freqtrade_origin/freqtrade/optimize/hyperopt.py

# pragma pylint: disable=too-many-instance-attributes, pointless-string-statement

"""
This module contains the hyperopt logic
"""

import locale
import logging
import sys
from collections import OrderedDict
from operator import itemgetter
from pathlib import Path
from pprint import pprint
from typing import Any, Dict, List, Optional

import rapidjson
from colorama import Fore, Style
from colorama import init as colorama_init
from joblib import (Parallel, cpu_count, delayed, dump, load,
                    wrap_non_picklable_objects)
from pandas import DataFrame
from skopt import Optimizer
from skopt.space import Dimension

from freqtrade.data.history import get_timeframe, trim_dataframe
from freqtrade.misc import plural, round_dict
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
from freqtrade.optimize.hyperopt_interface import IHyperOpt  # noqa: F4
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss  # noqa: F4
from freqtrade.resolvers.hyperopt_resolver import (HyperOptLossResolver,
                                                   HyperOptResolver)

logger = logging.getLogger(__name__)


INITIAL_POINTS = 30

# Keep no more than 2*SKOPT_MODELS_MAX_NUM models
# in the skopt models list
SKOPT_MODELS_MAX_NUM = 10

MAX_LOSS = 100000  # just a big enough number to be bad result in loss optimization


class Hyperopt:
    """
    Hyperopt class, this class contains all the logic to run a hyperopt simulation

    To run a backtest:
    hyperopt = Hyperopt(config)
    hyperopt.start()
    """
    def __init__(self, config: Dict[str, Any]) -> None:
        self.config = config

        self.backtesting = Backtesting(self.config)

        self.custom_hyperopt = HyperOptResolver(self.config).hyperopt

        self.custom_hyperoptloss = HyperOptLossResolver(self.config).hyperoptloss
        self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function

        self.trials_file = (self.config['user_data_dir'] /
                            'hyperopt_results' / 'hyperopt_results.pickle')
        self.tickerdata_pickle = (self.config['user_data_dir'] /
                                  'hyperopt_results' / 'hyperopt_tickerdata.pkl')
        self.total_epochs = config.get('epochs', 0)

        self.current_best_loss = 100

        if not self.config.get('hyperopt_continue'):
            self.clean_hyperopt()
        else:
            logger.info("Continuing on previous hyperopt results.")

        # Previous evaluations
        self.trials: List = []

        self.num_trials_saved = 0

        # Populate functions here (hasattr is slow so should not be run during "regular" operations)
        if hasattr(self.custom_hyperopt, 'populate_indicators'):
            self.backtesting.strategy.advise_indicators = \
                    self.custom_hyperopt.populate_indicators  # type: ignore
        if hasattr(self.custom_hyperopt, 'populate_buy_trend'):
            self.backtesting.strategy.advise_buy = \
                    self.custom_hyperopt.populate_buy_trend  # type: ignore
        if hasattr(self.custom_hyperopt, 'populate_sell_trend'):
            self.backtesting.strategy.advise_sell = \
                    self.custom_hyperopt.populate_sell_trend  # type: ignore

        # Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
        if self.config.get('use_max_market_positions', True):
            self.max_open_trades = self.config['max_open_trades']
        else:
            logger.debug('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
            self.max_open_trades = 0
        self.position_stacking = self.config.get('position_stacking', False)

        if self.has_space('sell'):
            # Make sure use_sell_signal is enabled
            if 'ask_strategy' not in self.config:
                self.config['ask_strategy'] = {}
            self.config['ask_strategy']['use_sell_signal'] = True

    @staticmethod
    def get_lock_filename(config) -> str:

        return str(config['user_data_dir'] / 'hyperopt.lock')

    def clean_hyperopt(self):
        """
        Remove hyperopt pickle files to restart hyperopt.
        """
        for f in [self.tickerdata_pickle, self.trials_file]:
            p = Path(f)
            if p.is_file():
                logger.info(f"Removing `{p}`.")
                p.unlink()

    def get_args(self, params):

        dimensions = self.dimensions

        # Ensure the number of dimensions match
        # the number of parameters in the list x.
        if len(params) != len(dimensions):
            raise ValueError('Mismatch in number of search-space dimensions. '
                             f'len(dimensions)=={len(dimensions)} and len(x)=={len(params)}')

