mirror of
https://github.com/freqtrade/freqtrade.git
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371 lines
13 KiB
Python
371 lines
13 KiB
Python
import logging
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from pathlib import Path
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from typing import List
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import joblib
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import pandas as pd
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from tabulate import tabulate
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from freqtrade.configuration import TimeRange
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from freqtrade.constants import Config
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from freqtrade.data.btanalysis import (
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get_latest_backtest_filename,
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load_backtest_data,
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load_backtest_stats,
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)
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from freqtrade.exceptions import OperationalException
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logger = logging.getLogger(__name__)
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def _load_backtest_analysis_data(backtest_dir: Path, name: str):
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if backtest_dir.is_dir():
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scpf = Path(
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backtest_dir,
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Path(get_latest_backtest_filename(backtest_dir)).stem + "_" + name + ".pkl",
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)
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else:
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scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_{name}.pkl")
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try:
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with scpf.open("rb") as scp:
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loaded_data = joblib.load(scp)
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logger.info(f"Loaded {name} candles: {str(scpf)}")
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except Exception as e:
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logger.error(f"Cannot load {name} data from pickled results: ", e)
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return None
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return loaded_data
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def _load_rejected_signals(backtest_dir: Path):
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return _load_backtest_analysis_data(backtest_dir, "rejected")
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def _load_signal_candles(backtest_dir: Path):
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return _load_backtest_analysis_data(backtest_dir, "signals")
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def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_candles):
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analysed_trades_dict = {}
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analysed_trades_dict[strategy_name] = {}
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try:
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logger.info(f"Processing {strategy_name} : {len(pairlist)} pairs")
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for pair in pairlist:
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if pair in signal_candles[strategy_name]:
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analysed_trades_dict[strategy_name][pair] = _analyze_candles_and_indicators(
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pair, trades, signal_candles[strategy_name][pair]
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)
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except Exception as e:
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print(f"Cannot process entry/exit reasons for {strategy_name}: ", e)
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return analysed_trades_dict
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def _analyze_candles_and_indicators(pair, trades: pd.DataFrame, signal_candles: pd.DataFrame):
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buyf = signal_candles
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if len(buyf) > 0:
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buyf = buyf.set_index("date", drop=False)
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trades_red = trades.loc[trades["pair"] == pair].copy()
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trades_inds = pd.DataFrame()
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if trades_red.shape[0] > 0 and buyf.shape[0] > 0:
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for t, v in trades_red.open_date.items():
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allinds = buyf.loc[(buyf["date"] < v)]
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if allinds.shape[0] > 0:
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tmp_inds = allinds.iloc[[-1]]
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trades_red.loc[t, "signal_date"] = tmp_inds["date"].values[0]
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trades_red.loc[t, "enter_reason"] = trades_red.loc[t, "enter_tag"]
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tmp_inds.index.rename("signal_date", inplace=True)
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trades_inds = pd.concat([trades_inds, tmp_inds])
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if "signal_date" in trades_red:
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trades_red["signal_date"] = pd.to_datetime(trades_red["signal_date"], utc=True)
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trades_red.set_index("signal_date", inplace=True)
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try:
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trades_red = pd.merge(trades_red, trades_inds, on="signal_date", how="outer")
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except Exception as e:
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raise e
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return trades_red
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else:
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return pd.DataFrame()
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def _do_group_table_output(
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bigdf,
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glist,
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csv_path: Path,
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to_csv=False,
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):
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for g in glist:
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# 0: summary wins/losses grouped by enter tag
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if g == "0":
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group_mask = ["enter_reason"]
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wins = (
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bigdf.loc[bigdf["profit_abs"] >= 0].groupby(group_mask).agg({"profit_abs": ["sum"]})
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)
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wins.columns = ["profit_abs_wins"]
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loss = (
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bigdf.loc[bigdf["profit_abs"] < 0].groupby(group_mask).agg({"profit_abs": ["sum"]})
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)
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loss.columns = ["profit_abs_loss"]
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new = bigdf.groupby(group_mask).agg(
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{"profit_abs": ["count", lambda x: sum(x > 0), lambda x: sum(x <= 0)]}
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)
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new = pd.concat([new, wins, loss], axis=1).fillna(0)
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new["profit_tot"] = new["profit_abs_wins"] - abs(new["profit_abs_loss"])
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new["wl_ratio_pct"] = (new.iloc[:, 1] / new.iloc[:, 0] * 100).fillna(0)
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new["avg_win"] = (new["profit_abs_wins"] / new.iloc[:, 1]).fillna(0)
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new["avg_loss"] = (new["profit_abs_loss"] / new.iloc[:, 2]).fillna(0)
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new["exp_ratio"] = (
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((1 + (new["avg_win"] / abs(new["avg_loss"]))) * (new["wl_ratio_pct"] / 100)) - 1
