stable/freqtrade/optimize/optimize_reports.py

import logging
from copy import deepcopy
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, List, Union

from numpy import int64
from pandas import DataFrame, to_datetime
from tabulate import tabulate

from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT
from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change,
                                    calculate_max_drawdown)
from freqtrade.misc import decimals_per_coin, file_dump_joblib, file_dump_json, round_coin_value
from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename


logger = logging.getLogger(__name__)


def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> None:
    """
    Stores backtest results
    :param recordfilename: Path object, which can either be a filename or a directory.
        Filenames will be appended with a timestamp right before the suffix
        while for directories, <directory>/backtest-result-<datetime>.json will be used as filename
    :param stats: Dataframe containing the backtesting statistics
    """
    if recordfilename.is_dir():
        filename = (recordfilename /
                    f'backtest-result-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}.json')
    else:
        filename = Path.joinpath(
            recordfilename.parent,
            f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}'
        ).with_suffix(recordfilename.suffix)

    # Store metadata separately.
    file_dump_json(get_backtest_metadata_filename(filename), stats['metadata'])
    del stats['metadata']

    file_dump_json(filename, stats)

    latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
    file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})


def store_backtest_signal_candles(recordfilename: Path, candles: Dict[str, Dict]) -> Path:
    """
    Stores backtest trade signal candles
    :param recordfilename: Path object, which can either be a filename or a directory.
        Filenames will be appended with a timestamp right before the suffix
        while for directories, <directory>/backtest-result-<datetime>_signals.pkl will be used
        as filename
    :param stats: Dict containing the backtesting signal candles
    """
    if recordfilename.is_dir():
        filename = (recordfilename /
                    f'backtest-result-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}_signals.pkl')
    else:
        filename = Path.joinpath(
            recordfilename.parent,
            f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}_signals.pkl'
        )

    file_dump_joblib(filename, candles)

    return filename


def _get_line_floatfmt(stake_currency: str) -> List[str]:
    """
    Generate floatformat (goes in line with _generate_result_line())
    """
    return ['s', 'd', '.2f', '.2f', f'.{decimals_per_coin(stake_currency)}f',
            '.2f', 'd', 's', 's']


def _get_line_header(first_column: str, stake_currency: str, direction: str = 'Buys') -> List[str]:
    """
    Generate header lines (goes in line with _generate_result_line())
    """
    return [first_column, direction, 'Avg Profit %', 'Cum Profit %',
            f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
            'Win  Draw  Loss  Win%']


def _generate_wins_draws_losses(wins, draws, losses):
    if wins > 0 and losses == 0:
        wl_ratio = '100'
    elif wins == 0:
        wl_ratio = '0'
    else:
        wl_ratio = f'{100.0 / (wins + draws + losses) * wins:.1f}' if losses > 0 else '100'
    return f'{wins:>4}  {draws:>4}  {losses:>4}  {wl_ratio:>4}'


def _generate_result_line(result: DataFrame, starting_balance: int, first_column: str) -> Dict:
    """
    Generate one result dict, with "first_column" as key.
    """
    profit_sum = result['profit_ratio'].sum()
    # (end-capital - starting capital) / starting capital
    profit_total = result['profit_abs'].sum() / starting_balance

    return {
        'key': first_column,
        'trades': len(result),
        'profit_mean': result['profit_ratio'].mean() if len(result) > 0 else 0.0,
        'profit_mean_pct': result['profit_ratio'].mean() * 100.0 if len(result) > 0 else 0.0,
        'profit_sum': profit_sum,
        'profit_sum_pct': round(profit_sum * 100.0, 2),
        'profit_total_abs': result['profit_abs'].sum(),
        'profit_total': profit_total,
        'profit_total_pct': round(profit_total * 100.0, 2),
        'duration_avg': str(timedelta(
                            minutes=round(result['trade_duration'].mean()))
                            ) if not result.empty else '0:00',
        # 'duration_max': str(timedelta(
        #                     minutes=round(result['trade_duration'].max()))
        #                     ) if not result.empty else '0:00',
        # 'duration_min': str(timedelta(
        #                     minutes=round(result['trade_duration'].min()))
        #                     ) if not result.empty else '0:00',
        'wins': len(result[result['profit_abs'] > 0]),
        'draws': len(result[result['profit_abs'] == 0]),
        'losses': len(result[result['profit_abs'] < 0]),
    }


