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First pass changes for cleaning up

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This commit is contained in:
froggleston
2022-05-29 11:20:11 +01:00
parent 145faf9817
commit e7c5818d16
4 changed files with 75 additions and 96 deletions
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145
freqtrade/data/entryexitanalysis.py
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@@ -7,7 +7,8 @@ import joblib
import pandas as pd
from tabulate import tabulate
from freqtrade.data.btanalysis import get_latest_backtest_filename, load_backtest_data
from freqtrade.data.btanalysis import (get_latest_backtest_filename, load_backtest_data,
load_backtest_stats)
from freqtrade.exceptions import OperationalException
@@ -49,8 +50,8 @@ def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_cand
pair,
trades,
signal_candles[strategy_name][pair])
except Exception:
pass
except Exception as e:
print(f"Cannot process entry/exit reasons for {strategy_name}: ", e)
return analysed_trades_dict
@@ -82,104 +83,79 @@ def _analyze_candles_and_indicators(pair, trades, signal_candles):
try:
trades_red = pd.merge(trades_red, trades_inds, on='signal_date', how='outer')
except Exception as e:
print(e)
raise e
return trades_red
else:
return pd.DataFrame()
def _do_group_table_output(bigdf, glist):
if "0" in glist:
wins = bigdf.loc[bigdf['profit_abs'] >= 0] \
.groupby(['enter_reason']) \
.agg({'profit_abs': ['sum']})
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(['enter_reason']) \
.agg({'profit_abs': ['sum']})
loss.columns = ['profit_abs_loss']
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(['enter_reason']).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 = 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['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']
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']
sortcols = ['total_num_buys']
_print_table(new, sortcols, show_index=True)
if "1" in glist:
new = bigdf.groupby(['enter_reason']) \
.agg({'profit_abs': ['count', 'sum', 'median', 'mean'],
'profit_ratio': ['sum', 'median', 'mean']}
).reset_index()
new.columns = ['enter_reason', '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']
_print_table(new, sortcols, show_index=True)
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)
if "2" in glist:
new = bigdf.groupby(['enter_reason', 'exit_reason']) \
.agg({'profit_abs': ['count', 'sum', 'median', 'mean'],
'profit_ratio': ['sum', 'median', 'mean']}
).reset_index()
new.columns = ['enter_reason', 'exit_reason', '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']
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)
if "3" in glist:
new = bigdf.groupby(['pair', 'enter_reason']) \
.agg({'profit_abs': ['count', 'sum', 'median', 'mean'],
else:
agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
'profit_ratio': ['sum', 'median', 'mean']}
).reset_index()
new.columns = ['pair', 'enter_reason', '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']
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']
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
# 1: profit summaries grouped by enter_tag
if g == "1":
group_mask = ['enter_reason']
_print_table(new, sortcols)
if "4" in glist:
new = bigdf.groupby(['pair', 'enter_reason', 'exit_reason']) \
.agg({'profit_abs': ['count', 'sum', 'median', 'mean'],
'profit_ratio': ['sum', 'median', 'mean']}
).reset_index()
new.columns = ['pair', 'enter_reason', 'exit_reason', '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']
# 2: profit summaries grouped by enter_tag and exit_tag
if g == "2":
group_mask = ['enter_reason', 'exit_reason']
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
# 3: profit summaries grouped by pair and enter_tag
if g == "3":
group_mask = ['pair', 'enter_reason']
_print_table(new, sortcols)
# 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, group,
def _print_results(analysed_trades, stratname, analysis_groups,
enter_reason_list, exit_reason_list,
indicator_list, columns=None):
@@ -191,8 +167,8 @@ def _print_results(analysed_trades, stratname, group,
bigdf = pd.concat([bigdf, trades], ignore_index=True)
if bigdf.shape[0] > 0 and ('enter_reason' in bigdf.columns):
if group is not None:
glist = group.split(",")
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":
@@ -244,6 +220,9 @@ def process_entry_exit_reasons(backtest_dir: Path,
indicator_list: Optional[str] = None):
try:
bt_stats = load_backtest_stats(backtest_dir)
logger.info(bt_stats)
# strategy_name = bt_stats['something']
trades = load_backtest_data(backtest_dir, strategy_name)
except ValueError as e:
raise OperationalException(e) from e