freqtrade_origin/freqtrade/data/entryexitanalysis.py

import logging
from pathlib import Path

import joblib
import pandas as pd
from tabulate import tabulate

from freqtrade.configuration import TimeRange
from freqtrade.constants import Config
from freqtrade.data.btanalysis import (get_latest_backtest_filename, load_backtest_data,
                                       load_backtest_stats)
from freqtrade.exceptions import OperationalException


logger = logging.getLogger(__name__)


def _load_backtest_analysis_data(backtest_dir: Path, name: str):
    if backtest_dir.is_dir():
        scpf = Path(backtest_dir,
                    Path(get_latest_backtest_filename(backtest_dir)).stem + "_" + name + ".pkl"
                    )
    else:
        scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_{name}.pkl")

    try:
        with scpf.open("rb") as scp:
            loaded_data = joblib.load(scp)
            logger.info(f"Loaded {name} candles: {str(scpf)}")
    except Exception as e:
        logger.error(f"Cannot load {name} data from pickled results: ", e)
        return None

    return loaded_data


def _load_rejected_signals(backtest_dir: Path):
    return _load_backtest_analysis_data(backtest_dir, "rejected")


def _load_signal_candles(backtest_dir: Path):
    return _load_backtest_analysis_data(backtest_dir, "signals")


def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_candles):
    analysed_trades_dict = {}
    analysed_trades_dict[strategy_name] = {}

    try:
        logger.info(f"Processing {strategy_name} : {len(pairlist)} pairs")

        for pair in pairlist:
            if pair in signal_candles[strategy_name]:
                analysed_trades_dict[strategy_name][pair] = _analyze_candles_and_indicators(
                                                            pair,
                                                            trades,
                                                            signal_candles[strategy_name][pair])
    except Exception as e:
        print(f"Cannot process entry/exit reasons for {strategy_name}: ", e)

    return analysed_trades_dict


def _analyze_candles_and_indicators(pair, trades: pd.DataFrame, signal_candles: pd.DataFrame):
    buyf = signal_candles

    if len(buyf) > 0:
        buyf = buyf.set_index('date', drop=False)
        trades_red = trades.loc[trades['pair'] == pair].copy()

        trades_inds = pd.DataFrame()

        if trades_red.shape[0] > 0 and buyf.shape[0] > 0:
            for t, v in trades_red.open_date.items():
                allinds = buyf.loc[(buyf['date'] < v)]
                if allinds.shape[0] > 0:
                    tmp_inds = allinds.iloc[[-1]]

                    trades_red.loc[t, 'signal_date'] = tmp_inds['date'].values[0]
                    trades_red.loc[t, 'enter_reason'] = trades_red.loc[t, 'enter_tag']
                    tmp_inds.index.rename('signal_date', inplace=True)
                    trades_inds = pd.concat([trades_inds, tmp_inds])

            if 'signal_date' in trades_red:
                trades_red['signal_date'] = pd.to_datetime(trades_red['signal_date'], utc=True)
                trades_red.set_index('signal_date', inplace=True)

                try:
                    trades_red = pd.merge(trades_red, trades_inds, on='signal_date', how='outer')
                except Exception as e:
                    raise e
        return trades_red
    else:
        return pd.DataFrame()


def _do_group_table_output(bigdf, glist, to_csv=False, csv_path=None):
    for g in glist:
        # 0: summary wins/losses grouped by enter tag
        if g == "0":
            group_mask = ['enter_reason']
            wins = bigdf.loc[bigdf['profit_abs'] >= 0] \
                        .groupby(group_mask) \
                        .agg({'profit_abs': ['sum']})

            wins.columns = ['profit_abs_wins']
            loss = bigdf.loc[bigdf['profit_abs'] < 0] \
                        .groupby(group_mask) \
                        .agg({'profit_abs': ['sum']})
            loss.columns = ['profit_abs_loss']