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).fillna(0)
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new.columns = [
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"total_num_buys",
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"wins",
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"losses",
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"profit_abs_wins",
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"profit_abs_loss",
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"profit_tot",
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"wl_ratio_pct",
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"avg_win",
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"avg_loss",
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"exp_ratio",
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]
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sortcols = ["total_num_buys"]
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_print_table(
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new, sortcols, show_index=True, name="Group 0:", to_csv=to_csv, csv_path=csv_path
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)
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else:
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agg_mask = {
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"profit_abs": ["count", "sum", "median", "mean"],
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"profit_ratio": ["median", "mean", "sum"],
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}
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agg_cols = [
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"num_buys",
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"profit_abs_sum",
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"profit_abs_median",
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"profit_abs_mean",
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"median_profit_pct",
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"mean_profit_pct",
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"total_profit_pct",
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]
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sortcols = ["profit_abs_sum", "enter_reason"]
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# 1: profit summaries grouped by enter_tag
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if g == "1":
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group_mask = ["enter_reason"]
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# 2: profit summaries grouped by enter_tag and exit_tag
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if g == "2":
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group_mask = ["enter_reason", "exit_reason"]
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# 3: profit summaries grouped by pair and enter_tag
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if g == "3":
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group_mask = ["pair", "enter_reason"]
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# 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
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if g == "4":
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group_mask = ["pair", "enter_reason", "exit_reason"]
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# 5: profit summaries grouped by exit_tag
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if g == "5":
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group_mask = ["exit_reason"]
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sortcols = ["exit_reason"]
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if group_mask:
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new = bigdf.groupby(group_mask).agg(agg_mask).reset_index()
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new.columns = group_mask + agg_cols
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new["median_profit_pct"] = new["median_profit_pct"] * 100
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new["mean_profit_pct"] = new["mean_profit_pct"] * 100
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new["total_profit_pct"] = new["total_profit_pct"] * 100
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_print_table(new, sortcols, name=f"Group {g}:", to_csv=to_csv, csv_path=csv_path)
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else:
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logger.warning("Invalid group mask specified.")
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def _do_rejected_signals_output(
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rejected_signals_df: pd.DataFrame, to_csv: bool = False, csv_path=None
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) -> None:
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cols = ["pair", "date", "enter_tag"]
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sortcols = ["date", "pair", "enter_tag"]
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_print_table(
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rejected_signals_df[cols],
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sortcols,
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show_index=False,
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name="Rejected Signals:",
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to_csv=to_csv,
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csv_path=csv_path,
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)
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def _select_rows_within_dates(df, timerange=None, df_date_col: str = "date"):
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if timerange:
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if timerange.starttype == "date":
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df = df.loc[(df[df_date_col] >= timerange.startdt)]
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if timerange.stoptype == "date":
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df = df.loc[(df[df_date_col] < timerange.stopdt)]
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return df
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def _select_rows_by_tags(df, enter_reason_list, exit_reason_list):
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if enter_reason_list and "all" not in enter_reason_list:
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df = df.loc[(df["enter_reason"].isin(enter_reason_list))]
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if exit_reason_list and "all" not in exit_reason_list:
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df = df.loc[(df["exit_reason"].isin(exit_reason_list))]
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return df
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def prepare_results(
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analysed_trades, stratname, enter_reason_list, exit_reason_list, timerange=None
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):
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res_df = pd.DataFrame()
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for pair, trades in analysed_trades[stratname].items():
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if trades.shape[0] > 0:
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trades.dropna(subset=["close_date"], inplace=True)
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res_df = pd.concat([res_df, trades], ignore_index=True)
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res_df = _select_rows_within_dates(res_df, timerange)
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if res_df is not None and res_df.shape[0] > 0 and ("enter_reason" in res_df.columns):
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res_df = _select_rows_by_tags(res_df, enter_reason_list, exit_reason_list)
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return res_df
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def print_results(
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res_df: pd.DataFrame,
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analysis_groups: List[str],
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indicator_list: List[str],
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csv_path: Path,
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rejected_signals=None,
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to_csv=False,
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):
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if res_df.shape[0] > 0:
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if analysis_groups:
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_do_group_table_output(res_df, analysis_groups, to_csv=to_csv, csv_path=csv_path)
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if rejected_signals is not None:
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if rejected_signals.empty:
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print("There were no rejected signals.")