        # Create a dict where the keys are the names of the dimensions
        # and the values are taken from the list of parameters x.
        arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
        return arg_dict

    def save_trials(self, final: bool = False) -> None:
        """
        Save hyperopt trials to file
        """
        num_trials = len(self.trials)
        if num_trials > self.num_trials_saved:
            logger.info(f"Saving {num_trials} {plural(num_trials, 'epoch')}.")
            dump(self.trials, self.trials_file)
            self.num_trials_saved = num_trials
        if final:
            logger.info(f"{num_trials} {plural(num_trials, 'epoch')} "
                        f"saved to '{self.trials_file}'.")

    def read_trials(self) -> List:
        """
        Read hyperopt trials file
        """
        logger.info("Reading Trials from '%s'", self.trials_file)
        trials = load(self.trials_file)
        self.trials_file.unlink()
        return trials

    def log_trials_result(self) -> None:
        """
        Display Best hyperopt result
        """
        # This is printed when Ctrl+C is pressed quickly, before first epochs have
        # a chance to be evaluated.
        if not self.trials:
            print("No epochs evaluated yet, no best result.")
            return

        results = sorted(self.trials, key=itemgetter('loss'))
        best_result = results[0]
        params = best_result['params']
        log_str = self.format_results_logstring(best_result)

        print(f"\nBest result:\n\n{log_str}\n")

        if self.config.get('print_json'):
            result_dict: Dict = {}
            for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
                self._params_update_for_json(result_dict, params, s)
            print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))

        else:
            self._params_pretty_print(params, 'buy', "Buy hyperspace params:")
            self._params_pretty_print(params, 'sell', "Sell hyperspace params:")
            self._params_pretty_print(params, 'roi', "ROI table:")
            self._params_pretty_print(params, 'stoploss', "Stoploss:")
            self._params_pretty_print(params, 'trailing', "Trailing stop:")

    def _params_update_for_json(self, result_dict, params, space: str):
        if self.has_space(space):
            space_params = self.space_params(params, space)
            if space in ['buy', 'sell']:
                result_dict.setdefault('params', {}).update(space_params)
            elif space == 'roi':
                # Convert keys in min_roi dict to strings because
                # rapidjson cannot dump dicts with integer keys...
                # OrderedDict is used to keep the numeric order of the items
                # in the dict.
                result_dict['minimal_roi'] = OrderedDict(
                    (str(k), v) for k, v in space_params.items()
                )
            else:  # 'stoploss', 'trailing'
                result_dict.update(space_params)

    def _params_pretty_print(self, params, space: str, header: str):
        if self.has_space(space):
            space_params = self.space_params(params, space, 5)
            print(header)
            pprint(space_params, indent=4)

    def is_best(self, results) -> bool:
        return results['loss'] < self.current_best_loss

    def log_results(self, results) -> None:
        """
        Log results if it is better than any previous evaluation
        """
        print_all = self.config.get('print_all', False)
        is_best_loss = self.is_best(results)

        if not print_all:
            print('.', end='' if results['current_epoch'] % 100 != 0 else None)  # type: ignore
            sys.stdout.flush()

        if print_all or is_best_loss:
            if is_best_loss:
                self.current_best_loss = results['loss']
            log_str = self.format_results_logstring(results)
            # Colorize output
            if self.config.get('print_colorized', False):
                if results['total_profit'] > 0:
                    log_str = Fore.GREEN + log_str
                if print_all and is_best_loss:
                    log_str = Style.BRIGHT + log_str
            if print_all:
                print(log_str)
            else:
                print(f'\n{log_str}')

    def format_results_logstring(self, results) -> str:
        current = results['current_epoch']
        total = self.total_epochs
        res = results['results_explanation']
        loss = results['loss']
        log_str = f'{current:5d}/{total}: {res} Objective: {loss:.5f}'
        log_str = f'*{log_str}' if results['is_initial_point'] else f' {log_str}'
        return log_str

    def has_space(self, space: str) -> bool:
        """
        Tell if the space value is contained in the configuration
        """
        # The 'trailing' space is not included in the 'default' set of spaces
        if space == 'trailing':
            return any(s in self.config['spaces'] for s in [space, 'all'])
        else:
            return any(s in self.config['spaces'] for s in [space, 'all', 'default'])