            new = bigdf.groupby(group_mask).agg({'profit_abs': [
                                                    'count',
                                                    lambda x: sum(x > 0),
                                                    lambda x: sum(x <= 0)]})
            new = pd.concat([new, wins, loss], axis=1).fillna(0)

            new['profit_tot'] = new['profit_abs_wins'] - abs(new['profit_abs_loss'])
            new['wl_ratio_pct'] = (new.iloc[:, 1] / new.iloc[:, 0] * 100).fillna(0)
            new['avg_win'] = (new['profit_abs_wins'] / new.iloc[:, 1]).fillna(0)
            new['avg_loss'] = (new['profit_abs_loss'] / new.iloc[:, 2]).fillna(0)

            new.columns = ['total_num_buys', 'wins', 'losses', 'profit_abs_wins', 'profit_abs_loss',
                           'profit_tot', 'wl_ratio_pct', 'avg_win', 'avg_loss']

            sortcols = ['total_num_buys']

            _print_table(new, sortcols, show_index=True, name="Group 0:",
                         to_csv=to_csv, csv_path=csv_path)

        else:
            agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
                        'profit_ratio': ['median', 'mean', 'sum']}
            agg_cols = ['num_buys', 'profit_abs_sum', 'profit_abs_median',
                        'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct',
                        'total_profit_pct']
            sortcols = ['profit_abs_sum', 'enter_reason']

            # 1: profit summaries grouped by enter_tag
            if g == "1":
                group_mask = ['enter_reason']

            # 2: profit summaries grouped by enter_tag and exit_tag
            if g == "2":
                group_mask = ['enter_reason', 'exit_reason']

            # 3: profit summaries grouped by pair and enter_tag
            if g == "3":
                group_mask = ['pair', 'enter_reason']

            # 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
            if g == "4":
                group_mask = ['pair', 'enter_reason', 'exit_reason']

            # 5: profit summaries grouped by exit_tag
            if g == "5":
                group_mask = ['exit_reason']
                sortcols = ['exit_reason']

            if group_mask:
                new = bigdf.groupby(group_mask).agg(agg_mask).reset_index()
                new.columns = group_mask + agg_cols
                new['median_profit_pct'] = new['median_profit_pct'] * 100
                new['mean_profit_pct'] = new['mean_profit_pct'] * 100
                new['total_profit_pct'] = new['total_profit_pct'] * 100

                _print_table(new, sortcols, name=f"Group {g}:",
                             to_csv=to_csv, csv_path=csv_path)
            else:
                logger.warning("Invalid group mask specified.")


def _do_rejected_signals_output(rejected_signals_df, to_csv=False, csv_path=None):
    cols = ['pair', 'date', 'enter_tag']
    sortcols = ['date', 'pair', 'enter_tag']
    _print_table(rejected_signals_df[cols],
                 sortcols,
                 show_index=False,
                 name="Rejected Signals:",
                 to_csv=to_csv,
                 csv_path=csv_path)


def _select_rows_within_dates(df, timerange=None, df_date_col: str = 'date'):
    if timerange:
        if timerange.starttype == 'date':
            df = df.loc[(df[df_date_col] >= timerange.startdt)]
        if timerange.stoptype == 'date':
            df = df.loc[(df[df_date_col] < timerange.stopdt)]
    return df


def _select_rows_by_tags(df, enter_reason_list, exit_reason_list):
    if enter_reason_list and "all" not in enter_reason_list:
        df = df.loc[(df['enter_reason'].isin(enter_reason_list))]

    if exit_reason_list and "all" not in exit_reason_list:
        df = df.loc[(df['exit_reason'].isin(exit_reason_list))]
    return df


def prepare_results(analysed_trades, stratname,
                    enter_reason_list, exit_reason_list,
                    timerange=None):
    res_df = pd.DataFrame()
    for pair, trades in analysed_trades[stratname].items():
        res_df = pd.concat([res_df, trades], ignore_index=True)