def generate_pair_metrics(pairlist: List[str], stake_currency: str, starting_balance: int,
                          results: DataFrame, skip_nan: bool = False) -> List[Dict]:
    """
    Generates and returns a list  for the given backtest data and the results dataframe
    :param pairlist: Pairlist used
    :param stake_currency: stake-currency - used to correctly name headers
    :param starting_balance: Starting balance
    :param results: Dataframe containing the backtest results
    :param skip_nan: Print "left open" open trades
    :return: List of Dicts containing the metrics per pair
    """

    tabular_data = []

    for pair in pairlist:
        result = results[results['pair'] == pair]
        if skip_nan and result['profit_abs'].isnull().all():
            continue

        tabular_data.append(_generate_result_line(result, starting_balance, pair))

    # Sort by total profit %:
    tabular_data = sorted(tabular_data, key=lambda k: k['profit_total_abs'], reverse=True)

    # Append Total
    tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL'))
    return tabular_data


def generate_tag_metrics(tag_type: str,
                         starting_balance: int,
                         results: DataFrame,
                         skip_nan: bool = False) -> List[Dict]:
    """
    Generates and returns a list of metrics for the given tag trades and the results dataframe
    :param starting_balance: Starting balance
    :param results: Dataframe containing the backtest results
    :param skip_nan: Print "left open" open trades
    :return: List of Dicts containing the metrics per pair
    """

    tabular_data = []

    if tag_type in results.columns:
        for tag, count in results[tag_type].value_counts().iteritems():
            result = results[results[tag_type] == tag]
            if skip_nan and result['profit_abs'].isnull().all():
                continue

            tabular_data.append(_generate_result_line(result, starting_balance, tag))

        # Sort by total profit %:
        tabular_data = sorted(tabular_data, key=lambda k: k['profit_total_abs'], reverse=True)

        # Append Total
        tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL'))
        return tabular_data
    else:
        return []


def generate_exit_reason_stats(max_open_trades: int, results: DataFrame) -> List[Dict]:
    """
    Generate small table outlining Backtest results
    :param max_open_trades: Max_open_trades parameter
    :param results: Dataframe containing the backtest result for one strategy
    :return: List of Dicts containing the metrics per Sell reason
    """
    tabular_data = []

    for reason, count in results['exit_reason'].value_counts().iteritems():
        result = results.loc[results['exit_reason'] == reason]

        profit_mean = result['profit_ratio'].mean()
        profit_sum = result['profit_ratio'].sum()
        profit_total = profit_sum / max_open_trades

        tabular_data.append(
            {
                'exit_reason': reason,
                'trades': count,
                'wins': len(result[result['profit_abs'] > 0]),
                'draws': len(result[result['profit_abs'] == 0]),
                'losses': len(result[result['profit_abs'] < 0]),
                'profit_mean': profit_mean,
                'profit_mean_pct': round(profit_mean * 100, 2),
                'profit_sum': profit_sum,
                'profit_sum_pct': round(profit_sum * 100, 2),
                'profit_total_abs': result['profit_abs'].sum(),
                'profit_total': profit_total,
                'profit_total_pct': round(profit_total * 100, 2),
            }
        )
    return tabular_data


def generate_strategy_comparison(bt_stats: Dict) -> List[Dict]:
    """
    Generate summary per strategy
    :param bt_stats: Dict of <Strategyname: DataFrame> containing results for all strategies
    :return: List of Dicts containing the metrics per Strategy
    """

    tabular_data = []
    for strategy, result in bt_stats.items():
        tabular_data.append(deepcopy(result['results_per_pair'][-1]))
        # Update "key" to strategy (results_per_pair has it as "Total").
        tabular_data[-1]['key'] = strategy
        tabular_data[-1]['max_drawdown_account'] = result['max_drawdown_account']
        tabular_data[-1]['max_drawdown_abs'] = round_coin_value(
            result['max_drawdown_abs'], result['stake_currency'], False)
    return tabular_data


def generate_edge_table(results: dict) -> str:
    floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', 'd', 'd')
    tabular_data = []
    headers = ['Pair', 'Stoploss', 'Win Rate', 'Risk Reward Ratio',
               'Required Risk Reward', 'Expectancy', 'Total Number of Trades',
               'Average Duration (min)']

    for result in results.items():
        if result[1].nb_trades > 0:
            tabular_data.append([
                result[0],
                result[1].stoploss,
                result[1].winrate,
                result[1].risk_reward_ratio,
                result[1].required_risk_reward,
                result[1].expectancy,
                result[1].nb_trades,
                round(result[1].avg_trade_duration)
            ])

