import io import logging from copy import deepcopy from datetime import datetime, timezone from pathlib import Path from typing import Any, Dict, Iterator, List, Optional, Tuple import numpy as np import pandas as pd import rapidjson import tabulate from colorama import Fore, Style from pandas import isna, json_normalize from freqtrade.constants import FTHYPT_FILEVERSION, Config from freqtrade.enums import HyperoptState from freqtrade.exceptions import OperationalException from freqtrade.misc import deep_merge_dicts, round_coin_value, round_dict, safe_value_fallback2 from freqtrade.optimize.hyperopt_epoch_filters import hyperopt_filter_epochs from freqtrade.optimize.optimize_reports import generate_wins_draws_losses logger = logging.getLogger(__name__) NON_OPT_PARAM_APPENDIX = " # value loaded from strategy" def hyperopt_serializer(x): if isinstance(x, np.integer): return int(x) if isinstance(x, np.bool_): return bool(x) return str(x) class HyperoptStateContainer(): """ Singleton class to track state of hyperopt""" state: HyperoptState = HyperoptState.OPTIMIZE @classmethod def set_state(cls, value: HyperoptState): cls.state = value class HyperoptTools(): @staticmethod def get_strategy_filename(config: Config, strategy_name: str) -> Optional[Path]: """ Get Strategy-location (filename) from strategy_name """ from freqtrade.resolvers.strategy_resolver import StrategyResolver strategy_objs = StrategyResolver.search_all_objects( config, False, config.get('recursive_strategy_search', False)) strategies = [s for s in strategy_objs if s['name'] == strategy_name] if strategies: strategy = strategies[0] return Path(strategy['location']) return None @staticmethod def export_params(params, strategy_name: str, filename: Path): """ Generate files """ final_params = deepcopy(params['params_not_optimized']) final_params = deep_merge_dicts(params['params_details'], final_params) final_params = { 'strategy_name': strategy_name, 'params': final_params, 'ft_stratparam_v': 1, 'export_time': datetime.now(timezone.utc), } logger.info(f"Dumping parameters to {filename}") with filename.open('w') as f: rapidjson.dump(final_params, f, indent=2, default=hyperopt_serializer, number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN ) @staticmethod def try_export_params(config: Config, strategy_name: str, params: Dict): if params.get(FTHYPT_FILEVERSION, 1) >= 2 and not config.get('disableparamexport', False): # Export parameters ... fn = HyperoptTools.get_strategy_filename(config, strategy_name) if fn: HyperoptTools.export_params(params, strategy_name, fn.with_suffix('.json')) else: logger.warning("Strategy not found, not exporting parameter file.") @staticmethod def has_space(config: Config, space: str) -> bool: """ Tell if the space value is contained in the configuration """ # 'trailing' and 'protection spaces are not included in the 'default' set of spaces if space in ('trailing', 'protection'): 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(results_file: Path, batch_size: int = 10) -> Iterator[List[Any]]: """ Stream hyperopt results from file """ import rapidjson logger.info(f"Reading epochs from '{results_file}'") with results_file.open('r') as f: data = [] for line in f: data += [rapidjson.loads(line)] if len(data) >= batch_size: yield data data = [] yield data @staticmethod def _test_hyperopt_results_exist(results_file) -> bool: if results_file.is_file() and results_file.stat().st_size > 0: if results_file.suffix == '.pickle': raise OperationalException( "Legacy hyperopt results are no longer supported." "Please rerun hyperopt or use an older version to load this file." ) return True else: # No file found. return False @staticmethod def load_filtered_results(results_file: Path, config: Config) -> Tuple[List, int]: 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'), 'filter_max_avg_time': config.get('hyperopt_list_max_avg_time'), 'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit'), 'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit'), 'filter_min_total_profit': config.get('hyperopt_list_min_total_profit'), 'filter_max_total_profit': config.get('hyperopt_list_max_total_profit'), 'filter_min_objective': config.get('hyperopt_list_min_objective'), 'filter_max_objective': config.get('hyperopt_list_max_objective'), } if not HyperoptTools._test_hyperopt_results_exist(results_file): # No file found. logger.warning(f"Hyperopt file {results_file} not found.") return [], 0 epochs = [] total_epochs = 0 for epochs_tmp in HyperoptTools._read_results(results_file): if total_epochs == 0 and epochs_tmp[0].get('is_best') is None: raise OperationalException( "The file with HyperoptTools results is incompatible with this version " "of Freqtrade and cannot be loaded.") total_epochs += len(epochs_tmp) epochs += hyperopt_filter_epochs(epochs_tmp, filteroptions, log=False) logger.info(f"Loaded {total_epochs} previous evaluations from disk.") # Final filter run ... epochs = hyperopt_filter_epochs(epochs, filteroptions, log=True) return epochs, total_epochs @staticmethod def show_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', 'protection', 'roi', 'stoploss', 'trailing']: HyperoptTools._params_update_for_json(result_dict, params, non_optimized, 