    res_df = _select_rows_within_dates(res_df, timerange)

    if res_df is not None and res_df.shape[0] > 0 and ('enter_reason' in res_df.columns):
        res_df = _select_rows_by_tags(res_df, enter_reason_list, exit_reason_list)

    return res_df


def print_results(res_df, analysis_groups, indicator_list,
                  rejected_signals=None, to_csv=False, csv_path=None):
    if res_df.shape[0] > 0:
        if analysis_groups:
            _do_group_table_output(res_df, analysis_groups, to_csv=to_csv, csv_path=csv_path)

        if rejected_signals is not None and not rejected_signals.empty:
            _do_rejected_signals_output(rejected_signals, to_csv=to_csv, csv_path=csv_path)

        # NB this can be large for big dataframes!
        if "all" in indicator_list:
            _print_table(res_df,
                         show_index=False,
                         name="Indicators:",
                         to_csv=to_csv,
                         csv_path=csv_path)
        elif indicator_list is not None and indicator_list:
            available_inds = []
            for ind in indicator_list:
                if ind in res_df:
                    available_inds.append(ind)
            ilist = ["pair", "enter_reason", "exit_reason"] + available_inds
            _print_table(res_df[ilist],
                         sortcols=['exit_reason'],
                         show_index=False,
                         name="Indicators:",
                         to_csv=to_csv,
                         csv_path=csv_path)
    else:
        print("\\No trades to show")


def _print_table(df, sortcols=None, show_index=False, name=None, to_csv=False, csv_path=None):
    if (sortcols is not None):
        data = df.sort_values(sortcols)
    else:
        data = df

    if to_csv:
        if csv_path is not None:
            safe_name = Path(csv_path,
                             name.lower().replace(" ", "_").replace(":", ""))
        else:
            safe_name = Path("user_data",
                             "backtest_results",
                             name.lower().replace(" ", "_").replace(":", ""))
        data.to_csv(f"{str(safe_name)}.csv")
    else:
        if name is not None:
            print(name)

        print(
            tabulate(
                data,
                headers='keys',
                tablefmt='psql',
                showindex=show_index
            )
        )


def process_entry_exit_reasons(config: Config):
    try:
        analysis_groups = config.get('analysis_groups', [])
        enter_reason_list = config.get('enter_reason_list', ["all"])
        exit_reason_list = config.get('exit_reason_list', ["all"])
        indicator_list = config.get('indicator_list', [])
        do_rejected = config.get('analysis_rejected', False)
        to_csv = config.get('analysis_to_csv', False)
        csv_path = config.get('analysis_csv_path', config['exportfilename'])

        timerange = TimeRange.parse_timerange(None if config.get(
            'timerange') is None else str(config.get('timerange')))

        backtest_stats = load_backtest_stats(config['exportfilename'])

        for strategy_name, results in backtest_stats['strategy'].items():
            trades = load_backtest_data(config['exportfilename'], strategy_name)

            if trades is not None and not trades.empty:
                signal_candles = _load_signal_candles(config['exportfilename'])

                rej_df = None
                if do_rejected:
                    rejected_signals_dict = _load_rejected_signals(config['exportfilename'])
                    rej_df = prepare_results(rejected_signals_dict, strategy_name,
                                             enter_reason_list, exit_reason_list,
                                             timerange=timerange)

                analysed_trades_dict = _process_candles_and_indicators(
                                        config['exchange']['pair_whitelist'], strategy_name,
                                        trades, signal_candles)

                res_df = prepare_results(analysed_trades_dict, strategy_name,
                                         enter_reason_list, exit_reason_list,
                                         timerange=timerange)

                print_results(res_df,
                              analysis_groups,
                              indicator_list,
                              rejected_signals=rej_df,
                              to_csv=to_csv,
                              csv_path=csv_path)

    except ValueError as e:
        raise OperationalException(e) from e