    # Ignore type as floatfmt does allow tuples but mypy does not know that
    return tabulate(tabular_data, headers=headers,
                    floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")


def _get_resample_from_period(period: str) -> str:
    if period == 'day':
        return '1d'
    if period == 'week':
        return '1w'
    if period == 'month':
        return '1M'
    raise ValueError(f"Period {period} is not supported.")


def generate_periodic_breakdown_stats(trade_list: List, period: str) -> List[Dict[str, Any]]:
    results = DataFrame.from_records(trade_list)
    if len(results) == 0:
        return []
    results['close_date'] = to_datetime(results['close_date'], utc=True)
    resample_period = _get_resample_from_period(period)
    resampled = results.resample(resample_period, on='close_date')
    stats = []
    for name, day in resampled:
        profit_abs = day['profit_abs'].sum().round(10)
        wins = sum(day['profit_abs'] > 0)
        draws = sum(day['profit_abs'] == 0)
        loses = sum(day['profit_abs'] < 0)
        stats.append(
            {
                'date': name.strftime('%d/%m/%Y'),
                'profit_abs': profit_abs,
                'wins': wins,
                'draws': draws,
                'loses': loses
            }
        )
    return stats


def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
    """ Generate overall trade statistics """
    if len(results) == 0:
        return {
            'wins': 0,
            'losses': 0,
            'draws': 0,
            'holding_avg': timedelta(),
            'winner_holding_avg': timedelta(),
            'loser_holding_avg': timedelta(),
        }

    winning_trades = results.loc[results['profit_ratio'] > 0]
    draw_trades = results.loc[results['profit_ratio'] == 0]
    losing_trades = results.loc[results['profit_ratio'] < 0]

    holding_avg = (timedelta(minutes=round(results['trade_duration'].mean()))
                   if not results.empty else timedelta())
    winner_holding_avg = (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
                          if not winning_trades.empty else timedelta())
    loser_holding_avg = (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
                         if not losing_trades.empty else timedelta())

    return {
        'wins': len(winning_trades),
        'losses': len(losing_trades),
        'draws': len(draw_trades),
        'holding_avg': holding_avg,
        'holding_avg_s': holding_avg.total_seconds(),
        'winner_holding_avg': winner_holding_avg,
        'winner_holding_avg_s': winner_holding_avg.total_seconds(),
        'loser_holding_avg': loser_holding_avg,
        'loser_holding_avg_s': loser_holding_avg.total_seconds(),
    }


def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
    """ Generate daily statistics """
    if len(results) == 0:
        return {
            'backtest_best_day': 0,
            'backtest_worst_day': 0,
            'backtest_best_day_abs': 0,
            'backtest_worst_day_abs': 0,
            'winning_days': 0,
            'draw_days': 0,
            'losing_days': 0,
            'daily_profit_list': [],
        }
    daily_profit_rel = results.resample('1d', on='close_date')['profit_ratio'].sum()
    daily_profit = results.resample('1d', on='close_date')['profit_abs'].sum().round(10)
    worst_rel = min(daily_profit_rel)
    best_rel = max(daily_profit_rel)
    worst = min(daily_profit)
    best = max(daily_profit)
    winning_days = sum(daily_profit > 0)
    draw_days = sum(daily_profit == 0)
    losing_days = sum(daily_profit < 0)
    daily_profit_list = [(str(idx.date()), val) for idx, val in daily_profit.iteritems()]

    return {
        'backtest_best_day': best_rel,
        'backtest_worst_day': worst_rel,
        'backtest_best_day_abs': best,
        'backtest_worst_day_abs': worst,
        'winning_days': winning_days,
        'draw_days': draw_days,
        'losing_days': losing_days,
        'daily_profit': daily_profit_list,
    }


