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⚡️ Speed up function build_exposure_df by 11% #482

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⚡️ Speed up function build_exposure_df by 11% #482

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69 changes: 42 additions & 27 deletions gs_quant/markets/portfolio_manager_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -174,57 +174,72 @@ def build_exposure_df(notional_df: pd.DataFrame,
factor_data: pd.DataFrame,
by_name: bool) -> pd.DataFrame:
# Multiply sensitivity with notional
columns = universe_sensitivities_df.columns.values.tolist()
columns = universe_sensitivities_df.columns.values
notional_values = notional_df['Notional'].values
universe_sensitivities_df /= 100
for column in columns:
universe_sensitivities_df[column] = universe_sensitivities_df[column] * notional_df['Notional']
# Vectorized multiplication using DataFrame.values for speed
universe_sensitivities_df.loc[:, columns] = universe_sensitivities_df[columns].values * notional_values[:, None]

if factor_data.empty:
if factor_categories:
categories_names = [f.name for f in factor_categories] if by_name else [f.id for f in factor_categories]
universe_sensitivities_df = universe_sensitivities_df[categories_names]

universe_sensitivities_df = pd.concat([universe_sensitivities_df,
universe_sensitivities_df.agg("sum").to_frame().rename(
columns={0: "Total Factor Category Exposure"}).T])
total_row = universe_sensitivities_df.agg("sum")
total_row.name = "Total Factor Category Exposure"
universe_sensitivities_df = pd.concat([
universe_sensitivities_df,
total_row.to_frame().T
])

universe_sensitivities_df = universe_sensitivities_df.sort_values(
by="Total Factor Category Exposure", axis=1, ascending=False)
notional_df = pd.concat(
[notional_df,
notional_df[["Notional"]].agg("sum").to_frame().rename(
columns={0: "Total Factor Category Exposure"}).T])
notional_sum_row = notional_df[["Notional"]].agg("sum")
notional_sum_row.name = "Total Factor Category Exposure"
notional_df = pd.concat([
notional_df,
notional_sum_row.to_frame().T
])

exposure_df = notional_df.join(universe_sensitivities_df).rename_axis("Factor Category", axis=1)
else:
factor_data = factor_data.set_index("name") if by_name else factor_data.set_index("identifier")
new_columns = [(factor_data.loc[f, 'factorCategory'], f) for f in universe_sensitivities_df.columns.values] \
if by_name else [(factor_data.loc[f, 'factorCategoryId'], f) for f in
universe_sensitivities_df.columns.values]
idx_col = "name" if by_name else "identifier"
# Avoid unnecessary df copy - use inplace set_index
factor_data_indexed = factor_data.set_index(idx_col, drop=False)
columns_values = universe_sensitivities_df.columns.values
# Faster lookup using .loc and map
cat_col = "factorCategory" if by_name else "factorCategoryId"
categories = factor_data_indexed.loc[columns_values, cat_col].values
new_columns = list(zip(categories, columns_values))
universe_sensitivities_df = (
universe_sensitivities_df.set_axis(pd.MultiIndex.from_tuples(new_columns), axis=1)
.rename_axis(("Factor Category", "Factor"), axis=1)
)
universe_sensitivities_df = pd.concat([universe_sensitivities_df,
universe_sensitivities_df.agg("sum").to_frame().rename(
columns={0: "Total Factor Exposure"}).T
])
total_row = universe_sensitivities_df.agg("sum")
total_row.name = "Total Factor Exposure"
universe_sensitivities_df = pd.concat([
universe_sensitivities_df,
total_row.to_frame().T
])
universe_sensitivities_df = universe_sensitivities_df.sort_values(
by=["Total Factor Exposure"], axis=1, ascending=False)

# Only return factors that are grouped in the factor categories that we passed; if empty return all factors
if factor_categories:
categories_names = [f.name for f in factor_categories] if by_name else [f.id for f in factor_categories]
universe_sensitivities_df = universe_sensitivities_df[categories_names]
# Slice using .loc for MultiIndex speed/path
universe_sensitivities_df = universe_sensitivities_df.loc[:, categories_names]

notional_sum_row = notional_df[["Notional"]].agg("sum")
notional_sum_row.name = "Total Factor Exposure"
notional_df = pd.concat([
notional_df,
notional_sum_row.to_frame().T
])

notional_df = notional_df.set_axis(pd.MultiIndex.from_tuples(
[("Asset Information", "Asset Name"), ("Asset Information", "Notional")]), axis=1)

notional_df = (
pd.concat([
notional_df,
notional_df[["Notional"]].agg("sum").to_frame().rename(columns={0: "Total Factor Exposure"}).T
]).set_axis(pd.MultiIndex.from_tuples(
[("Asset Information", "Asset Name"), ("Asset Information", "Notional")]), axis=1)
)
# Merge universe sensitivity with notional df
exposure_df = notional_df.join(universe_sensitivities_df).rename_axis(("Factor Category", "Factor"), axis=1)

return exposure_df
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