mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-10 18:23:55 +00:00
411 lines
18 KiB
Python
411 lines
18 KiB
Python
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import io
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import logging
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from pathlib import Path
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from typing import Any, Dict, List
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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.exceptions import OperationalException
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from freqtrade.misc import round_coin_value, round_dict
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logger = logging.getLogger(__name__)
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class HyperoptTools():
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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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# The 'trailing' space is not included in the 'default' set of spaces
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if space == 'trailing':
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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_pickle(results_file: Path) -> List:
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"""
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Read hyperopt results from pickle file
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LEGACY method - new files are written as json and cannot be read with this method.
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"""
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from joblib import load
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logger.info(f"Reading pickled epochs from '{results_file}'")
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data = load(results_file)
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return data
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@staticmethod
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def _read_results(results_file: Path) -> List:
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"""
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Read 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 = [rapidjson.loads(line) for line in f]
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return data
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@staticmethod
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def load_previous_results(results_file: Path) -> List:
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"""
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Load data for epochs from the file if we have one
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"""
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epochs: List = []
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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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epochs = HyperoptTools._read_results_pickle(results_file)
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else:
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epochs = HyperoptTools._read_results(results_file)
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# Detection of some old format, without 'is_best' field saved
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if epochs[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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logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
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return 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', 'roi', 'stoploss', 'trailing']:
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HyperoptTools._params_update_for_json(result_dict, params, 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, 'roi', "ROI table:")
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HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:")
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HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:")
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@staticmethod
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def _params_update_for_json(result_dict, params, space: str) -> None:
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if space in params:
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space_params = HyperoptTools._space_params(params, space)
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if space in ['buy', 'sell']:
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result_dict.setdefault('params', {}).update(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 space_params.items()}
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else: # 'stoploss', 'trailing'
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result_dict.update(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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result = f"\n# {header}\n"
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if space == 'stoploss':
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result += f"stoploss = {space_params.get('stoploss')}"
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elif space == 'roi':
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minimal_roi_result = rapidjson.dumps({
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str(k): v for k, v in space_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.items():
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result += f'{k} = {v}\n'
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else:
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no_params = HyperoptTools._space_params(non_optimized, space, 5)
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result += f"{space}_params = {HyperoptTools._pprint(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(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 += " # value loaded from strategy"
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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'] * 100: 6.2f}%. "
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f"Median profit {results_metrics['profit_median'] * 100: 6.2f}%. "
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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'] * 100: 7.2f}%). "
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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, legacy_mode: bool, has_drawdown: bool) -> str:
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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_abs'] = None
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trials['results_metrics.max_drawdown'] = None
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if not legacy_mode:
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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', 'results_metrics.max_drawdown_abs',
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'loss', 'is_initial_point', 'is_best']]
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else:
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# Legacy mode
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trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
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'results_metrics.winsdrawslosses', 'results_metrics.avg_profit',
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'results_metrics.total_profit', 'results_metrics.profit',
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'results_metrics.duration', 'results_metrics.max_drawdown',
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'results_metrics.max_drawdown_abs', 'loss', 'is_initial_point',
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'is_best']]
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trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
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'Total profit', 'Profit', 'Avg duration', 'Max Drawdown',
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'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best']
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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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legacy_mode = 'results_metrics.total_trades' not in trials
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has_drawdown = 'results_metrics.max_drawdown_abs' in trials.columns
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trials = HyperoptTools.prepare_trials_columns(trials, legacy_mode, has_drawdown)
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trials['is_profit'] = False
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trials.loc[trials['is_initial_point'], 'Best'] = '* '
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trials.loc[trials['is_best'], 'Best'] = 'Best'
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trials.loc[trials['is_initial_point'] & trials['is_best'], '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 * perc_multi:,.2f}%'.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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if has_drawdown:
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trials['Max Drawdown'] = trials.apply(
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lambda x: '{} {}'.format(
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round_coin_value(x['max_drawdown_abs'], stake_currency),
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'({:,.2f}%)'.format(x['Max Drawdown'] * perc_multi).rjust(10, ' ')
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).rjust(25 + len(stake_currency))
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if x['Max Drawdown'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
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axis=1
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)
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else:
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trials = trials.drop(columns=['Max Drawdown'])
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trials = trials.drop(columns=['max_drawdown_abs'])
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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),
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'({:,.2f}%)'.format(x['Profit'] * perc_multi).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'])
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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, total_epochs: int, highlight_best: bool,
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csv_file: str) -> None:
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"""
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Log result to csv-file
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"""
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if not results:
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return
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# Verification for overwrite
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if Path(csv_file).is_file():
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logger.error(f"CSV file already exists: {csv_file}")
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return
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try:
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io.open(csv_file, 'w+').close()
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except IOError:
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logger.error(f"Failed to create CSV file: {csv_file}")
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return
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trials = json_normalize(results, max_level=1)
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trials['Best'] = ''
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trials['Stake currency'] = config['stake_currency']
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if 'results_metrics.total_trades' in trials:
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base_metrics = ['Best', 'current_epoch', 'results_metrics.total_trades',
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'results_metrics.profit_mean', 'results_metrics.profit_median',
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'results_metrics.profit_total',
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'Stake currency',
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'results_metrics.profit_total_abs', 'results_metrics.holding_avg',
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'loss', 'is_initial_point', 'is_best']
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perc_multi = 100
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else:
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perc_multi = 1
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base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
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'results_metrics.avg_profit', 'results_metrics.median_profit',
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'results_metrics.total_profit',
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'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
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'loss', 'is_initial_point', 'is_best']
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param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
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trials = trials[base_metrics + param_metrics]
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base_columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Median profit', 'Total profit',
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'Stake currency', 'Profit', 'Avg duration', 'Objective',
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'is_initial_point', 'is_best']
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param_columns = list(results[0]['params_dict'].keys())
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trials.columns = base_columns + param_columns
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trials['is_profit'] = False
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trials.loc[trials['is_initial_point'], 'Best'] = '*'
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trials.loc[trials['is_best'], 'Best'] = 'Best'
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trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
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trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
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trials['Epoch'] = trials['Epoch'].astype(str)
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trials['Trades'] = trials['Trades'].astype(str)
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trials['Median profit'] = trials['Median profit'] * perc_multi
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trials['Total profit'] = trials['Total profit'].apply(
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lambda x: f'{x:,.8f}' if x != 0.0 else ""
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)
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trials['Profit'] = trials['Profit'].apply(
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lambda x: f'{x:,.2f}' if not isna(x) else ""
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)
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trials['Avg profit'] = trials['Avg profit'].apply(
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lambda x: f'{x * perc_multi:,.2f}%' if not isna(x) else ""
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)
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if perc_multi == 1:
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trials['Avg duration'] = trials['Avg duration'].apply(
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lambda x: f'{x:,.1f} m' if isinstance(
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x, float) else f"{x.total_seconds() // 60:,.1f} m" if not isna(x) else ""
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)
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trials['Objective'] = trials['Objective'].apply(
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lambda x: f'{x:,.5f}' if x != 100000 else ""
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)
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trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
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trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
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logger.info(f"CSV file created: {csv_file}")
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