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fix: start drawdown series with a 0 row

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This will account for edge-cases with no winning / losing trades
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Matthias
2025-11-15 09:52:24 +01:00
parent 70ec376657
commit 38ff755533
1 changed files with 21 additions and 6 deletions
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27
freqtrade/data/metrics.py
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@@ -143,6 +143,20 @@ def _calc_drawdown_series(
max_drawdown_df["drawdown_relative"] = ( max_drawdown_df["drawdown_relative"] = (
max_drawdown_df["high_value"] - max_drawdown_df["cumulative"] max_drawdown_df["high_value"] - max_drawdown_df["cumulative"]
) / max_drawdown_df["high_value"] ) / max_drawdown_df["high_value"]
# Add zero row at start to account for edge-cases with no winning / losing trades - so high/low
# will be 0.0 in such cases.
zero_row = pd.DataFrame(
{
"cumulative": [0.0],
"high_value": [0.0],
"drawdown": [0.0],
"drawdown_relative": [0.0],
"date": [profit_results.loc[0, date_col]],
}
)
max_drawdown_df = pd.concat([zero_row, max_drawdown_df], ignore_index=True)
return max_drawdown_df return max_drawdown_df
@@ -215,6 +229,7 @@ def calculate_max_drawdown(
max_drawdown_df = _calc_drawdown_series( max_drawdown_df = _calc_drawdown_series(
profit_results, date_col=date_col, value_col=value_col, starting_balance=starting_balance profit_results, date_col=date_col, value_col=value_col, starting_balance=starting_balance
) )
# max_drawdown_df has an extra zero row at the start
# Calculate maximum drawdown # Calculate maximum drawdown
idxmin = ( idxmin = (
@@ -223,15 +238,15 @@ def calculate_max_drawdown(
else max_drawdown_df["drawdown"].idxmin() else max_drawdown_df["drawdown"].idxmin()
) )
high_idx = max_drawdown_df.iloc[: idxmin + 1]["high_value"].idxmax() high_idx = max_drawdown_df.iloc[: idxmin + 1]["high_value"].idxmax()
high_date = profit_results.loc[high_idx, date_col] high_date = profit_results.at[max(high_idx - 1, 0), date_col]
low_date = profit_results.loc[idxmin, date_col] low_date = profit_results.at[max(idxmin - 1, 0), date_col]
high_val = max_drawdown_df.loc[high_idx, "high_value"] high_val = max_drawdown_df.at[high_idx, "cumulative"]
low_val = max_drawdown_df.loc[idxmin, "cumulative"] low_val = max_drawdown_df.at[idxmin, "cumulative"]
max_drawdown_rel = max_drawdown_df.loc[idxmin, "drawdown_relative"] max_drawdown_rel = max_drawdown_df.at[idxmin, "drawdown_relative"]
# Calculate current drawdown # Calculate current drawdown
current_high_idx = max_drawdown_df["high_value"].iloc[:-1].idxmax() current_high_idx = max_drawdown_df["high_value"].iloc[:-1].idxmax()
current_high_date = profit_results.loc[current_high_idx, date_col] current_high_date = profit_results.at[max(current_high_idx - 1, 0), date_col]
current_high_value = max_drawdown_df.iloc[-1]["high_value"] current_high_value = max_drawdown_df.iloc[-1]["high_value"]
current_cumulative = max_drawdown_df.iloc[-1]["cumulative"] current_cumulative = max_drawdown_df.iloc[-1]["cumulative"]
current_drawdown_abs = current_high_value - current_cumulative current_drawdown_abs = current_high_value - current_cumulative