import io import logging from collections import OrderedDict from pathlib import Path from typing import Any, Dict, List import rapidjson import tabulate from colorama import Fore, Style from pandas import isna, json_normalize from freqtrade.exceptions import OperationalException from freqtrade.misc import round_coin_value, round_dict logger = logging.getLogger(__name__) class HyperoptTools(): @staticmethod def has_space(config: Dict[str, Any], space: str) -> bool: """ Tell if the space value is contained in the configuration """ # The 'trailing' space is not included in the 'default' set of spaces if space == 'trailing': return any(s in config['spaces'] for s in [space, 'all']) else: return any(s in config['spaces'] for s in [space, 'all', 'default']) @staticmethod def _read_results_pickle(results_file: Path) -> List: """ Read hyperopt results from pickle file LEGACY method - new files are written as json and cannot be read with this method. """ from joblib import load logger.info(f"Reading pickled epochs from '{results_file}'") data = load(results_file) return data @staticmethod def _read_results(results_file: Path) -> List: """ Read hyperopt results from file """ import rapidjson logger.info(f"Reading epochs from '{results_file}'") with results_file.open('r') as f: data = [rapidjson.loads(line) for line in f] return data @staticmethod def load_previous_results(results_file: Path) -> List: """ Load data for epochs from the file if we have one """ epochs: List = [] if results_file.is_file() and results_file.stat().st_size > 0: if results_file.suffix == '.pickle': epochs = HyperoptTools._read_results_pickle(results_file) else: epochs = HyperoptTools._read_results(results_file) # Detection of some old format, without 'is_best' field saved if epochs[0].get('is_best') is None: raise OperationalException( "The file with HyperoptTools results is incompatible with this version " "of Freqtrade and cannot be loaded.") logger.info(f"Loaded {len(epochs)} previous evaluations from disk.") return epochs @staticmethod def print_epoch_details(results, total_epochs: int, print_json: bool, no_header: bool = False, header_str: str = None) -> None: """ Display details of the hyperopt result """ params = results.get('params_details', {}) non_optimized = results.get('params_not_optimized', {}) # Default header string if header_str is None: header_str = "Best result" if not no_header: explanation_str = HyperoptTools._format_explanation_string(results, total_epochs) print(f"\n{header_str}:\n\n{explanation_str}\n") if print_json: result_dict: Dict = {} for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']: HyperoptTools._params_update_for_json(result_dict, params, s) print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE)) else: HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:", non_optimized) HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:", non_optimized) HyperoptTools._params_pretty_print(params, 'roi', "ROI table:") HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:") HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:") @staticmethod def _params_update_for_json(result_dict, params, space: str) -> None: if space in params: space_params = HyperoptTools._space_params(params, space) if space in ['buy', 'sell']: result_dict.setdefault('params', {}).update(space_params) elif space == 'roi': # TODO: get rid of OrderedDict when support for python 3.6 will be # dropped (dicts keep the order as the language feature) # Convert keys in min_roi dict to strings because # rapidjson cannot dump dicts with integer keys... # OrderedDict is used to keep the numeric order of the items # in the dict. result_dict['minimal_roi'] = OrderedDict( (str(k), v) for k, v in space_params.items() ) else: # 'stoploss', 'trailing' result_dict.update(space_params) @staticmethod def _params_pretty_print(params, space: str, header: str, non_optimized={}) -> None: if space in params or space in non_optimized: space_params = HyperoptTools._space_params(params, space, 5) result = f"\n# {header}\n" if space == 'stoploss': result += f"stoploss = {space_params.get('stoploss')}" elif space == 'roi': # TODO: get rid of OrderedDict when support for python 3.6 will be # dropped (dicts keep the order as the language feature) minimal_roi_result = rapidjson.dumps( OrderedDict( (str(k), v) for k, v in space_params.items() ), default=str, indent=4, number_mode=rapidjson.NM_NATIVE) result += f"minimal_roi = {minimal_roi_result}" elif space == 'trailing': for k, v in space_params.items(): result += f'{k} = {v}\n' else: no_params = HyperoptTools._space_params(non_optimized, space, 5) result += f"{space}_params = {HyperoptTools._pprint(space_params, no_params)}" result = result.replace("\n", "\n ") print(result) @staticmethod def _space_params(params, space: str, r: int = None) -> Dict: d = params.get(space) if d: # Round floats to `r` digits after the decimal point if requested return round_dict(d, r) if r else d return {} @staticmethod def _pprint(params, non_optimized, indent: int = 4): """ Pretty-print hyperopt results (based on 2 dicts - with add. comment) """ p = params.copy() p.update(non_optimized) result = '{\n' for k, param in p.items(): result += " " * indent + f'"{k}": ' result += f'"{param}",' if isinstance(param, str) else f'{param},' if k in non_optimized: result += " # value loaded from strategy" result += "\n" result += '}' return result @staticmethod def is_best_loss(results, current_best_loss: float) -> bool: return bool(results['loss'] < current_best_loss) @staticmethod def format_results_explanation_string(results_metrics: Dict, stake_currency: str) -> str: """ Return the formatted results explanation in a string """ return (f"{results_metrics['total_trades']:6d} trades. " f"{results_metrics['wins']}/{results_metrics['draws']}" f"/{results_metrics['losses']} Wins/Draws/Losses. " f"Avg profit {results_metrics['profit_mean'] * 100: 6.2f}%. " f"Median profit {results_metrics['profit_median'] * 100: 6.2f}%. " f"Total profit {results_metrics['profit_total_abs']: 11.8f} {stake_currency} " f"({results_metrics['profit_total'] * 100: 7.2f}%). " f"Avg duration {results_metrics['holding_avg']} min." ) @staticmethod def _format_explanation_string(results, total_epochs) -> str: return (("*" if results['is_initial_point'] else " ") + f"{results['current_epoch']:5d}/{total_epochs}: " + f"{results['results_explanation']} " + f"Objective: {results['loss']:.5f}") @staticmethod def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool, print_colorized: bool, remove_header: int) -> str: """ Log result table """ if not results: return '' tabulate.PRESERVE_WHITESPACE = True trials = json_normalize(results, max_level=1) trials['Best'] = '' if 'results_metrics.winsdrawslosses' not in trials.columns: # Ensure compatibility with older versions of hyperopt results trials['results_metrics.winsdrawslosses'] = 'N/A' has_drawdown = True if 'results_metrics.max_drawdown_abs' not in trials.columns: # Ensure compatibility with older versions of hyperopt results trials['results_metrics.max_drawdown_abs'] = None trials['results_metrics.max_drawdown'] = None has_drawdown = False legacy_mode = True if 'results_metrics.total_trades' in trials: legacy_mode = False # New mode, using backtest result for metrics trials['results_metrics.winsdrawslosses'] = trials.apply( lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} " f"{x['results_metrics.losses']:>4}", axis=1) trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades', 'results_metrics.winsdrawslosses', 'results_metrics.profit_mean', 'results_metrics.profit_total_abs', 'results_metrics.profit_total', 'results_metrics.holding_avg', 'results_metrics.max_drawdown', 'results_metrics.max_drawdown_abs', 'loss', 'is_initial_point', 'is_best']] else: # Legacy mode trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count', 'results_metrics.winsdrawslosses', 'results_metrics.avg_profit', 'results_metrics.total_profit', 'results_metrics.profit', 'results_metrics.duration', 'results_metrics.max_drawdown', 'results_metrics.max_drawdown_abs', 'loss', 'is_initial_point', 'is_best']] trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit', 'Total profit', 'Profit', 'Avg duration', 'Max Drawdown', 'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best'] trials['is_profit'] = False trials.loc[trials['is_initial_point'], 'Best'] = '* ' trials.loc[trials['is_best'], 'Best'] = 'Best' trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best' trials.loc[trials['Total profit'] > 0, 'is_profit'] = True trials['Trades'] = trials['Trades'].astype(str) perc_multi = 1 if legacy_mode else 100 trials['Epoch'] = trials['Epoch'].apply( lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs) ) trials['Avg profit'] = trials['Avg profit'].apply( lambda x: f'{x * perc_multi:,.2f}%'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ') ) trials['Avg duration'] = trials['Avg duration'].apply( lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(x, float) else f"{x}" if not isna(x) else "--".rjust(7, ' ') ) trials['Objective'] = trials['Objective'].apply( lambda