import io
import logging
from copy import deepcopy
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple

import numpy as np
import rapidjson
import tabulate
from colorama import Fore, Style
from pandas import isna, json_normalize

from freqtrade.constants import FTHYPT_FILEVERSION, USERPATH_STRATEGIES
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 HyperoptTools():

    @staticmethod
    def get_strategy_filename(config: Dict, 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)
        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}")
        rapidjson.dump(final_params, filename.open('w'), indent=2,
                       default=hyperopt_serializer,
                       number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN
                       )

    @staticmethod
    def try_export_params(config: Dict[str, Any], 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: Dict[str, Any], 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_pickle(results_file: Path) -> List:
        """
        Read hyperopt results from pickle file
        LEGACY method - new files are written as json and cannot be read with this method.
        """
        from joblib import load

        logger.info(f"Reading pickled epochs from '{results_file}'")
        data = load(results_file)
        return data

    @staticmethod
    def _read_results(results_file: Path) -> List:
        """
        Read hyperopt results from file
        """
        import rapidjson
        logger.info(f"Reading epochs from '{results_file}'")
        with results_file.open('r') as f:
            data = [rapidjson.loads(line) for line in f]
        return data

    @staticmethod
    def load_previous_results(results_file: Path) -> List:
        """
        Load data for epochs from the file if we have one
        """
        epochs: List = []
        if results_file.is_file() and results_file.stat().st_size > 0:
            if results_file.suffix == '.pickle':
                epochs = HyperoptTools._read_results_pickle(results_file)
            else:
                epochs = HyperoptTools._read_results(results_file)
            # Detection of some old format, without 'is_best' field saved
            if epochs[0].get('is_best') is None:
                raise OperationalException(
                    "The file with HyperoptTools results is incompatible with this version "
                    "of Freqtrade and cannot be loaded.")
            logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
        return epochs

    @staticmethod
    def load_filtered_results(results_file: Path, config: Dict[str, Any]) -> 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', None),
            'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None),
            'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
            'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
            'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
            'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None),
            'filter_min_objective': config.get('hyperopt_list_min_objective', None),
            'filter_max_objective': config.get('hyperopt_list_max_objective', None),
        }

        # Previous evaluations
        epochs = HyperoptTools.load_previous_results(results_file)
        total_epochs = len(epochs)

        epochs = hyperopt_filter_epochs(epochs, filteroptions)

        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'] * 100: 6.2f}%. "
                f"Median profit {results_metrics['profit_median'] * 100: 6.2f}%. "
                f"Total profit {results_metrics['profit_total_abs']: 11.8f} {stake_currency} "
                f"({results_metrics['profit_total'] * 100: 7.2f}%). "
                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, legacy_mode: bool, has_drawdown: bool) -> str:

        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_abs'] = None
            trials['results_metrics.max_drawdown'] = None

        if not legacy_mode:
            # 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_abs',
                             'loss', 'is_initial_point', 'is_best']]

        else:
            # Legacy mode
            trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
                             'results_metrics.winsdrawslosses', 'results_metrics.avg_profit',
                             'results_metrics.total_profit', 'results_metrics.profit',
                             'results_metrics.duration', 'results_metrics.max_drawdown',
                             'results_metrics.max_drawdown_abs', 'loss', 'is_initial_point',
                             'is_best']]

        trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
                          'Total profit', 'Profit', 'Avg duration', 'Max Drawdown',
                          'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best']

        return trials

    @staticmethod
    def get_result_table(config: dict, 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)

        legacy_mode = 'results_metrics.total_trades' not in trials
        has_drawdown = 'results_metrics.max_drawdown_abs' in trials.columns

        trials = HyperoptTools.prepare_trials_columns(trials, legacy_mode, has_drawdown)

        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['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 * perc_multi:,.2f}%'.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']

        if has_drawdown:
            trials['Max Drawdown'] = trials.apply(
                lambda x: '{} {}'.format(
                    round_coin_value(x['max_drawdown_abs'], stake_currency),
                    '({:,.2f}%)'.format(x['Max Drawdown'] * perc_multi).rjust(10, ' ')
                ).rjust(25 + len(stake_currency))
                if x['Max Drawdown'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
                axis=1
            )
        else:
            trials = trials.drop(columns=['Max Drawdown'])

        trials = trials.drop(columns=['max_drawdown_abs'])

        trials['Profit'] = trials.apply(
            lambda x: '{} {}'.format(
                round_coin_value(x['Total profit'], stake_currency),
                '({:,.2f}%)'.format(x['Profit'] * perc_multi).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'])
        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: dict, results: list, total_epochs: int, highlight_best: bool,
                        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}")