def generate_strategy_stats(pairlist: List[str],
                            strategy: str,
                            content: Dict[str, Any],
                            min_date: datetime, max_date: datetime,
                            market_change: float
                            ) -> Dict[str, Any]:
    """
    :param pairlist: List of pairs to backtest
    :param strategy: Strategy name
    :param content: Backtest result data in the format:
                    {'results: results, 'config: config}}.
    :param min_date: Backtest start date
    :param max_date: Backtest end date
    :param market_change: float indicating the market change
    :return: Dictionary containing results per strategy and a strategy summary.
    """
    results: Dict[str, DataFrame] = content['results']
    if not isinstance(results, DataFrame):
        return {}
    config = content['config']
    max_open_trades = min(config['max_open_trades'], len(pairlist))
    start_balance = config['dry_run_wallet']
    stake_currency = config['stake_currency']

    pair_results = generate_pair_metrics(pairlist, stake_currency=stake_currency,
                                         starting_balance=start_balance,
                                         results=results, skip_nan=False)

    enter_tag_results = generate_tag_metrics("enter_tag", starting_balance=start_balance,
                                             results=results, skip_nan=False)

    exit_reason_stats = generate_exit_reason_stats(max_open_trades=max_open_trades,
                                                   results=results)
    left_open_results = generate_pair_metrics(pairlist, stake_currency=stake_currency,
                                              starting_balance=start_balance,
                                              results=results.loc[results['is_open']],
                                              skip_nan=True)
    daily_stats = generate_daily_stats(results)
    trade_stats = generate_trading_stats(results)
    best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],
                    key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
    worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'],
                     key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
    if not results.empty:
        results['open_timestamp'] = results['open_date'].view(int64) // 1e6
        results['close_timestamp'] = results['close_date'].view(int64) // 1e6

    backtest_days = (max_date - min_date).days or 1
    strat_stats = {
        'trades': results.to_dict(orient='records'),
        'locks': [lock.to_json() for lock in content['locks']],
        'best_pair': best_pair,
        'worst_pair': worst_pair,
        'results_per_pair': pair_results,
        'results_per_enter_tag': enter_tag_results,
        'exit_reason_summary': exit_reason_stats,
        'left_open_trades': left_open_results,
        # 'days_breakdown_stats': days_breakdown_stats,

        'total_trades': len(results),
        'trade_count_long': len(results.loc[~results['is_short']]),
        'trade_count_short': len(results.loc[results['is_short']]),
        'total_volume': float(results['stake_amount'].sum()),
        'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
        'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
        'profit_median': results['profit_ratio'].median() if len(results) > 0 else 0,
        'profit_total': results['profit_abs'].sum() / start_balance,
        'profit_total_long': results.loc[~results['is_short'], 'profit_abs'].sum() / start_balance,
        'profit_total_short': results.loc[results['is_short'], 'profit_abs'].sum() / start_balance,
        'profit_total_abs': results['profit_abs'].sum(),
        'profit_total_long_abs': results.loc[~results['is_short'], 'profit_abs'].sum(),
        'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(),
        'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']),
        'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
        'backtest_start_ts': int(min_date.timestamp() * 1000),
        'backtest_end': max_date.strftime(DATETIME_PRINT_FORMAT),
        'backtest_end_ts': int(max_date.timestamp() * 1000),
        'backtest_days': backtest_days,

        'backtest_run_start_ts': content['backtest_start_time'],
        'backtest_run_end_ts': content['backtest_end_time'],

        'trades_per_day': round(len(results) / backtest_days, 2),
        'market_change': market_change,
        'pairlist': pairlist,
        'stake_amount': config['stake_amount'],
        'stake_currency': config['stake_currency'],
        'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
        'starting_balance': start_balance,
        'dry_run_wallet': start_balance,
        'final_balance': content['final_balance'],
        'rejected_signals': content['rejected_signals'],
        'timedout_entry_orders': content['timedout_entry_orders'],
        'timedout_exit_orders': content['timedout_exit_orders'],
        'canceled_trade_entries': content['canceled_trade_entries'],
        'canceled_entry_orders': content['canceled_entry_orders'],
        'replaced_entry_orders': content['replaced_entry_orders'],
        'max_open_trades': max_open_trades,
        'max_open_trades_setting': (config['max_open_trades']
                                    if config['max_open_trades'] != float('inf') else -1),
        'timeframe': config['timeframe'],
        'timeframe_detail': config.get('timeframe_detail', ''),
        'timerange': config.get('timerange', ''),
        'enable_protections': config.get('enable_protections', False),
        'strategy_name': strategy,
        # Parameters relevant for backtesting
        'stoploss': config['stoploss'],
        'trailing_stop': config.get('trailing_stop', False),
        'trailing_stop_positive': config.get('trailing_stop_positive'),
        'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset', 0.0),
        'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False),
        'use_custom_stoploss': config.get('use_custom_stoploss', False),
        'minimal_roi': config['minimal_roi'],
        'use_exit_signal': config['use_exit_signal'],
        'exit_profit_only': config['exit_profit_only'],
        'exit_profit_offset': config['exit_profit_offset'],
        'ignore_roi_if_entry_signal': config['ignore_roi_if_entry_signal'],
        **daily_stats,
        **trade_stats
    }

