mirror of
https://github.com/freqtrade/freqtrade.git
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260 lines
10 KiB
Python
Executable File
260 lines
10 KiB
Python
Executable File
import logging
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import os
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from pathlib import Path
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from typing import List, Optional
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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.data.btanalysis import get_latest_backtest_filename, load_backtest_data
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from freqtrade.exceptions import OperationalException
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logger = logging.getLogger(__name__)
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def _load_signal_candles(backtest_dir: Path):
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if backtest_dir.is_dir():
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scpf = Path(backtest_dir,
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os.path.splitext(
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get_latest_backtest_filename(backtest_dir))[0] + "_signals.pkl"
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)
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else:
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scpf = Path(os.path.splitext(
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get_latest_backtest_filename(backtest_dir))[0] + "_signals.pkl"
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)
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try:
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scp = open(scpf, "rb")
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signal_candles = joblib.load(scp)
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logger.info(f"Loaded signal candles: {str(scpf)}")
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except Exception as e:
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logger.error("Cannot load signal candles from pickled results: ", e)
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return signal_candles
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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:
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pass
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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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print(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):
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if "0" in glist:
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wins = bigdf.loc[bigdf['profit_abs'] >= 0] \
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.groupby(['enter_reason']) \
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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(['enter_reason']) \
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.agg({'profit_abs': ['sum']})
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loss.columns = ['profit_abs_loss']
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new = bigdf.groupby(['enter_reason']).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)
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if "1" in glist:
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new = bigdf.groupby(['enter_reason']) \
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.agg({'profit_abs': ['count', 'sum', 'median', 'mean'],
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'profit_ratio': ['sum', 'median', 'mean']}
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).reset_index()
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new.columns = ['enter_reason', '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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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)
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if "2" in glist:
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new = bigdf.groupby(['enter_reason', 'exit_reason']) \
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.agg({'profit_abs': ['count', 'sum', 'median', 'mean'],
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'profit_ratio': ['sum', 'median', 'mean']}
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).reset_index()
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new.columns = ['enter_reason', 'exit_reason', 'num_buys', 'profit_abs_sum',
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'profit_abs_median', 'profit_abs_mean', 'median_profit_pct',
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'mean_profit_pct', 'total_profit_pct']
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sortcols = ['profit_abs_sum', 'enter_reason']
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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)
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if "3" in glist:
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new = bigdf.groupby(['pair', 'enter_reason']) \
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.agg({'profit_abs': ['count', 'sum', 'median', 'mean'],
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'profit_ratio': ['sum', 'median', 'mean']}
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).reset_index()
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new.columns = ['pair', 'enter_reason', 'num_buys', 'profit_abs_sum',
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'profit_abs_median', 'profit_abs_mean', 'median_profit_pct',
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'mean_profit_pct', 'total_profit_pct']
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sortcols = ['profit_abs_sum', 'enter_reason']
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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)
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if "4" in glist:
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new = bigdf.groupby(['pair', 'enter_reason', 'exit_reason']) \
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.agg({'profit_abs': ['count', 'sum', 'median', 'mean'],
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'profit_ratio': ['sum', 'median', 'mean']}
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).reset_index()
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new.columns = ['pair', 'enter_reason', 'exit_reason', 'num_buys', 'profit_abs_sum',
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'profit_abs_median', 'profit_abs_mean', 'median_profit_pct',
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'mean_profit_pct', 'total_profit_pct']
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sortcols = ['profit_abs_sum', 'enter_reason']
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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)
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def _print_results(analysed_trades, stratname, group,
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enter_reason_list, exit_reason_list,
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indicator_list, columns=None):
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if columns is None:
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columns = ['pair', 'open_date', 'close_date', 'profit_abs', 'enter_reason', 'exit_reason']
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bigdf = pd.DataFrame()
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for pair, trades in analysed_trades[stratname].items():
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bigdf = pd.concat([bigdf, trades], ignore_index=True)
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if bigdf.shape[0] > 0 and ('enter_reason' in bigdf.columns):
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if group is not None:
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glist = group.split(",")
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_do_group_table_output(bigdf, glist)
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if enter_reason_list is not None and not enter_reason_list == "all":
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enter_reason_list = enter_reason_list.split(",")
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bigdf = bigdf.loc[(bigdf['enter_reason'].isin(enter_reason_list))]
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if exit_reason_list is not None and not exit_reason_list == "all":
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exit_reason_list = exit_reason_list.split(",")
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bigdf = bigdf.loc[(bigdf['exit_reason'].isin(exit_reason_list))]
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if indicator_list is not None:
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if indicator_list == "all":
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print(bigdf)
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else:
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available_inds = []
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for ind in indicator_list.split(","):
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if ind in bigdf:
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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(bigdf[ilist], sortcols=['exit_reason'], show_index=False)
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else:
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_print_table(bigdf[columns], sortcols=['pair'], show_index=False)
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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):
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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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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(backtest_dir: Path,
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pairlist: List[str],
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strategy_name: str,
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analysis_groups: Optional[str] = "0,1,2",
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enter_reason_list: Optional[str] = "all",
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exit_reason_list: Optional[str] = "all",
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indicator_list: Optional[str] = None):
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try:
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trades = load_backtest_data(backtest_dir, strategy_name)
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except ValueError as e:
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raise OperationalException(e) from e
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if not trades.empty:
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signal_candles = _load_signal_candles(backtest_dir)
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analysed_trades_dict = _process_candles_and_indicators(pairlist, strategy_name,
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trades, signal_candles)
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_print_results(analysed_trades_dict,
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strategy_name,
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analysis_groups,
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enter_reason_list,
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exit_reason_list,
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indicator_list)
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