)
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@ -78,8 +78,8 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
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print_json = config.get('print_json', False)
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no_header = config.get('hyperopt_show_no_header', False)
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trials_file = (config['user_data_dir'] /
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'hyperopt_results' / 'hyperopt_results.pickle')
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results_file = (config['user_data_dir'] /
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'hyperopt_results' / 'hyperopt_results.pickle')
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n = config.get('hyperopt_show_index', -1)
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filteroptions = {
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@ -96,89 +96,87 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
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}
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# Previous evaluations
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trials = Hyperopt.load_previous_results(trials_file)
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total_epochs = len(trials)
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epochs = Hyperopt.load_previous_results(results_file)
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total_epochs = len(epochs)
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trials = _hyperopt_filter_trials(trials, filteroptions)
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trials_epochs = len(trials)
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epochs = _hyperopt_filter_epochs(epochs, filteroptions)
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filtered_epochs = len(epochs)
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if n > trials_epochs:
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if n > filtered_epochs:
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raise OperationalException(
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f"The index of the epoch to show should be less than {trials_epochs + 1}.")
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if n < -trials_epochs:
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f"The index of the epoch to show should be less than {filtered_epochs + 1}.")
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if n < -filtered_epochs:
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raise OperationalException(
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f"The index of the epoch to show should be greater than {-trials_epochs - 1}.")
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f"The index of the epoch to show should be greater than {-filtered_epochs - 1}.")
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# Translate epoch index from human-readable format to pythonic
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if n > 0:
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n -= 1
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if trials:
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val = trials[n]
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if epochs:
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val = epochs[n]
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Hyperopt.print_epoch_details(val, total_epochs, print_json, no_header,
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header_str="Epoch details")
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def _hyperopt_filter_trials(trials: List, filteroptions: dict) -> List:
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def _hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
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"""
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Filter our items from the list of hyperopt results
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"""
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if filteroptions['only_best']:
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trials = [x for x in trials if x['is_best']]
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epochs = [x for x in epochs if x['is_best']]
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if filteroptions['only_profitable']:
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trials = [x for x in trials if x['results_metrics']['profit'] > 0]
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epochs = [x for x in epochs if x['results_metrics']['profit'] > 0]
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if filteroptions['filter_min_trades'] > 0:
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trials = [
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x for x in trials
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epochs = [
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x for x in epochs
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if x['results_metrics']['trade_count'] > filteroptions['filter_min_trades']
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]
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if filteroptions['filter_max_trades'] > 0:
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trials = [
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x for x in trials
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epochs = [
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x for x in epochs
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if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades']
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]
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if filteroptions['filter_min_avg_time'] is not None:
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trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
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trials = [
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x for x in trials
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epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
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epochs = [
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x for x in epochs
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if x['results_metrics']['duration'] > filteroptions['filter_min_avg_time']
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]
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if filteroptions['filter_max_avg_time'] is not None:
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trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
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trials = [
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x for x in trials
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epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
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epochs = [
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x for x in epochs
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if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time']
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]
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if filteroptions['filter_min_avg_profit'] is not None:
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trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
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trials = [
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x for x in trials
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if x['results_metrics']['avg_profit']
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> filteroptions['filter_min_avg_profit']
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epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
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epochs = [
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x for x in epochs
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if x['results_metrics']['avg_profit'] > filteroptions['filter_min_avg_profit']
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]
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if filteroptions['filter_max_avg_profit'] is not None:
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trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
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trials = [
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x for x in trials
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if x['results_metrics']['avg_profit']
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< filteroptions['filter_max_avg_profit']
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epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
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epochs = [
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x for x in epochs
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if x['results_metrics']['avg_profit'] < filteroptions['filter_max_avg_profit']
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]
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if filteroptions['filter_min_total_profit'] is not None:
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trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
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trials = [
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x for x in trials
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epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
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epochs = [
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x for x in epochs
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if x['results_metrics']['profit'] > filteroptions['filter_min_total_profit']
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]
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if filteroptions['filter_max_total_profit'] is not None:
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trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
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trials = [
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x for x in trials
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epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
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epochs = [
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x for x in epochs
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if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit']
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]
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logger.info(f"{len(trials)} " +
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logger.info(f"{len(epochs)} " +
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("best " if filteroptions['only_best'] else "") +
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("profitable " if filteroptions['only_profitable'] else "") +
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"epochs found.")
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return trials
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return epochs
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