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, 'protection', "Protection hyperspace params:", non_optimized) HyperoptTools._params_pretty_print(params, 'roi', "ROI table:", non_optimized) HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:", non_optimized) HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:", non_optimized) @staticmethod def _params_update_for_json(result_dict, params, non_optimized, space: str) -> None: if (space in params) or (space in non_optimized): space_params = HyperoptTools._space_params(params, space) space_non_optimized = HyperoptTools._space_params(non_optimized, space) all_space_params = space_params # Merge non optimized params if there are any if len(space_non_optimized) > 0: all_space_params = {**space_params, **space_non_optimized} if space in ['buy', 'sell']: result_dict.setdefault('params', {}).update(all_space_params) elif space == 'roi': # Convert keys in min_roi dict to strings because # rapidjson cannot dump dicts with integer keys... result_dict['minimal_roi'] = {str(k): v for k, v in all_space_params.items()} else: # 'stoploss', 'trailing' result_dict.update(all_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) no_params = HyperoptTools._space_params(non_optimized, space, 5) appendix = '' if not space_params and not no_params: # No parameters - don't print return if not space_params: # Not optimized parameters - append string appendix = NON_OPT_PARAM_APPENDIX result = f"\n# {header}\n" if space == "stoploss": stoploss = safe_value_fallback2(space_params, no_params, space, space) result += (f"stoploss = {stoploss}{appendix}") elif space == "roi": result = result[:-1] + f'{appendix}\n' minimal_roi_result = rapidjson.dumps({ str(k): v for k, v in (space_params or no_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 or no_params).items(): result += f"{k} = {v}{appendix}\n" else: # Buy / sell parameters result += f"{space}_params = {HyperoptTools._pprint_dict(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_dict(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 += NON_OPT_PARAM_APPENDIX 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']:7.2%}. " f"Median profit {results_metrics['profit_median']:7.2%}. " f"Total profit {results_metrics['profit_total_abs']:11.8f} {stake_currency} " f"({results_metrics['profit_total']:8.2%}). " 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 prepare_trials_columns(trials: pd.DataFrame, has_drawdown: bool) -> pd.DataFrame: trials['Best'] = '' if 'results_metrics.winsdrawslosses' not in trials.columns: # Ensure compatibility with older versions of hyperopt results trials['results_metrics.winsdrawslosses'] = 'N/A' if not has_drawdown: # Ensure compatibility with older versions of hyperopt results trials['results_metrics.max_drawdown_account'] = None if 'is_random' not in trials.columns: trials['is_random'] = False # New mode, using backtest result for metrics trials['results_metrics.winsdrawslosses'] = trials.apply( lambda x: generate_wins_draws_losses( x['results_metrics.wins'], x['results_metrics.draws'], x['results_metrics.losses'] ), 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_account', 'results_metrics.max_drawdown_abs', 'loss', 'is_initial_point', 'is_random', 'is_best']] trials.columns = [ 'Best', 'Epoch', 'Trades', ' Win Draw Loss Win%', 'Avg profit', 'Total profit', 'Profit', 'Avg duration', 'max_drawdown', 'max_drawdown_account', 'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_random', 'is_best' ] return trials @staticmethod def get_result_table(config: Config, 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) has_account_drawdown = 'results_metrics.max_drawdown_account' in trials.columns trials = HyperoptTools.prepare_trials_columns(trials, has_account_drawdown) trials['is_profit'] = False trials.loc[trials['is_initial_point'] | trials['is_random'], 'Best'] = '* ' trials.loc[trials['is_best'], 'Best'] = 'Best' trials.loc[ (trials['is_initial_point'] | trials['is_random']) & 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:,.2%}'.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'] trials[f"Max Drawdown{' (Acct)' if has_account_drawdown else ''}"] = trials.apply( lambda x: "{} {}".format( round_coin_value(x['max_drawdown_abs'], stake_currency, keep_trailing_zeros=True), (f"({x['max_drawdown_account']:,.2%})" if has_account_drawdown else f"({x['max_drawdown']:,.2%})" ).rjust(10, ' ') ).rjust(25 + len(stake_currency)) if x['max_drawdown'] != 0.0 or x['max_drawdown_account'] != 0.0 else '--'.rjust(25 + len(stake_currency)), axis=1 ) trials = trials.drop(columns=['max_drawdown_abs', 'max_drawdown', 'max_drawdown_account']) trials['Profit'] = trials.apply( lambda x: '{} {}'.format( round_coin_value(x['Total profit'], stake_currency, keep_trailing_zeros=True), f"({x['Profit']:,.2%})".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', 'is_random']) 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: Config, results: list, 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'] 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 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 "" ) 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}")