    def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]:
        """
        Return the dimensions in the hyperoptimization space.
        :param space: Defines hyperspace to return dimensions for.
        If None, then the self.has_space() will be used to return dimensions
        for all hyperspaces used.
        """
        spaces: List[Dimension] = []

        if space == 'buy' or (space is None and self.has_space('buy')):
            logger.debug("Hyperopt has 'buy' space")
            spaces += self.custom_hyperopt.indicator_space()

        if space == 'sell' or (space is None and self.has_space('sell')):
            logger.debug("Hyperopt has 'sell' space")
            spaces += self.custom_hyperopt.sell_indicator_space()

        if space == 'roi' or (space is None and self.has_space('roi')):
            logger.debug("Hyperopt has 'roi' space")
            spaces += self.custom_hyperopt.roi_space()

        if space == 'stoploss' or (space is None and self.has_space('stoploss')):
            logger.debug("Hyperopt has 'stoploss' space")
            spaces += self.custom_hyperopt.stoploss_space()

        if space == 'trailing' or (space is None and self.has_space('trailing')):
            logger.debug("Hyperopt has 'trailing' space")
            spaces += self.custom_hyperopt.trailing_space()

        return spaces

    def space_params(self, params, space: str, r: int = None) -> Dict:
        if space == 'roi':
            d = self.custom_hyperopt.generate_roi_table(params)
        else:
            d = {p.name: params.get(p.name) for p in self.hyperopt_space(space)}
        # Round floats to `r` digits after the decimal point if requested
        return round_dict(d, r) if r else d

    def generate_optimizer(self, _params: Dict, iteration=None) -> Dict:
        """
        Used Optimize function. Called once per epoch to optimize whatever is configured.
        Keep this function as optimized as possible!
        """
        params = self.get_args(_params)

        if self.has_space('roi'):
            self.backtesting.strategy.minimal_roi = \
                    self.custom_hyperopt.generate_roi_table(params)

        if self.has_space('buy'):
            self.backtesting.strategy.advise_buy = \
                    self.custom_hyperopt.buy_strategy_generator(params)

        if self.has_space('sell'):
            self.backtesting.strategy.advise_sell = \
                    self.custom_hyperopt.sell_strategy_generator(params)

        if self.has_space('stoploss'):
            self.backtesting.strategy.stoploss = params['stoploss']

        if self.has_space('trailing'):
            self.backtesting.strategy.trailing_stop = params['trailing_stop']
            self.backtesting.strategy.trailing_stop_positive = params['trailing_stop_positive']
            self.backtesting.strategy.trailing_stop_positive_offset = \
                params['trailing_stop_positive_offset']
            self.backtesting.strategy.trailing_only_offset_is_reached = \
                params['trailing_only_offset_is_reached']

        processed = load(self.tickerdata_pickle)

        min_date, max_date = get_timeframe(processed)

        results = self.backtesting.backtest(
            {
                'stake_amount': self.config['stake_amount'],
                'processed': processed,
                'max_open_trades': self.max_open_trades,
                'position_stacking': self.position_stacking,
                'start_date': min_date,
                'end_date': max_date,
            }
        )
        results_explanation = self.format_results(results)

        trade_count = len(results.index)
        total_profit = results.profit_abs.sum()

        # If this evaluation contains too short amount of trades to be
        # interesting -- consider it as 'bad' (assigned max. loss value)
        # in order to cast this hyperspace point away from optimization
        # path. We do not want to optimize 'hodl' strategies.
        if trade_count < self.config['hyperopt_min_trades']:
            return {
                'loss': MAX_LOSS,
                'params': params,
                'results_explanation': results_explanation,
                'total_profit': total_profit,
            }

        loss = self.calculate_loss(results=results, trade_count=trade_count,
                                   min_date=min_date.datetime, max_date=max_date.datetime)

        return {
            'loss': loss,
            'params': params,
            'results_explanation': results_explanation,
            'total_profit': total_profit,
        }