    try:
        max_drawdown_legacy, _, _, _, _, _ = calculate_max_drawdown(
            results, value_col='profit_ratio')
        (drawdown_abs, drawdown_start, drawdown_end, high_val, low_val,
         max_drawdown) = calculate_max_drawdown(
             results, value_col='profit_abs', starting_balance=start_balance)
        (_, _, _, _, _, max_relative_drawdown) = calculate_max_drawdown(
             results, value_col='profit_abs', starting_balance=start_balance, relative=True)
        strat_stats.update({
            'max_drawdown': max_drawdown_legacy,  # Deprecated - do not use
            'max_drawdown_account': max_drawdown,
            'max_relative_drawdown': max_relative_drawdown,
            'max_drawdown_abs': drawdown_abs,
            'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT),
            'drawdown_start_ts': drawdown_start.timestamp() * 1000,
            'drawdown_end': drawdown_end.strftime(DATETIME_PRINT_FORMAT),
            'drawdown_end_ts': drawdown_end.timestamp() * 1000,

            'max_drawdown_low': low_val,
            'max_drawdown_high': high_val,
        })

        csum_min, csum_max = calculate_csum(results, start_balance)
        strat_stats.update({
            'csum_min': csum_min,
            'csum_max': csum_max
        })

    except ValueError:
        strat_stats.update({
            'max_drawdown': 0.0,
            'max_drawdown_account': 0.0,
            'max_relative_drawdown': 0.0,
            'max_drawdown_abs': 0.0,
            'max_drawdown_low': 0.0,
            'max_drawdown_high': 0.0,
            'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc),
            'drawdown_start_ts': 0,
            'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
            'drawdown_end_ts': 0,
            'csum_min': 0,
            'csum_max': 0
        })

    return strat_stats


def generate_backtest_stats(btdata: Dict[str, DataFrame],
                            all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
                            min_date: datetime, max_date: datetime
                            ) -> Dict[str, Any]:
    """
    :param btdata: Backtest data
    :param all_results: backtest result - dictionary in the form:
                     { Strategy: {'results: results, 'config: config}}.
    :param min_date: Backtest start date
    :param max_date: Backtest end date
    :return: Dictionary containing results per strategy and a strategy summary.
    """
    result: Dict[str, Any] = {
        'metadata': {},
        'strategy': {},
        'strategy_comparison': [],
    }
    market_change = calculate_market_change(btdata, 'close')
    metadata = {}
    pairlist = list(btdata.keys())
    for strategy, content in all_results.items():
        strat_stats = generate_strategy_stats(pairlist, strategy, content,
                                              min_date, max_date, market_change=market_change)
        metadata[strategy] = {
            'run_id': content['run_id'],
            'backtest_start_time': content['backtest_start_time'],
        }
        result['strategy'][strategy] = strat_stats

    strategy_results = generate_strategy_comparison(bt_stats=result['strategy'])

    result['metadata'] = metadata
    result['strategy_comparison'] = strategy_results

    return result


###
# Start output section
###

def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: str) -> str:
    """
    Generates and returns a text table for the given backtest data and the results dataframe
    :param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
    :param stake_currency: stake-currency - used to correctly name headers
    :return: pretty printed table with tabulate as string
    """

    headers = _get_line_header('Pair', stake_currency)
    floatfmt = _get_line_floatfmt(stake_currency)
    output = [[
        t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
        t['profit_total_pct'], t['duration_avg'],
        _generate_wins_draws_losses(t['wins'], t['draws'], t['losses'])
    ] for t in pair_results]
    # Ignore type as floatfmt does allow tuples but mypy does not know that
    return tabulate(output, headers=headers,
                    floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")


