Derive missing bank statement opening and closing balances

app/modules/bank_statement_analyzer/parsers/base.py

@@ -203,6 +203,75 @@ def extract_text(path: str | Path) -> str:
 def page_of_line(text: str, position: int) -> int:
     return text[:position].count('\f')+1
 
+
+
+def _chronological_row_indices(df: pd.DataFrame) -> list:
+    """Return transaction row indices in statement chronology.
+
+    Bank exports may be oldest-first or newest-first.  Preserve the bank's row
+    order within a date while reversing the full sequence only when the dated
+    rows clearly run newest-to-oldest.
+    """
+    if df.empty:
+        return []
+    dates = pd.to_datetime(df.get("transaction_date"), errors="coerce")
+    valid = dates.dropna()
+    descending = len(valid) >= 2 and valid.iloc[0] > valid.iloc[-1]
+    indices = df.index.tolist()
+    return list(reversed(indices)) if descending else indices
+
+
+def _derive_missing_statement_meta(df: pd.DataFrame, meta: StatementMeta) -> None:
+    """Populate balances/totals omitted by the printed statement.
+
+    Some bank statements, including HDFC's compact monthly export, begin with
+    the first transaction and its post-transaction closing balance but do not
+    print a separate opening balance.  In that case:
+
+        opening = first_balance + first_debit - first_credit
+
+    The closing balance is the last chronological running balance.  Explicit
+    values extracted from the PDF are never overwritten.
+    """
+    if df.empty:
+        return
+
+    order = _chronological_row_indices(df)
+    if not order:
+        return
+
+    if meta.opening_balance is None:
+        for idx in order:
+            balance = df.at[idx, "balance"] if "balance" in df.columns else None
+            if pd.isna(balance):
+                continue
+            debit = df.at[idx, "debit"] if "debit" in df.columns else None
+            credit = df.at[idx, "credit"] if "credit" in df.columns else None
+            debit_value = 0.0 if pd.isna(debit) else float(debit)
+            credit_value = 0.0 if pd.isna(credit) else float(credit)
+            if debit_value == 0.0 and credit_value == 0.0:
+                continue
+            meta.opening_balance = round(float(balance) + debit_value - credit_value, 2)
+            break
+
+    if meta.closing_balance is None:
+        for idx in reversed(order):
+            balance = df.at[idx, "balance"] if "balance" in df.columns else None
+            if pd.notna(balance):
+                meta.closing_balance = round(float(balance), 2)
+                break
+
+
+def _derive_missing_statement_totals(df: pd.DataFrame, meta: StatementMeta) -> None:
+    """Populate statement totals from the validated transaction population."""
+    if df.empty:
+        return
+    if meta.total_debit is None:
+        meta.total_debit = round(float(pd.to_numeric(df.get("debit"), errors="coerce").fillna(0).sum()), 2)
+    if meta.total_credit is None:
+        meta.total_credit = round(float(pd.to_numeric(df.get("credit"), errors="coerce").fillna(0).sum()), 2)
+
+
 def _apply_balance_delta_validation(df: pd.DataFrame, meta: StatementMeta) -> pd.DataFrame:
     """Validate and, when necessary, correct debit/credit using balance movement.
 
@@ -224,10 +293,7 @@ def _apply_balance_delta_validation(df: pd.DataFrame, meta: StatementMeta) -> pd
     x["correction_reason"] = ""
     x["extraction_confidence"] = "Printed columns"
 
-    dated = pd.to_datetime(x.get("transaction_date"), errors="coerce")
+    order = _chronological_row_indices(x)
-    valid_dates = dated.dropna()
-    descending = len(valid_dates) >= 2 and valid_dates.iloc[0] > valid_dates.iloc[-1]
-    order = list(reversed(x.index.tolist())) if descending else x.index.tolist()
 
     previous_balance = meta.opening_balance
     for idx in order:
@@ -285,7 +351,9 @@ def finalize(df: pd.DataFrame, meta: StatementMeta) -> pd.DataFrame:
             df[c] = pd.NaT
         else:
             df[c] = values.map(parse_flexible_date)
+    _derive_missing_statement_meta(df, meta)
     df = _apply_balance_delta_validation(df, meta)
+    _derive_missing_statement_totals(df, meta)
     narration_values = df['narration'] if 'narration' in df.columns else pd.Series('', index=df.index)
     reference_values = df['reference_no'] if 'reference_no' in df.columns else pd.Series('', index=df.index)
     df['narration']=narration_values.fillna('').map(norm)