500 lines
21 KiB
Python
Executable File
500 lines
21 KiB
Python
Executable File
import io
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import logging
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from copy import deepcopy
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any, Dict, Iterator, List, Optional, Tuple
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import numpy as np
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import pandas as pd
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import rapidjson
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import tabulate
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from colorama import Fore, Style
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from pandas import isna, json_normalize
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from freqtrade.constants import FTHYPT_FILEVERSION, USERPATH_STRATEGIES
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from freqtrade.exceptions import OperationalException
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from freqtrade.misc import deep_merge_dicts, round_coin_value, round_dict, safe_value_fallback2
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from freqtrade.optimize.hyperopt_epoch_filters import hyperopt_filter_epochs
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logger = logging.getLogger(__name__)
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NON_OPT_PARAM_APPENDIX = " # value loaded from strategy"
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def hyperopt_serializer(x):
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if isinstance(x, np.integer):
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return int(x)
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if isinstance(x, np.bool_):
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return bool(x)
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return str(x)
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class HyperoptTools():
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@staticmethod
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def get_strategy_filename(config: Dict, strategy_name: str) -> Optional[Path]:
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"""
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Get Strategy-location (filename) from strategy_name
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"""
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from freqtrade.resolvers.strategy_resolver import StrategyResolver
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directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
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strategy_objs = StrategyResolver.search_all_objects(directory, False)
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strategies = [s for s in strategy_objs if s['name'] == strategy_name]
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if strategies:
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strategy = strategies[0]
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return Path(strategy['location'])
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return None
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@staticmethod
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def export_params(params, strategy_name: str, filename: Path):
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"""
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Generate files
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"""
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final_params = deepcopy(params['params_not_optimized'])
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final_params = deep_merge_dicts(params['params_details'], final_params)
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final_params = {
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'strategy_name': strategy_name,
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'params': final_params,
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'ft_stratparam_v': 1,
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'export_time': datetime.now(timezone.utc),
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}
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logger.info(f"Dumping parameters to {filename}")
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with filename.open('w') as f:
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rapidjson.dump(final_params, f, indent=2,
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default=hyperopt_serializer,
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number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN
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)
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@staticmethod
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def try_export_params(config: Dict[str, Any], strategy_name: str, params: Dict):
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if params.get(FTHYPT_FILEVERSION, 1) >= 2 and not config.get('disableparamexport', False):
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# Export parameters ...
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fn = HyperoptTools.get_strategy_filename(config, strategy_name)
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if fn:
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HyperoptTools.export_params(params, strategy_name, fn.with_suffix('.json'))
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else:
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logger.warning("Strategy not found, not exporting parameter file.")
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@staticmethod
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def has_space(config: Dict[str, Any], space: str) -> bool:
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"""
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Tell if the space value is contained in the configuration
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"""
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# 'trailing' and 'protection spaces are not included in the 'default' set of spaces
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if space in ('trailing', 'protection'):
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return any(s in config['spaces'] for s in [space, 'all'])
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else:
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return any(s in config['spaces'] for s in [space, 'all', 'default'])
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@staticmethod
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def _read_results(results_file: Path, batch_size: int = 10) -> Iterator[List[Any]]:
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"""
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Stream hyperopt results from file
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"""
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import rapidjson
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logger.info(f"Reading epochs from '{results_file}'")
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with results_file.open('r') as f:
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data = []
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for line in f:
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data += [rapidjson.loads(line)]
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if len(data) >= batch_size:
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yield data
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data = []
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yield data
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@staticmethod
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def _test_hyperopt_results_exist(results_file) -> bool:
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if results_file.is_file() and results_file.stat().st_size > 0:
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if results_file.suffix == '.pickle':
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raise OperationalException(
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"Legacy hyperopt results are no longer supported."
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"Please rerun hyperopt or use an older version to load this file."
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)
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return True
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else:
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# No file found.