def text_table_exit_reason(exit_reason_stats: List[Dict[str, Any]], stake_currency: str) -> str:
    """
    Generate small table outlining Backtest results
    :param sell_reason_stats: Exit reason metrics
    :param stake_currency: Stakecurrency used
    :return: pretty printed table with tabulate as string
    """
    headers = [
        'Exit Reason',
        'Exits',
        'Win  Draws  Loss  Win%',
        'Avg Profit %',
        'Cum Profit %',
        f'Tot Profit {stake_currency}',
        'Tot Profit %',
    ]

    output = [[
        t.get('exit_reason', t.get('sell_reason')), t['trades'],
        _generate_wins_draws_losses(t['wins'], t['draws'], t['losses']),
        t['profit_mean_pct'], t['profit_sum_pct'],
        round_coin_value(t['profit_total_abs'], stake_currency, False),
        t['profit_total_pct'],
    ] for t in exit_reason_stats]
    return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")


def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_currency: str) -> str:
    """
    Generates and returns a text table for the given backtest data and the results dataframe
    :param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
    :param stake_currency: stake-currency - used to correctly name headers
    :return: pretty printed table with tabulate as string
    """
    if(tag_type == "enter_tag"):
        headers = _get_line_header("TAG", stake_currency)
    else:
        headers = _get_line_header("TAG", stake_currency, 'Sells')
    floatfmt = _get_line_floatfmt(stake_currency)
    output = [
        [
            t['key'] if t['key'] is not None and len(
                t['key']) > 0 else "OTHER",
            t['trades'],
            t['profit_mean_pct'],
            t['profit_sum_pct'],
            t['profit_total_abs'],
            t['profit_total_pct'],
            t['duration_avg'],
            _generate_wins_draws_losses(
                t['wins'],
                t['draws'],
                t['losses'])] for t in tag_results]
    # Ignore type as floatfmt does allow tuples but mypy does not know that
    return tabulate(output, headers=headers,
                    floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")


def text_table_periodic_breakdown(days_breakdown_stats: List[Dict[str, Any]],
                                  stake_currency: str, period: str) -> str:
    """
    Generate small table with Backtest results by days
    :param days_breakdown_stats: Days breakdown metrics
    :param stake_currency: Stakecurrency used
    :return: pretty printed table with tabulate as string
    """
    headers = [
        period.capitalize(),
        f'Tot Profit {stake_currency}',
        'Wins',
        'Draws',
        'Losses',
    ]
    output = [[
        d['date'], round_coin_value(d['profit_abs'], stake_currency, False),
        d['wins'], d['draws'], d['loses'],
    ] for d in days_breakdown_stats]
    return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")


def text_table_strategy(strategy_results, stake_currency: str) -> str:
    """
    Generate summary table per strategy
    :param strategy_results: Dict of <Strategyname: DataFrame> containing results for all strategies
    :param stake_currency: stake-currency - used to correctly name headers
    :return: pretty printed table with tabulate as string
    """
    floatfmt = _get_line_floatfmt(stake_currency)
    headers = _get_line_header('Strategy', stake_currency)
    # _get_line_header() is also used for per-pair summary. Per-pair drawdown is mostly useless
    # therefore we slip this column in only for strategy summary here.
    headers.append('Drawdown')

    # Align drawdown string on the center two space separator.
    if 'max_drawdown_account' in strategy_results[0]:
        drawdown = [f'{t["max_drawdown_account"] * 100:.2f}' for t in strategy_results]
    else:
        # Support for prior backtest results
        drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results]

    dd_pad_abs = max([len(t['max_drawdown_abs']) for t in strategy_results])
    dd_pad_per = max([len(dd) for dd in drawdown])
    drawdown = [f'{t["max_drawdown_abs"]:>{dd_pad_abs}} {stake_currency}  {dd:>{dd_pad_per}}%'
                for t, dd in zip(strategy_results, drawdown)]

    output = [[
        t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
        t['profit_total_pct'], t['duration_avg'],
        _generate_wins_draws_losses(t['wins'], t['draws'], t['losses']), drawdown]
        for t, drawdown in zip(strategy_results, drawdown)]
    # Ignore type as floatfmt does allow tuples but mypy does not know that
    return tabulate(output, headers=headers,
                    floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")


def text_table_add_metrics(strat_results: Dict) -> str:
    if len(strat_results['trades']) > 0:
        best_trade = max(strat_results['trades'], key=lambda x: x['profit_ratio'])
        worst_trade = min(strat_results['trades'], key=lambda x: x['profit_ratio'])

