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Update tradingagents/cross_asset_correlation/multi_asset_processor.py 

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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Insider77Circle 2026-02-07 15:06:09 -05:00 committed by GitHub
parent 528dcd8b57
commit b7954d4723
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1 changed files with 16 additions and 11 deletions
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tradingagents/cross_asset_correlation/multi_asset_processor.py
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@ -346,17 +346,22 @@ class MultiAssetProcessor:
class_correlations.loc[class1, class2] = np.nan class_correlations.loc[class1, class2] = np.nan
continue continue
# Get correlations between classes # Get correlations between classes using vectorized operations
class_corr_values = [] sub_matrix = corr_matrix.loc[assets1, assets2].values
for a1 in assets1:
for a2 in assets2: if class1 == class2:
if a1 != a2: # Exclude self-correlation # For intra-class correlation, use upper triangle (excluding diagonal)
corr_value = corr_matrix.loc[a1, a2] if len(assets1) > 1:
if not np.isnan(corr_value): corr_values = sub_matrix[np.triu_indices_from(sub_matrix, k=1)]
class_corr_values.append(corr_value) else:
corr_values = np.array([])
if class_corr_values: else:
class_correlations.loc[class1, class2] = np.mean(class_corr_values) # For inter-class correlation, use the whole sub-matrix
corr_values = sub_matrix.flatten()
# Calculate average correlation, ignoring NaNs
if corr_values.size > 0:
class_correlations.loc[class1, class2] = np.nanmean(corr_values)
else: else:
class_correlations.loc[class1, class2] = np.nan class_correlations.loc[class1, class2] = np.nan