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return False
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@staticmethod
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def load_filtered_results(results_file: Path, config: Dict[str, Any]) -> Tuple[List, int]:
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filteroptions = {
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'only_best': config.get('hyperopt_list_best', False),
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'only_profitable': config.get('hyperopt_list_profitable', False),
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'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
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'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
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'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None),
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'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None),
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'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
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'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
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'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
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'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None),
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'filter_min_objective': config.get('hyperopt_list_min_objective', None),
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'filter_max_objective': config.get('hyperopt_list_max_objective', None),
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}
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if not HyperoptTools._test_hyperopt_results_exist(results_file):
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# No file found.
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logger.warning(f"Hyperopt file {results_file} not found.")
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return [], 0
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epochs = []
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total_epochs = 0
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for epochs_tmp in HyperoptTools._read_results(results_file):
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if total_epochs == 0 and epochs_tmp[0].get('is_best') is None:
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raise OperationalException(
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"The file with HyperoptTools results is incompatible with this version "
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"of Freqtrade and cannot be loaded.")
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total_epochs += len(epochs_tmp)
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epochs += hyperopt_filter_epochs(epochs_tmp, filteroptions, log=False)
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logger.info(f"Loaded {total_epochs} previous evaluations from disk.")
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# Final filter run ...
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epochs = hyperopt_filter_epochs(epochs, filteroptions, log=True)
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return epochs, total_epochs
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@staticmethod
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def show_epoch_details(results, total_epochs: int, print_json: bool,
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no_header: bool = False, header_str: str = None) -> None:
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"""
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Display details of the hyperopt result
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"""
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params = results.get('params_details', {})
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non_optimized = results.get('params_not_optimized', {})
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# Default header string
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if header_str is None:
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header_str = "Best result"
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if not no_header:
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explanation_str = HyperoptTools._format_explanation_string(results, total_epochs)
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print(f"\n{header_str}:\n\n{explanation_str}\n")
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if print_json:
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result_dict: Dict = {}
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for s in ['buy', 'sell', 'protection', 'roi', 'stoploss', 'trailing']:
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HyperoptTools._params_update_for_json(result_dict, params, non_optimized, s)
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print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
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else:
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HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:",
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non_optimized)
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HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:",
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non_optimized)
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HyperoptTools._params_pretty_print(params, 'protection',
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"Protection hyperspace params:", non_optimized)
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HyperoptTools._params_pretty_print(params, 'roi', "ROI table:", non_optimized)
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HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:", non_optimized)
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HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:", non_optimized)
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@staticmethod
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def _params_update_for_json(result_dict, params, non_optimized, space: str) -> None:
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if (space in params) or (space in non_optimized):
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space_params = HyperoptTools._space_params(params, space)
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space_non_optimized = HyperoptTools._space_params(non_optimized, space)
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all_space_params = space_params
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# Merge non optimized params if there are any
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if len(space_non_optimized) > 0:
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all_space_params = {**space_params, **space_non_optimized}
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if space in ['buy', 'sell']:
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result_dict.setdefault('params', {}).update(all_space_params)
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elif space == 'roi':
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# Convert keys in min_roi dict to strings because
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# rapidjson cannot dump dicts with integer keys...
