316 lines
12 KiB
Python
Executable File
316 lines
12 KiB
Python
Executable File
import logging
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from pathlib import Path
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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 (get_latest_backtest_filename, load_backtest_data,
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load_backtest_stats)
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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(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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scp = open(scpf, "rb")
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loaded_data = joblib.load(scp)
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logger.info(f"Loaded {name} data: {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 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,
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trades,
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signal_candles[strategy_name][pair])
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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, signal_candles):
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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(bigdf, glist, to_csv=False, csv_path=None):
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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 = bigdf.loc[bigdf['profit_abs'] >= 0] \
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.groupby(group_mask) \
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.agg({'profit_abs': ['sum']})
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wins.columns = ['profit_abs_wins']
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loss = bigdf.loc[bigdf['profit_abs'] < 0] \
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.groupby(group_mask) \
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.agg({'profit_abs': ['sum']})
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loss.columns = ['profit_abs_loss']
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new = bigdf.groupby(group_mask).agg({'profit_abs': [
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'count',
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lambda x: sum(x > 0),
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lambda x: sum(x <= 0)]})
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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.columns = ['total_num_buys', 'wins', 'losses', 'profit_abs_wins', 'profit_abs_loss',
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'profit_tot', 'wl_ratio_pct', 'avg_win', 'avg_loss']
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sortcols = ['total_num_buys']
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_print_table(new, sortcols, show_index=True, name="Group 0:",
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to_csv=to_csv, csv_path=csv_path)
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else:
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agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
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'profit_ratio': ['sum', 'median', 'mean']}
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agg_cols = ['num_buys', 'profit_abs_sum', 'profit_abs_median',
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'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct',
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'total_profit_pct']
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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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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}:",
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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(rejected_signals_df, to_csv=False, csv_path=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(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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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(analysed_trades, stratname,
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enter_reason_list, exit_reason_list,
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timerange=None):
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res_df = pd.DataFrame()
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for pair, trades in analysed_trades[stratname].items():
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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(res_df, analysis_groups, indicator_list,
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rejected_signals=None, to_csv=False, csv_path=None):
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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 and not rejected_signals.empty:
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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(res_df,
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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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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(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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else:
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print("\\No trades to show")
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def _print_table(df, sortcols=None, show_index=False, name=None, to_csv=False, csv_path=None):
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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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if csv_path is not None:
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safe_name = Path(csv_path,
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name.lower().replace(" ", "_").replace(":", ""))
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else:
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safe_name = Path("user_data",
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"backtest_results",
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name.lower().replace(" ", "_").replace(":", ""))
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data.to_csv(f"{str(safe_name)}.csv")
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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(
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tabulate(
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data,
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headers='keys',
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tablefmt='psql',
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showindex=show_index
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)
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)
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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 = config.get('analysis_csv_path', config['exportfilename'])
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timerange = TimeRange.parse_timerange(None if config.get(
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'timerange') is None else str(config.get('timerange')))
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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(rejected_signals_dict, strategy_name,
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enter_reason_list, exit_reason_list,
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timerange=timerange)
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analysed_trades_dict = _process_candles_and_indicators(
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config['exchange']['pair_whitelist'], strategy_name,
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trades, signal_candles)
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res_df = prepare_results(analysed_trades_dict, strategy_name,
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enter_reason_list, exit_reason_list,
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timerange=timerange)
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print_results(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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except ValueError as e:
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raise OperationalException(e) from e
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