stable/freqtrade/optimize/hyperopt.py

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

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

import logging
import random
import warnings
from datetime import datetime, timezone
from math import ceil
from pathlib import Path
from typing import Any, Dict, List, Optional

import progressbar
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 freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN
from freqtrade.data.converter import trim_dataframes
from freqtrade.data.history import get_timerange
from freqtrade.misc import deep_merge_dicts, file_dump_json, plural
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
from freqtrade.optimize.hyperopt_auto import HyperOptAuto
from freqtrade.optimize.hyperopt_interface import IHyperOpt  # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss  # noqa: F401
from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer
from freqtrade.optimize.optimize_reports import generate_strategy_stats
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver


# Suppress scikit-learn FutureWarnings from skopt
with warnings.catch_warnings():
    warnings.filterwarnings("ignore", category=FutureWarning)
    from skopt import Optimizer
    from skopt.space import Dimension

progressbar.streams.wrap_stderr()
progressbar.streams.wrap_stdout()
logger = logging.getLogger(__name__)


INITIAL_POINTS = 30

# Keep no more than SKOPT_MODEL_QUEUE_SIZE models
# in the skopt model queue, to optimize memory consumption
SKOPT_MODEL_QUEUE_SIZE = 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()
    """
    custom_hyperopt: IHyperOpt

    def __init__(self, config: Dict[str, Any]) -> None:
        self.buy_space: List[Dimension] = []
        self.sell_space: List[Dimension] = []
        self.roi_space: List[Dimension] = []
        self.stoploss_space: List[Dimension] = []
        self.trailing_space: List[Dimension] = []
        self.dimensions: List[Dimension] = []

        self.config = config

        self.backtesting = Backtesting(self.config)

        if not self.config.get('hyperopt'):
            self.custom_hyperopt = HyperOptAuto(self.config)
            self.auto_hyperopt = True
        else:
            self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
            self.auto_hyperopt = False

        self.backtesting._set_strategy(self.backtesting.strategylist[0])
        self.custom_hyperopt.strategy = self.backtesting.strategy

        self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
        self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
        time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
        strategy = str(self.config['strategy'])
        self.results_file: Path = (self.config['user_data_dir'] / 'hyperopt_results' /
                                   f'strategy_{strategy}_{time_now}.fthypt')
        self.data_pickle_file = (self.config['user_data_dir'] /
                                 'hyperopt_results' / 'hyperopt_tickerdata.pkl')
        self.total_epochs = config.get('epochs', 0)

        self.current_best_loss = 100

        self.clean_hyperopt()

        self.num_epochs_saved = 0
        self.current_best_epoch: Optional[Dict[str, Any]] = None

        if not self.auto_hyperopt:
            # Populate "fallback" 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 = (  # type: ignore
                    self.custom_hyperopt.populate_indicators)  # type: ignore
            if hasattr(self.custom_hyperopt, 'populate_buy_trend'):
                self.backtesting.strategy.advise_buy = (  # type: ignore
                    self.custom_hyperopt.populate_buy_trend)  # type: ignore
            if hasattr(self.custom_hyperopt, 'populate_sell_trend'):
                self.backtesting.strategy.advise_sell = (  # type: ignore
                    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 HyperoptTools.has_space(self.config, '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

        self.print_all = self.config.get('print_all', False)
        self.hyperopt_table_header = 0
        self.print_colorized = self.config.get('print_colorized', False)
        self.print_json = self.config.get('print_json', False)

    @staticmethod
    def get_lock_filename(config: Dict[str, Any]) -> str:

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

    def clean_hyperopt(self) -> None:
        """
        Remove hyperopt pickle files to restart hyperopt.
        """
        for f in [self.data_pickle_file, self.results_file]:
            p = Path(f)
            if p.is_file():
                logger.info(f"Removing `{p}`.")
                p.unlink()

    def _get_params_dict(self, dimensions: List[Dimension], raw_params: List[Any]) -> Dict:

        # Ensure the number of dimensions match
        # the number of parameters in the list.
        if len(raw_params) != len(dimensions):
            raise ValueError('Mismatch in number of search-space dimensions.')

