stable/freqtrade/optimize/hyperopt_tools.py

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, USERPATH_STRATEGIES, 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


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
        directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
        strategy_objs = StrategyResolver.search_all_objects(
            directory, 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: 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_account', 'results_metrics.max_drawdown_abs',
                         'loss', 'is_initial_point', 'is_random', 'is_best']]

        trials.columns = [
            'Best', 'Epoch', 'Trades', ' Win Draw Loss', '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}")