    def format_results(self, results: DataFrame) -> str:
        """
        Return the formatted results explanation in a string
        """
        trades = len(results.index)
        avg_profit = results.profit_percent.mean() * 100.0
        total_profit = results.profit_abs.sum()
        stake_cur = self.config['stake_currency']
        profit = results.profit_percent.sum() * 100.0
        duration = results.trade_duration.mean()

        return (f'{trades:6d} trades. Avg profit {avg_profit: 5.2f}%. '
                f'Total profit {total_profit: 11.8f} {stake_cur} '
                f'({profit: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). '
                f'Avg duration {duration:5.1f} mins.'
                ).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')

    def get_optimizer(self, dimensions, cpu_count) -> Optimizer:
        return Optimizer(
            dimensions,
            base_estimator="ET",
            acq_optimizer="auto",
            n_initial_points=INITIAL_POINTS,
            acq_optimizer_kwargs={'n_jobs': cpu_count},
            random_state=self.config.get('hyperopt_random_state', None)
        )

    def fix_optimizer_models_list(self):
        """
        WORKAROUND: Since skopt is not actively supported, this resolves problems with skopt
        memory usage, see also: https://github.com/scikit-optimize/scikit-optimize/pull/746

        This may cease working when skopt updates if implementation of this intrinsic
        part changes.
        """
        n = len(self.opt.models) - SKOPT_MODELS_MAX_NUM
        # Keep no more than 2*SKOPT_MODELS_MAX_NUM models in the skopt models list,
        # remove the old ones. These are actually of no use, the current model
        # from the estimator is the only one used in the skopt optimizer.
        # Freqtrade code also does not inspect details of the models.
        if n >= SKOPT_MODELS_MAX_NUM:
            logger.debug(f"Fixing skopt models list, removing {n} old items...")
            del self.opt.models[0:n]

    def run_optimizer_parallel(self, parallel, asked, i) -> List:
        return parallel(delayed(
                        wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)

    def load_previous_results(self):
        """ read trials file if we have one """
        if self.trials_file.is_file() and self.trials_file.stat().st_size > 0:
            self.trials = self.read_trials()
            logger.info(
                'Loaded %d previous evaluations from disk.',
                len(self.trials)
            )

    def start(self) -> None:
        data, timerange = self.backtesting.load_bt_data()

        preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)

        # Trim startup period from analyzed dataframe
        for pair, df in preprocessed.items():
            preprocessed[pair] = trim_dataframe(df, timerange)
        min_date, max_date = get_timeframe(data)

        logger.info(
            'Hyperopting with data from %s up to %s (%s days)..',
            min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
        )
        dump(preprocessed, self.tickerdata_pickle)

        # We don't need exchange instance anymore while running hyperopt
        self.backtesting.exchange = None  # type: ignore

        self.load_previous_results()

        cpus = cpu_count()
        logger.info(f"Found {cpus} CPU cores. Let's make them scream!")
        config_jobs = self.config.get('hyperopt_jobs', -1)
        logger.info(f'Number of parallel jobs set as: {config_jobs}')

        self.dimensions = self.hyperopt_space()
        self.opt = self.get_optimizer(self.dimensions, config_jobs)

        if self.config.get('print_colorized', False):
            colorama_init(autoreset=True)

        try:
            with Parallel(n_jobs=config_jobs) as parallel:
                jobs = parallel._effective_n_jobs()
                logger.info(f'Effective number of parallel workers used: {jobs}')
                EVALS = max(self.total_epochs // jobs, 1)
                for i in range(EVALS):
                    asked = self.opt.ask(n_points=jobs)
                    f_val = self.run_optimizer_parallel(parallel, asked, i)
                    self.opt.tell(asked, [v['loss'] for v in f_val])
                    self.fix_optimizer_models_list()
                    for j in range(jobs):
                        # Use human-friendly index here (starting from 1)
                        current = i * jobs + j + 1
                        val = f_val[j]
                        val['current_epoch'] = current
                        val['is_initial_point'] = current <= INITIAL_POINTS
                        logger.debug(f"Optimizer epoch evaluated: {val}")
                        is_best = self.is_best(val)
                        self.log_results(val)
                        self.trials.append(val)
                        if is_best or current % 100 == 0:
                            self.save_trials()
        except KeyboardInterrupt:
            print('User interrupted..')

        self.save_trials(final=True)
        self.log_trials_result()