        short_metrics = [
            ('', ''),  # Empty line to improve readability
            ('Long / Short',
             f"{strat_results.get('trade_count_long', 'total_trades')} / "
             f"{strat_results.get('trade_count_short', 0)}"),
            ('Total profit Long %', f"{strat_results['profit_total_long']:.2%}"),
            ('Total profit Short %', f"{strat_results['profit_total_short']:.2%}"),
            ('Absolute profit Long', round_coin_value(strat_results['profit_total_long_abs'],
                                                      strat_results['stake_currency'])),
            ('Absolute profit Short', round_coin_value(strat_results['profit_total_short_abs'],
                                                       strat_results['stake_currency'])),
        ] if strat_results.get('trade_count_short', 0) > 0 else []

        drawdown_metrics = []
        if 'max_relative_drawdown' in strat_results:
            # Compatibility to show old hyperopt results
            drawdown_metrics.append(
                ('Max % of account underwater', f"{strat_results['max_relative_drawdown']:.2%}")
            )
        drawdown_metrics.extend([
            ('Absolute Drawdown (Account)', f"{strat_results['max_drawdown_account']:.2%}")
            if 'max_drawdown_account' in strat_results else (
                'Drawdown', f"{strat_results['max_drawdown']:.2%}"),
            ('Absolute Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
                                                   strat_results['stake_currency'])),
            ('Drawdown high', round_coin_value(strat_results['max_drawdown_high'],
                                               strat_results['stake_currency'])),
            ('Drawdown low', round_coin_value(strat_results['max_drawdown_low'],
                                              strat_results['stake_currency'])),
            ('Drawdown Start', strat_results['drawdown_start']),
            ('Drawdown End', strat_results['drawdown_end']),
        ])

        entry_adjustment_metrics = [
            ('Canceled Trade Entries', strat_results.get('canceled_trade_entries', 'N/A')),
            ('Canceled Entry Orders', strat_results.get('canceled_entry_orders', 'N/A')),
            ('Replaced Entry Orders', strat_results.get('replaced_entry_orders', 'N/A')),
        ] if strat_results.get('canceled_entry_orders', 0) > 0 else []

        # Newly added fields should be ignored if they are missing in strat_results. hyperopt-show
        # command stores these results and newer version of freqtrade must be able to handle old
        # results with missing new fields.
        metrics = [
            ('Backtesting from', strat_results['backtest_start']),
            ('Backtesting to', strat_results['backtest_end']),
            ('Max open trades', strat_results['max_open_trades']),
            ('', ''),  # Empty line to improve readability
            ('Total/Daily Avg Trades',
                f"{strat_results['total_trades']} / {strat_results['trades_per_day']}"),

            ('Starting balance', round_coin_value(strat_results['starting_balance'],
                                                  strat_results['stake_currency'])),
            ('Final balance', round_coin_value(strat_results['final_balance'],
                                               strat_results['stake_currency'])),
            ('Absolute profit ', round_coin_value(strat_results['profit_total_abs'],
                                                  strat_results['stake_currency'])),
            ('Total profit %', f"{strat_results['profit_total']:.2%}"),
            ('CAGR %', f"{strat_results['cagr']:.2%}" if 'cagr' in strat_results else 'N/A'),
            ('Trades per day', strat_results['trades_per_day']),
            ('Avg. daily profit %',
             f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),
            ('Avg. stake amount', round_coin_value(strat_results['avg_stake_amount'],
                                                   strat_results['stake_currency'])),
            ('Total trade volume', round_coin_value(strat_results['total_volume'],
                                                    strat_results['stake_currency'])),
            *short_metrics,
            ('', ''),  # Empty line to improve readability
            ('Best Pair', f"{strat_results['best_pair']['key']} "
                          f"{strat_results['best_pair']['profit_sum']:.2%}"),
            ('Worst Pair', f"{strat_results['worst_pair']['key']} "
                           f"{strat_results['worst_pair']['profit_sum']:.2%}"),
            ('Best trade', f"{best_trade['pair']} {best_trade['profit_ratio']:.2%}"),
            ('Worst trade', f"{worst_trade['pair']} "
                            f"{worst_trade['profit_ratio']:.2%}"),