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else:
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_do_rejected_signals_output(rejected_signals, to_csv=to_csv, csv_path=csv_path)
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# NB this can be large for big dataframes!
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if "all" in indicator_list:
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_print_table(
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res_df, show_index=False, name="Indicators:", to_csv=to_csv, csv_path=csv_path
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)
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elif indicator_list is not None and indicator_list:
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available_inds = []
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for ind in indicator_list:
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if ind in res_df:
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available_inds.append(ind)
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ilist = ["pair", "enter_reason", "exit_reason"] + available_inds
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_print_table(
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res_df[ilist],
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sortcols=["exit_reason"],
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show_index=False,
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name="Indicators:",
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to_csv=to_csv,
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csv_path=csv_path,
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)
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else:
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print("\\No trades to show")
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def _print_table(
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df: pd.DataFrame, sortcols=None, *, show_index=False, name=None, to_csv=False, csv_path: Path
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):
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if sortcols is not None:
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data = df.sort_values(sortcols)
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else:
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data = df
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if to_csv:
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safe_name = Path(csv_path, name.lower().replace(" ", "_").replace(":", "") + ".csv")
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data.to_csv(safe_name)
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print(f"Saved {name} to {safe_name}")
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else:
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if name is not None:
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print(name)
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print(tabulate(data, headers="keys", tablefmt="psql", showindex=show_index))
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def process_entry_exit_reasons(config: Config):
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try:
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analysis_groups = config.get("analysis_groups", [])
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enter_reason_list = config.get("enter_reason_list", ["all"])
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exit_reason_list = config.get("exit_reason_list", ["all"])
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indicator_list = config.get("indicator_list", [])
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do_rejected = config.get("analysis_rejected", False)
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to_csv = config.get("analysis_to_csv", False)
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csv_path = Path(config.get("analysis_csv_path", config["exportfilename"]))
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if to_csv and not csv_path.is_dir():
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raise OperationalException(f"Specified directory {csv_path} does not exist.")
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timerange = TimeRange.parse_timerange(
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None if config.get("timerange") is None else str(config.get("timerange"))
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)
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backtest_stats = load_backtest_stats(config["exportfilename"])
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for strategy_name, results in backtest_stats["strategy"].items():
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trades = load_backtest_data(config["exportfilename"], strategy_name)
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if trades is not None and not trades.empty:
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signal_candles = _load_signal_candles(config["exportfilename"])
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rej_df = None
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if do_rejected:
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rejected_signals_dict = _load_rejected_signals(config["exportfilename"])
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rej_df = prepare_results(
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rejected_signals_dict,
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strategy_name,
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enter_reason_list,
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exit_reason_list,
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timerange=timerange,
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)
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analysed_trades_dict = _process_candles_and_indicators(
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config["exchange"]["pair_whitelist"], strategy_name, trades, signal_candles
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)
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res_df = prepare_results(
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analysed_trades_dict,
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strategy_name,
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enter_reason_list,
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exit_reason_list,
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timerange=timerange,
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)
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print_results(
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res_df,
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analysis_groups,
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indicator_list,
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rejected_signals=rej_df,
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to_csv=to_csv,
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csv_path=csv_path,
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)
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except ValueError as e:
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raise OperationalException(e) from e
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