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Update to pandas 1.5.0 syntax, avoiding warnings

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Matthias 2022-09-27 08:02:51 +00:00
parent bc007ce038
commit 43e847ff2f
2 changed files with 6 additions and 6 deletions
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6
freqtrade/data/btanalysis.py
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@ -341,9 +341,9 @@ def trade_list_to_dataframe(trades: List[LocalTrade]) -> pd.DataFrame:
""" """
df = pd.DataFrame.from_records([t.to_json(True) for t in trades], columns=BT_DATA_COLUMNS) df = pd.DataFrame.from_records([t.to_json(True) for t in trades], columns=BT_DATA_COLUMNS)
if len(df) > 0: if len(df) > 0:
df.loc[:, 'close_date'] = pd.to_datetime(df['close_date'], utc=True) df['close_date'] = pd.to_datetime(df['close_date'], utc=True)
df.loc[:, 'open_date'] = pd.to_datetime(df['open_date'], utc=True) df['open_date'] = pd.to_datetime(df['open_date'], utc=True)
df.loc[:, 'close_rate'] = df['close_rate'].astype('float64') df['close_rate'] = df['close_rate'].astype('float64')
return df return df

6
freqtrade/optimize/optimize_reports.py
View File

@ -173,7 +173,7 @@ def generate_tag_metrics(tag_type: str,
tabular_data = [] tabular_data = []
if tag_type in results.columns: if tag_type in results.columns:
for tag, count in results[tag_type].value_counts().iteritems(): for tag, count in results[tag_type].value_counts().items():
result = results[results[tag_type] == tag] result = results[results[tag_type] == tag]
if skip_nan and result['profit_abs'].isnull().all(): if skip_nan and result['profit_abs'].isnull().all():
continue continue
@ -199,7 +199,7 @@ def generate_exit_reason_stats(max_open_trades: int, results: DataFrame) -> List
""" """
tabular_data = [] tabular_data = []
for reason, count in results['exit_reason'].value_counts().iteritems(): for reason, count in results['exit_reason'].value_counts().items():
result = results.loc[results['exit_reason'] == reason] result = results.loc[results['exit_reason'] == reason]
profit_mean = result['profit_ratio'].mean() profit_mean = result['profit_ratio'].mean()
@ -361,7 +361,7 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
winning_days = sum(daily_profit > 0) winning_days = sum(daily_profit > 0)
draw_days = sum(daily_profit == 0) draw_days = sum(daily_profit == 0)
losing_days = sum(daily_profit < 0) losing_days = sum(daily_profit < 0)
daily_profit_list = [(str(idx.date()), val) for idx, val in daily_profit.iteritems()] daily_profit_list = [(str(idx.date()), val) for idx, val in daily_profit.items()]
return { return {
'backtest_best_day': best_rel, 'backtest_best_day': best_rel,