        # Return a dict where the keys are the names of the dimensions
        # and the values are taken from the list of parameters.
        return {d.name: v for d, v in zip(dimensions, raw_params)}

    def _save_result(self, epoch: Dict) -> None:
        """
        Save hyperopt results to file
        Store one line per epoch.
        While not a valid json object - this allows appending easily.
        :param epoch: result dictionary for this epoch.
        """
        epoch[FTHYPT_FILEVERSION] = 2
        with self.results_file.open('a') as f:
            rapidjson.dump(epoch, f, default=hyperopt_serializer,
                           number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN)
            f.write("\n")

        self.num_epochs_saved += 1
        logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
                     f"saved to '{self.results_file}'.")
        # Store hyperopt filename
        latest_filename = Path.joinpath(self.results_file.parent, LAST_BT_RESULT_FN)
        file_dump_json(latest_filename, {'latest_hyperopt': str(self.results_file.name)},
                       log=False)

    def _get_params_details(self, params: Dict) -> Dict:
        """
        Return the params for each space
        """
        result: Dict = {}

        if HyperoptTools.has_space(self.config, 'buy'):
            result['buy'] = {p.name: params.get(p.name) for p in self.buy_space}
        if HyperoptTools.has_space(self.config, 'sell'):
            result['sell'] = {p.name: params.get(p.name) for p in self.sell_space}
        if HyperoptTools.has_space(self.config, 'roi'):
            result['roi'] = {str(k): v for k, v in
                             self.custom_hyperopt.generate_roi_table(params).items()}
        if HyperoptTools.has_space(self.config, 'stoploss'):
            result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
        if HyperoptTools.has_space(self.config, 'trailing'):
            result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)

        return result

    def _get_no_optimize_details(self) -> Dict[str, Any]:
        """
        Get non-optimized parameters
        """
        result: Dict[str, Any] = {}
        strategy = self.backtesting.strategy
        if not HyperoptTools.has_space(self.config, 'roi'):
            result['roi'] = {str(k): v for k, v in strategy.minimal_roi.items()}
        if not HyperoptTools.has_space(self.config, 'stoploss'):
            result['stoploss'] = {'stoploss': strategy.stoploss}
        if not HyperoptTools.has_space(self.config, 'trailing'):
            result['trailing'] = {
                'trailing_stop': strategy.trailing_stop,
                'trailing_stop_positive': strategy.trailing_stop_positive,
                'trailing_stop_positive_offset': strategy.trailing_stop_positive_offset,
                'trailing_only_offset_is_reached': strategy.trailing_only_offset_is_reached,
            }
        return result

    def print_results(self, results) -> None:
        """
        Log results if it is better than any previous evaluation
        TODO: this should be moved to HyperoptTools too
        """
        is_best = results['is_best']

        if self.print_all or is_best:
            print(
                HyperoptTools.get_result_table(
                    self.config, results, self.total_epochs,
                    self.print_all, self.print_colorized,
                    self.hyperopt_table_header
                )
            )
            self.hyperopt_table_header = 2

    def init_spaces(self):
        """
        Assign the dimensions in the hyperoptimization space.
        """

        if HyperoptTools.has_space(self.config, 'buy'):
            logger.debug("Hyperopt has 'buy' space")
            self.buy_space = self.custom_hyperopt.indicator_space()

        if HyperoptTools.has_space(self.config, 'sell'):
            logger.debug("Hyperopt has 'sell' space")
            self.sell_space = self.custom_hyperopt.sell_indicator_space()

        if HyperoptTools.has_space(self.config, 'roi'):
            logger.debug("Hyperopt has 'roi' space")
            self.roi_space = self.custom_hyperopt.roi_space()

        if HyperoptTools.has_space(self.config, 'stoploss'):
            logger.debug("Hyperopt has 'stoploss' space")
            self.stoploss_space = self.custom_hyperopt.stoploss_space()

        if HyperoptTools.has_space(self.config, 'trailing'):
            logger.debug("Hyperopt has 'trailing' space")
            self.trailing_space = self.custom_hyperopt.trailing_space()
        self.dimensions = (self.buy_space + self.sell_space + self.roi_space +
                           self.stoploss_space + self.trailing_space)

    def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
        """
        Used Optimize function.
        Called once per epoch to optimize whatever is configured.
        Keep this function as optimized as possible!
        """
        backtest_start_time = datetime.now(timezone.utc)
        params_dict = self._get_params_dict(self.dimensions, raw_params)