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result_dict['minimal_roi'] = {str(k): v for k, v in all_space_params.items()}
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else: # 'stoploss', 'trailing'
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result_dict.update(all_space_params)
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@staticmethod
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def _params_pretty_print(params, space: str, header: str, non_optimized={}) -> None:
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if space in params or space in non_optimized:
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space_params = HyperoptTools._space_params(params, space, 5)
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no_params = HyperoptTools._space_params(non_optimized, space, 5)
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appendix = ''
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if not space_params and not no_params:
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# No parameters - don't print
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return
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if not space_params:
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# Not optimized parameters - append string
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appendix = NON_OPT_PARAM_APPENDIX
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result = f"\n# {header}\n"
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if space == "stoploss":
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stoploss = safe_value_fallback2(space_params, no_params, space, space)
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result += (f"stoploss = {stoploss}{appendix}")
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elif space == "roi":
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result = result[:-1] + f'{appendix}\n'
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minimal_roi_result = rapidjson.dumps({
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str(k): v for k, v in (space_params or no_params).items()
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}, default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
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result += f"minimal_roi = {minimal_roi_result}"
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elif space == "trailing":
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for k, v in (space_params or no_params).items():
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result += f"{k} = {v}{appendix}\n"
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else:
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# Buy / sell parameters
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result += f"{space}_params = {HyperoptTools._pprint_dict(space_params, no_params)}"
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result = result.replace("\n", "\n ")
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print(result)
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@staticmethod
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def _space_params(params, space: str, r: int = None) -> Dict:
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d = params.get(space)
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if d:
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# Round floats to `r` digits after the decimal point if requested
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return round_dict(d, r) if r else d
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return {}
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@staticmethod
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def _pprint_dict(params, non_optimized, indent: int = 4):
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"""
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Pretty-print hyperopt results (based on 2 dicts - with add. comment)
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"""
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p = params.copy()
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p.update(non_optimized)
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result = '{\n'
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for k, param in p.items():
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result += " " * indent + f'"{k}": '
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result += f'"{param}",' if isinstance(param, str) else f'{param},'
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if k in non_optimized:
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result += NON_OPT_PARAM_APPENDIX
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result += "\n"
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result += '}'
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return result
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@staticmethod
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def is_best_loss(results, current_best_loss: float) -> bool:
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return bool(results['loss'] < current_best_loss)
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@staticmethod
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def format_results_explanation_string(results_metrics: Dict, stake_currency: str) -> str:
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"""
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Return the formatted results explanation in a string
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"""
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return (f"{results_metrics['total_trades']:6d} trades. "
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f"{results_metrics['wins']}/{results_metrics['draws']}"
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f"/{results_metrics['losses']} Wins/Draws/Losses. "
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f"Avg profit {results_metrics['profit_mean']:7.2%}. "
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f"Median profit {results_metrics['profit_median']:7.2%}. "
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f"Total profit {results_metrics['profit_total_abs']:11.8f} {stake_currency} "
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f"({results_metrics['profit_total']:8.2%}). "
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f"Avg duration {results_metrics['holding_avg']} min."
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)
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@staticmethod
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def _format_explanation_string(results, total_epochs) -> str:
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return (("*" if results['is_initial_point'] else " ") +
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f"{results['current_epoch']:5d}/{total_epochs}: " +
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f"{results['results_explanation']} " +
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f"Objective: {results['loss']:.5f}")
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@staticmethod
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def prepare_trials_columns(trials: pd.DataFrame, has_drawdown: bool) -> pd.DataFrame:
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trials['Best'] = ''
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if 'results_metrics.winsdrawslosses' not in trials.columns:
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# Ensure compatibility with older versions of hyperopt results
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trials['results_metrics.winsdrawslosses'] = 'N/A'
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if not has_drawdown:
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# Ensure compatibility with older versions of hyperopt results
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trials['results_metrics.max_drawdown_account'] = None
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if 'is_random' not in trials.columns:
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trials['is_random'] = False
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# New mode, using backtest result for metrics
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trials['results_metrics.winsdrawslosses'] = trials.apply(
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lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
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f"{x['results_metrics.losses']:>4}", axis=1)
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trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
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'results_metrics.winsdrawslosses',
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'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
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'results_metrics.profit_total', 'results_metrics.holding_avg',