x: f'{x:,.5f}'.rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ') ) stake_currency = config['stake_currency'] if has_drawdown: trials['Max Drawdown'] = trials.apply( lambda x: '{} {}'.format( round_coin_value(x['max_drawdown_abs'], stake_currency), '({:,.2f}%)'.format(x['Max Drawdown'] * perc_multi).rjust(10, ' ') ).rjust(25 + len(stake_currency)) if x['Max Drawdown'] != 0.0 else '--'.rjust(25 + len(stake_currency)), axis=1 ) else: trials = trials.drop(columns=['Max Drawdown']) trials = trials.drop(columns=['max_drawdown_abs']) trials['Profit'] = trials.apply( lambda x: '{} {}'.format( round_coin_value(x['Total profit'], stake_currency), '({:,.2f}%)'.format(x['Profit'] * perc_multi).rjust(10, ' ') ).rjust(25+len(stake_currency)) if x['Total profit'] != 0.0 else '--'.rjust(25+len(stake_currency)), axis=1 ) trials = trials.drop(columns=['Total profit']) if print_colorized: for i in range(len(trials)): if trials.loc[i]['is_profit']: for j in range(len(trials.loc[i])-3): trials.iat[i, j] = "{}{}{}".format(Fore.GREEN, str(trials.loc[i][j]), Fore.RESET) if trials.loc[i]['is_best'] and highlight_best: for j in range(len(trials.loc[i])-3): trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT, str(trials.loc[i][j]), Style.RESET_ALL) trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit']) if remove_header > 0: table = tabulate.tabulate( trials.to_dict(orient='list'), tablefmt='orgtbl', headers='keys', stralign="right" ) table = table.split("\n", remove_header)[remove_header] elif remove_header < 0: table = tabulate.tabulate( trials.to_dict(orient='list'), tablefmt='psql', headers='keys', stralign="right" ) table = "\n".join(table.split("\n")[0:remove_header]) else: table = tabulate.tabulate( trials.to_dict(orient='list'), tablefmt='psql', headers='keys', stralign="right" ) return table @staticmethod def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool, csv_file: str) -> None: """ Log result to csv-file """ if not results: return # Verification for overwrite if Path(csv_file).is_file(): logger.error(f"CSV file already exists: {csv_file}") return try: io.open(csv_file, 'w+').close() except IOError: logger.error(f"Failed to create CSV file: {csv_file}") return trials = json_normalize(results, max_level=1) trials['Best'] = '' trials['Stake currency'] = config['stake_currency'] if 'results_metrics.total_trades' in trials: base_metrics = ['Best', 'current_epoch', 'results_metrics.total_trades', 'results_metrics.profit_mean', 'results_metrics.profit_median', 'results_metrics.profit_total', 'Stake currency', 'results_metrics.profit_total_abs', 'results_metrics.holding_avg', 'loss', 'is_initial_point', 'is_best'] perc_multi = 100 else: perc_multi = 1 base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count', 'results_metrics.avg_profit', 'results_metrics.median_profit', 'results_metrics.total_profit', 'Stake currency', 'results_metrics.profit', 'results_metrics.duration', 'loss', 'is_initial_point', 'is_best'] param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()] trials = trials[base_metrics + param_metrics] base_columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Median profit', 'Total profit', 'Stake currency', 'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best'] param_columns = list(results[0]['params_dict'].keys()) trials.columns = base_columns + param_columns trials['is_profit'] = False trials.loc[trials['is_initial_point'], 'Best'] = '*' trials.loc[trials['is_best'], 'Best'] = 'Best' trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best' trials.loc[trials['Total profit'] > 0, 'is_profit'] = True trials['Epoch'] = trials['Epoch'].astype(str) trials['Trades'] = trials['Trades'].astype(str) trials['Median profit'] = trials['Median profit'] * perc_multi trials['Total profit'] = trials['Total profit'].apply( lambda x: f'{x:,.8f}' if x != 0.0 else "" ) trials['Profit'] = trials['Profit'].apply( lambda x: f'{x:,.2f}' if not isna(x) else "" ) trials['Avg profit'] = trials['Avg profit'].apply( lambda x: f'{x * perc_multi:,.2f}%' if not isna(x) else "" ) if perc_multi == 1: trials['Avg duration'] = trials['Avg duration'].apply( lambda x: f'{x:,.1f} m' if isinstance( x, float) else f"{x.total_seconds() // 60:,.1f} m" if not isna(x) else "" ) trials['Objective'] = trials['Objective'].apply( lambda x: f'{x:,.5f}' if x != 100000 else "" ) trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit']) trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8') logger.info(f"CSV file created: {csv_file}")