freqtrade_origin/freqtrade/data/entryexitanalysis.py

import logging
from pathlib import Path
from typing import Dict, List

import joblib
import pandas as pd

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


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 _load_exit_signal_candles(backtest_dir: Path) -> Dict[str, Dict[str, pd.DataFrame]]:
    return _load_backtest_analysis_data(backtest_dir, "exited")


def _process_candles_and_indicators(
    pairlist, strategy_name, trades, signal_candles, date_col: str = "open_date"
):
    analysed_trades_dict: Dict[str, 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], date_col
                )
    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: str, trades: pd.DataFrame, signal_candles: pd.DataFrame, date_col: str = "open_date"
) -> 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.iterrows():
                allinds = buyf.loc[(buyf["date"] < v[date_col])]
                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,
    csv_path: Path,
    to_csv=False,
):
    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["exp_ratio"] = (
                ((1 + (new["avg_win"] / abs(new["avg_loss"]))) * (new["wl_ratio_pct"] / 100)) - 1
            ).fillna(0)

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

            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: pd.DataFrame, to_csv: bool = False, csv_path=None
) -> 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
) -> pd.DataFrame:
    res_df = pd.DataFrame()
    for pair, trades in analysed_trades[stratname].items():
        if trades.shape[0] > 0:
            trades.dropna(subset=["close_date"], inplace=True)
            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: pd.DataFrame,
    exit_df: pd.DataFrame,
    analysis_groups: List[str],
    indicator_list: List[str],
    csv_path: Path,
    rejected_signals=None,
    to_csv=False,
):
    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:
            if rejected_signals.empty:
                print("There were no rejected signals.")
            else:
                _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)

            merged_df = _merge_dfs(res_df, exit_df, available_inds)

            _print_table(
                merged_df,
                sortcols=["exit_reason"],
                show_index=False,
                name="Indicators:",
                to_csv=to_csv,
                csv_path=csv_path,
            )
    else:
        print("\\No trades to show")


def _merge_dfs(entry_df, exit_df, available_inds):
    merge_on = ["pair", "open_date"]
    signal_wide_indicators = list(set(available_inds) - set(BT_DATA_COLUMNS))
    columns_to_keep = merge_on + ["enter_reason", "exit_reason"] + available_inds

    if exit_df is None or exit_df.empty:
        return entry_df[columns_to_keep]

    return pd.merge(
        entry_df[columns_to_keep],
        exit_df[merge_on + signal_wide_indicators],
        on=merge_on,
        suffixes=(" (entry)", " (exit)"),
    )


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

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

        print_df_rich_table(data, data.keys(), show_index=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 = Path(config.get("analysis_csv_path", config["exportfilename"]))
        if to_csv and not csv_path.is_dir():
            raise OperationalException(f"Specified directory {csv_path} does not exist.")

        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"])
                exit_signals = _load_exit_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,
                    )

                entry_df = _generate_dfs(
                    config["exchange"]["pair_whitelist"],
                    enter_reason_list,
                    exit_reason_list,
                    signal_candles,
                    strategy_name,
                    timerange,
                    trades,
                    "open_date",
                )

                exit_df = _generate_dfs(
                    config["exchange"]["pair_whitelist"],
                    enter_reason_list,
                    exit_reason_list,
                    exit_signals,
                    strategy_name,
                    timerange,
                    trades,
                    "close_date",
                )

                print_results(
                    entry_df,
                    exit_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


def _generate_dfs(
    pairlist: list,
    enter_reason_list: list,
    exit_reason_list: list,
    signal_candles: Dict,
    strategy_name: str,
    timerange: TimeRange,
    trades: pd.DataFrame,
    date_col: str,
) -> pd.DataFrame:
    analysed_trades_dict = _process_candles_and_indicators(
        pairlist,
        strategy_name,
        trades,
        signal_candles,
        date_col,
    )
    res_df = prepare_results(
        analysed_trades_dict,
        strategy_name,
        enter_reason_list,
        exit_reason_list,
        timerange=timerange,
    )
    return res_df