            ('Best day', round_coin_value(strat_results['backtest_best_day_abs'],
                                          strat_results['stake_currency'])),
            ('Worst day', round_coin_value(strat_results['backtest_worst_day_abs'],
                                           strat_results['stake_currency'])),
            ('Days win/draw/lose', f"{strat_results['winning_days']} / "
                f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
            ('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
            ('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
            ('Rejected Entry signals', strat_results.get('rejected_signals', 'N/A')),
            ('Entry/Exit Timeouts',
             f"{strat_results.get('timedout_entry_orders', 'N/A')} / "
             f"{strat_results.get('timedout_exit_orders', 'N/A')}"),
            *entry_adjustment_metrics,
            ('', ''),  # Empty line to improve readability

            ('Min balance', round_coin_value(strat_results['csum_min'],
                                             strat_results['stake_currency'])),
            ('Max balance', round_coin_value(strat_results['csum_max'],
                                             strat_results['stake_currency'])),

            *drawdown_metrics,
            ('Market change', f"{strat_results['market_change']:.2%}"),
        ]

        return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
    else:
        start_balance = round_coin_value(strat_results['starting_balance'],
                                         strat_results['stake_currency'])
        stake_amount = round_coin_value(
            strat_results['stake_amount'], strat_results['stake_currency']
        ) if strat_results['stake_amount'] != UNLIMITED_STAKE_AMOUNT else 'unlimited'

        message = ("No trades made. "
                   f"Your starting balance was {start_balance}, "
                   f"and your stake was {stake_amount}."
                   )
        return message


def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str,
                         backtest_breakdown=[]):
    """
    Print results for one strategy
    """
    # Print results
    print(f"Result for strategy {strategy}")
    table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency)
    if isinstance(table, str):
        print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
    print(table)

    if (results.get('results_per_enter_tag') is not None
            or results.get('results_per_buy_tag') is not None):
        # results_per_buy_tag is deprecated and should be removed 2 versions after short golive.
        table = text_table_tags(
            "enter_tag",
            results.get('results_per_enter_tag', results.get('results_per_buy_tag')),
            stake_currency=stake_currency)

        if isinstance(table, str) and len(table) > 0:
            print(' ENTER TAG STATS '.center(len(table.splitlines()[0]), '='))
        print(table)

    exit_reasons = results.get('exit_reason_summary', results.get('sell_reason_summary'))
    table = text_table_exit_reason(exit_reason_stats=exit_reasons,
                                   stake_currency=stake_currency)
    if isinstance(table, str) and len(table) > 0:
        print(' EXIT REASON STATS '.center(len(table.splitlines()[0]), '='))
    print(table)

    table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
    if isinstance(table, str) and len(table) > 0:
        print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
    print(table)

    for period in backtest_breakdown:
        days_breakdown_stats = generate_periodic_breakdown_stats(
            trade_list=results['trades'], period=period)
        table = text_table_periodic_breakdown(days_breakdown_stats=days_breakdown_stats,
                                              stake_currency=stake_currency, period=period)
        if isinstance(table, str) and len(table) > 0:
            print(f' {period.upper()} BREAKDOWN '.center(len(table.splitlines()[0]), '='))
        print(table)

    table = text_table_add_metrics(results)
    if isinstance(table, str) and len(table) > 0:
        print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
    print(table)

    if isinstance(table, str) and len(table) > 0:
        print('=' * len(table.splitlines()[0]))

    print()


def show_backtest_results(config: Dict, backtest_stats: Dict):
    stake_currency = config['stake_currency']

    for strategy, results in backtest_stats['strategy'].items():
        show_backtest_result(
            strategy, results, stake_currency,
            config.get('backtest_breakdown', []))

    if len(backtest_stats['strategy']) > 1:
        # Print Strategy summary table

        table = text_table_strategy(backtest_stats['strategy_comparison'], stake_currency)
        print(f"{results['backtest_start']} -> {results['backtest_end']} |"
              f" Max open trades : {results['max_open_trades']}")
        print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '='))
        print(table)
        print('=' * len(table.splitlines()[0]))
        print('\nFor more details, please look at the detail tables above')


def show_sorted_pairlist(config: Dict, backtest_stats: Dict):
    if config.get('backtest_show_pair_list', False):
        for strategy, results in backtest_stats['strategy'].items():
            print(f"Pairs for Strategy {strategy}: \n[")
            for result in results['results_per_pair']:
                if result["key"] != 'TOTAL':
                    print(f'"{result["key"]}",  // {result["profit_mean"]:.2%}')
            print("]")