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'results_metrics.max_drawdown',
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'results_metrics.max_drawdown_account', 'results_metrics.max_drawdown_abs',
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'loss', 'is_initial_point', 'is_random', 'is_best']]
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trials.columns = [
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'Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
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'Total profit', 'Profit', 'Avg duration', 'max_drawdown', 'max_drawdown_account',
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'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_random', 'is_best'
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]
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return trials
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@staticmethod
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def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
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print_colorized: bool, remove_header: int) -> str:
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"""
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Log result table
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"""
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if not results:
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return ''
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tabulate.PRESERVE_WHITESPACE = True
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trials = json_normalize(results, max_level=1)
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has_account_drawdown = 'results_metrics.max_drawdown_account' in trials.columns
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trials = HyperoptTools.prepare_trials_columns(trials, has_account_drawdown)
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trials['is_profit'] = False
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trials.loc[trials['is_initial_point'] | trials['is_random'], 'Best'] = '* '
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trials.loc[trials['is_best'], 'Best'] = 'Best'
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trials.loc[
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(trials['is_initial_point'] | trials['is_random']) & trials['is_best'],
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'Best'] = '* Best'
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trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
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trials['Trades'] = trials['Trades'].astype(str)
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# perc_multi = 1 if legacy_mode else 100
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trials['Epoch'] = trials['Epoch'].apply(
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lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
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)
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trials['Avg profit'] = trials['Avg profit'].apply(
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lambda x: f'{x:,.2%}'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
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)
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trials['Avg duration'] = trials['Avg duration'].apply(
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lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(x, float) else f"{x}"
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if not isna(x) else "--".rjust(7, ' ')
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)
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trials['Objective'] = trials['Objective'].apply(
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lambda x: f'{x:,.5f}'.rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
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)
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stake_currency = config['stake_currency']
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trials[f"Max Drawdown{' (Acct)' if has_account_drawdown else ''}"] = trials.apply(
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lambda x: "{} {}".format(
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round_coin_value(x['max_drawdown_abs'], stake_currency, keep_trailing_zeros=True),
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(f"({x['max_drawdown_account']:,.2%})"
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if has_account_drawdown
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else f"({x['max_drawdown']:,.2%})"
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).rjust(10, ' ')
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).rjust(25 + len(stake_currency))
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if x['max_drawdown'] != 0.0 or x['max_drawdown_account'] != 0.0
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else '--'.rjust(25 + len(stake_currency)),
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axis=1
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)
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trials = trials.drop(columns=['max_drawdown_abs', 'max_drawdown', 'max_drawdown_account'])
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trials['Profit'] = trials.apply(
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lambda x: '{} {}'.format(
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round_coin_value(x['Total profit'], stake_currency, keep_trailing_zeros=True),
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f"({x['Profit']:,.2%})".rjust(10, ' ')
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).rjust(25 + len(stake_currency))
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if x['Total profit'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
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axis=1
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)
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trials = trials.drop(columns=['Total profit'])
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if print_colorized:
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for i in range(len(trials)):
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if trials.loc[i]['is_profit']:
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for j in range(len(trials.loc[i]) - 3):
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trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
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str(trials.loc[i][j]), Fore.RESET)
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if trials.loc[i]['is_best'] and highlight_best:
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for j in range(len(trials.loc[i]) - 3):
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trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
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str(trials.loc[i][j]), Style.RESET_ALL)
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trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit', 'is_random'])
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if remove_header > 0:
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table = tabulate.tabulate(
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trials.to_dict(orient='list'), tablefmt='orgtbl',
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headers='keys', stralign="right"
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)
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table = table.split("\n", remove_header)[remove_header]
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elif remove_header < 0:
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table = tabulate.tabulate(
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trials.to_dict(orient='list'), tablefmt='psql',
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headers='keys', stralign="right"
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)
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table = "\n".join(table.split("\n")[0:remove_header])
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else:
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table = tabulate.tabulate(
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trials.to_dict(orient='list'), tablefmt='psql',
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headers='keys', stralign="right"
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)
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return table
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@staticmethod
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def export_csv_file(config: dict, results: list, csv_file: str) -> None:
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"""
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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}")
|