        # Apply parameters
        if HyperoptTools.has_space(self.config, 'buy'):
            self.backtesting.strategy.advise_buy = (  # type: ignore
                self.custom_hyperopt.buy_strategy_generator(params_dict))

        if HyperoptTools.has_space(self.config, 'sell'):
            self.backtesting.strategy.advise_sell = (  # type: ignore
                self.custom_hyperopt.sell_strategy_generator(params_dict))

        if HyperoptTools.has_space(self.config, 'roi'):
            self.backtesting.strategy.minimal_roi = (  # type: ignore
                self.custom_hyperopt.generate_roi_table(params_dict))

        if HyperoptTools.has_space(self.config, 'stoploss'):
            self.backtesting.strategy.stoploss = params_dict['stoploss']

        if HyperoptTools.has_space(self.config, 'trailing'):
            d = self.custom_hyperopt.generate_trailing_params(params_dict)
            self.backtesting.strategy.trailing_stop = d['trailing_stop']
            self.backtesting.strategy.trailing_stop_positive = d['trailing_stop_positive']
            self.backtesting.strategy.trailing_stop_positive_offset = \
                d['trailing_stop_positive_offset']
            self.backtesting.strategy.trailing_only_offset_is_reached = \
                d['trailing_only_offset_is_reached']

        with self.data_pickle_file.open('rb') as f:
            processed = load(f, mmap_mode='r')
        bt_results = self.backtesting.backtest(
            processed=processed,
            start_date=self.min_date,
            end_date=self.max_date,
            max_open_trades=self.max_open_trades,
            position_stacking=self.position_stacking,
            enable_protections=self.config.get('enable_protections', False),
        )
        backtest_end_time = datetime.now(timezone.utc)
        bt_results.update({
            'backtest_start_time': int(backtest_start_time.timestamp()),
            'backtest_end_time': int(backtest_end_time.timestamp()),
        })

        return self._get_results_dict(bt_results, self.min_date, self.max_date,
                                      params_dict,
                                      processed=processed)

    def _get_results_dict(self, backtesting_results, min_date, max_date,
                          params_dict, processed: Dict[str, DataFrame]
                          ) -> Dict[str, Any]:
        params_details = self._get_params_details(params_dict)

        strat_stats = generate_strategy_stats(
            processed, self.backtesting.strategy.get_strategy_name(),
            backtesting_results, min_date, max_date, market_change=0
        )
        results_explanation = HyperoptTools.format_results_explanation_string(
            strat_stats, self.config['stake_currency'])

        not_optimized = self.backtesting.strategy.get_no_optimize_params()
        not_optimized = deep_merge_dicts(not_optimized, self._get_no_optimize_details())

        trade_count = strat_stats['total_trades']
        total_profit = strat_stats['profit_total']

        # 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.
        loss: float = MAX_LOSS
        if trade_count >= self.config['hyperopt_min_trades']:
            loss = self.calculate_loss(results=backtesting_results['results'],
                                       trade_count=trade_count,
                                       min_date=min_date, max_date=max_date,
                                       config=self.config, processed=processed,
                                       backtest_stats=strat_stats)
        return {
            'loss': loss,
            'params_dict': params_dict,
            'params_details': params_details,
            'params_not_optimized': not_optimized,
            'results_metrics': strat_stats,
            'results_explanation': results_explanation,
            'total_profit': total_profit,
        }

    def get_optimizer(self, dimensions: List[Dimension], 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.random_state,
            model_queue_size=SKOPT_MODEL_QUEUE_SIZE,
        )

