stable/freqtrade/data/entryexitanalysis.py

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import logging
from pathlib import Path
from typing import List, Optional
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import joblib
import pandas as pd
from tabulate import tabulate
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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_signal_candles(backtest_dir: Path):
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if backtest_dir.is_dir():
scpf = Path(backtest_dir,
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Path(get_latest_backtest_filename(backtest_dir)).stem + "_signals.pkl"
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)
else:
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scpf = Path(Path(get_latest_backtest_filename(backtest_dir)).stem + "_signals.pkl")
try:
scp = open(scpf, "rb")
signal_candles = joblib.load(scp)
logger.info(f"Loaded signal candles: {str(scpf)}")
except Exception as e:
logger.error("Cannot load signal candles from pickled results: ", e)
return signal_candles
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])
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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, signal_candles):
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:
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raise e
return trades_red
else:
return pd.DataFrame()
def _do_group_table_output(bigdf, glist):
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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)
else:
agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
'profit_ratio': ['sum', 'median', 'mean']}
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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']
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)
def _print_results(analysed_trades, stratname, analysis_groups,
enter_reason_list, exit_reason_list,
indicator_list, columns=None):
if columns is None:
columns = ['pair', 'open_date', 'close_date', 'profit_abs', 'enter_reason', 'exit_reason']
bigdf = pd.DataFrame()
for pair, trades in analysed_trades[stratname].items():
bigdf = pd.concat([bigdf, trades], ignore_index=True)
if bigdf.shape[0] > 0 and ('enter_reason' in bigdf.columns):
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if analysis_groups is not None:
glist = analysis_groups.split(",")
_do_group_table_output(bigdf, glist)
if enter_reason_list is not None and not enter_reason_list == "all":
enter_reason_list = enter_reason_list.split(",")
bigdf = bigdf.loc[(bigdf['enter_reason'].isin(enter_reason_list))]
if exit_reason_list is not None and not exit_reason_list == "all":
exit_reason_list = exit_reason_list.split(",")
bigdf = bigdf.loc[(bigdf['exit_reason'].isin(exit_reason_list))]
if indicator_list is not None and indicator_list != "":
if indicator_list == "all":
print(bigdf)
else:
available_inds = []
for ind in indicator_list.split(","):
if ind in bigdf:
available_inds.append(ind)
ilist = ["pair", "enter_reason", "exit_reason"] + available_inds
_print_table(bigdf[ilist], sortcols=['exit_reason'], show_index=False)
else:
print("\\_ No trades to show")
def _print_table(df, sortcols=None, show_index=False):
if (sortcols is not None):
data = df.sort_values(sortcols)
else:
data = df
print(
tabulate(
data,
headers='keys',
tablefmt='psql',
showindex=show_index
)
)
def process_entry_exit_reasons(backtest_dir: Path,
pairlist: List[str],
analysis_groups: Optional[str] = "0,1,2",
enter_reason_list: Optional[str] = "all",
exit_reason_list: Optional[str] = "all",
indicator_list: Optional[str] = None):
try:
backtest_stats = load_backtest_stats(backtest_dir)
for strategy_name, results in backtest_stats['strategy'].items():
trades = load_backtest_data(backtest_dir, strategy_name)
if not trades.empty:
signal_candles = _load_signal_candles(backtest_dir)
analysed_trades_dict = _process_candles_and_indicators(pairlist, strategy_name,
trades, signal_candles)
_print_results(analysed_trades_dict,
strategy_name,
analysis_groups,
enter_reason_list,
exit_reason_list,
indicator_list)
except ValueError as e:
raise OperationalException(e) from e