    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 _set_random_state(self, random_state: Optional[int]) -> int:
        return random_state or random.randint(1, 2**16 - 1)

    def prepare_hyperopt_data(self) -> None:
        data, timerange = self.backtesting.load_bt_data()
        logger.info("Dataload complete. Calculating indicators")

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

        # Trim startup period from analyzed dataframe to get correct dates for output.
        processed = trim_dataframes(preprocessed, timerange, self.backtesting.required_startup)
        self.min_date, self.max_date = get_timerange(processed)

        logger.info(f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
                    f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
                    f'({(self.max_date - self.min_date).days} days)..')
        # Store non-trimmed data - will be trimmed after signal generation.
        dump(preprocessed, self.data_pickle_file)

    def start(self) -> None:
        self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
        logger.info(f"Using optimizer random state: {self.random_state}")
        self.hyperopt_table_header = -1
        # Initialize spaces ...
        self.init_spaces()

        self.prepare_hyperopt_data()

        # We don't need exchange instance anymore while running hyperopt
        self.backtesting.exchange.close()
        self.backtesting.exchange._api = None  # type: ignore
        self.backtesting.exchange._api_async = None  # type: ignore
        # self.backtesting.exchange = None  # type: ignore
        self.backtesting.pairlists = None  # type: ignore

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

        if self.print_colorized:
            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}')

                # Define progressbar
                if self.print_colorized:
                    widgets = [
                        ' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
                        ' (', progressbar.Percentage(), ')] ',
                        progressbar.Bar(marker=progressbar.AnimatedMarker(
                            fill='\N{FULL BLOCK}',
                            fill_wrap=Fore.GREEN + '{}' + Fore.RESET,
                            marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL,
                        )),
                        ' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
                    ]
                else:
                    widgets = [
                        ' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
                        ' (', progressbar.Percentage(), ')] ',
                        progressbar.Bar(marker=progressbar.AnimatedMarker(
                            fill='\N{FULL BLOCK}',
                        )),
                        ' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
                    ]
                with progressbar.ProgressBar(
                    max_value=self.total_epochs, redirect_stdout=False, redirect_stderr=False,
                    widgets=widgets
                ) as pbar:
                    EVALS = ceil(self.total_epochs / jobs)
                    for i in range(EVALS):
                        # Correct the number of epochs to be processed for the last
                        # iteration (should not exceed self.total_epochs in total)
                        n_rest = (i + 1) * jobs - self.total_epochs
                        current_jobs = jobs - n_rest if n_rest > 0 else jobs

                        asked = self.opt.ask(n_points=current_jobs)
                        f_val = self.run_optimizer_parallel(parallel, asked, i)
                        self.opt.tell(asked, [v['loss'] for v in f_val])

                        # Calculate progressbar outputs
                        for j, val in enumerate(f_val):
                            # Use human-friendly indexes here (starting from 1)
                            current = i * jobs + j + 1
                            val['current_epoch'] = current
                            val['is_initial_point'] = current <= INITIAL_POINTS

                            logger.debug(f"Optimizer epoch evaluated: {val}")

                            is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
                            # This value is assigned here and not in the optimization method
                            # to keep proper order in the list of results. That's because
                            # evaluations can take different time. Here they are aligned in the
                            # order they will be shown to the user.
                            val['is_best'] = is_best
                            self.print_results(val)

                            if is_best:
                                self.current_best_loss = val['loss']
                                self.current_best_epoch = val

                            self._save_result(val)

                            pbar.update(current)

        except KeyboardInterrupt:
            print('User interrupted..')

        logger.info(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
                    f"saved to '{self.results_file}'.")

        if self.current_best_epoch:
            if self.auto_hyperopt:
                HyperoptTools.try_export_params(
                    self.config,
                    self.backtesting.strategy.get_strategy_name(),
                    self.current_best_epoch)

            HyperoptTools.show_epoch_details(self.current_best_epoch, self.total_epochs,
                                             self.print_json)
        else:
            # This is printed when Ctrl+C is pressed quickly, before first epochs have
            # a chance to be evaluated.
            print("No epochs evaluated yet, no best result.")