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scripts/postprocess_form4.py

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+# postprocess_form4.py
+# Cleans up form4_transactions.csv:
+#   1. Drops footnote columns (*_fn)
+#   2. Converts date format from 30-MAR-2006 → 2006-03-30
+#   3. Re-fetches is_officer / is_director / officer_title from one sample
+#      quarter to confirm correct column names, then re-downloads all quarters
+#      to add these columns to the existing data
+#   4. Renames columns for clarity
+#   5. Overwrites form4_transactions.csv with the clean version
+#
+# Run: python scripts/postprocess_form4.py
+
+import io
+import os
+import time
+import zipfile
+import requests
+import pandas as pd
+from tqdm import tqdm
+
+# ── Paths ──────────────────────────────────────────────────────────────────────
+OUT_PATH  = r"D:\UoE AI\Dissertation\IPP Draft\datasets\form4_transactions.csv"
+HEADERS   = {'User-Agent': 'S2880814 University of Edinburgh s.g.vishnu@sms.ed.ac.uk'}
+SLEEP     = 0.5
+TIMEOUT   = 120
+
+# ── 1. Load existing data ──────────────────────────────────────────────────────
+print("Loading form4_transactions.csv...")
+df = pd.read_csv(OUT_PATH, low_memory=False)
+print(f"  {len(df):,} rows, {len(df.columns)} columns")
+
+# ── 2. Drop footnote columns ───────────────────────────────────────────────────
+fn_cols = [c for c in df.columns if c.endswith('_fn')]
+df.drop(columns=fn_cols, inplace=True)
+print(f"  Dropped {len(fn_cols)} footnote columns → {len(df.columns)} columns remain")
+
+# ── 3. Fix date format: 30-MAR-2006 → 2006-03-30 ─────────────────────────────
+print("  Converting dates...")
+df['transaction_date'] = pd.to_datetime(
+    df['transaction_date'], format='mixed', dayfirst=True, errors='coerce'
+).dt.strftime('%Y-%m-%d')
+print(f"  Sample dates after fix: {df['transaction_date'].dropna().head(3).tolist()}")
+
+# ── 4. Discover correct REPORTINGOWNER column names from one sample quarter ────
+print("\nChecking REPORTINGOWNER column names from 2024 Q1...")
+sample_url = "https://www.sec.gov/files/structureddata/data/insider-transactions-data-sets/2024q1_form345.zip"
+r = requests.get(sample_url, headers=HEADERS, timeout=TIMEOUT)
+zf = zipfile.ZipFile(io.BytesIO(r.content))
+owner_file = next((n for n in zf.namelist() if 'reportingowner' in n.lower()), None)
+sample_own = pd.read_csv(io.BytesIO(zf.read(owner_file)), sep='\t', low_memory=False, nrows=5)
+sample_own.columns = sample_own.columns.str.lower().str.strip()
+print(f"  REPORTINGOWNER columns: {sample_own.columns.tolist()}")
+
+# Identify the correct column names
+col_map = {}
+for col in sample_own.columns:
+    if col in ('isdir', 'isdirector'):
+        col_map['is_director'] = col
+    elif col == 'isofficer':
+        col_map['is_officer'] = col
+    elif col == 'istenpercentowner':
+        col_map['is_ten_pct_owner'] = col
+    elif col == 'officertitle':
+        col_map['officer_title'] = col
+    elif 'accession' in col:
+        col_map['accession_col'] = col
+
+print(f"  Mapped columns: {col_map}")
+
+# ── 5. Re-download all quarters to get owner role columns ─────────────────────
+def quarter_urls():
+    urls = []
+    year, qtr = 2006, 1
+    while (year, qtr) <= (2026, 1):
+        url = f"https://www.sec.gov/files/structureddata/data/insider-transactions-data-sets/{year}q{qtr}_form345.zip"
+        urls.append((year, qtr, url))
+        qtr += 1
+        if qtr > 4:
+            qtr, year = 1, year + 1
+    return urls
+
+# Get accession numbers already in our dataset
+our_accessions = set(df['accession_number'].dropna())
+print(f"\nRe-fetching owner role columns for {len(our_accessions):,} accessions...")
+
+acc_col   = col_map.get('accession_col', 'accession_number')
+dir_col   = col_map.get('is_director')
+off_col   = col_map.get('is_officer')
+pct_col   = col_map.get('is_ten_pct_owner')
+title_col = col_map.get('officer_title')
+keep_own  = [c for c in [acc_col, 'rptownercik', dir_col, off_col, pct_col, title_col] if c]
+
+owner_frames = []
+
+for year, qtr, url in tqdm(quarter_urls(), desc="Re-fetching owner data"):
+    time.sleep(SLEEP)
+    try:
+        r = requests.get(url, headers=HEADERS, timeout=TIMEOUT)
+        if r.status_code != 200:
+            continue
+        zf  = zipfile.ZipFile(io.BytesIO(r.content))
+        own_file = next((n for n in zf.namelist() if 'reportingowner' in n.lower()), None)
+        if not own_file:
+            continue
+        own = pd.read_csv(io.BytesIO(zf.read(own_file)), sep='\t', low_memory=False, on_bad_lines='skip')
+        own.columns = own.columns.str.lower().str.strip()
+
+        acc_col_q = next((c for c in own.columns if 'accession' in c), None)
+        if not acc_col_q:
+            continue
+
+        # Only keep rows for our accessions
+        own = own[own[acc_col_q].isin(our_accessions)]
+        if own.empty:
+            continue
+
+        cols_to_keep = [c for c in [acc_col_q, 'rptownercik', dir_col, off_col, pct_col, title_col] if c and c in own.columns]
+        owner_frames.append(own[cols_to_keep].copy())
+
+    except Exception as e:
+        tqdm.write(f"  [WARN] {year} Q{qtr}: {e}")
+        continue
+
+if owner_frames:
+    owner_df = pd.concat(owner_frames, ignore_index=True)
+    owner_df.columns = owner_df.columns.str.lower().str.strip()
+
+    # Rename to standard names
+    rename_own = {}
+    if dir_col   and dir_col   in owner_df.columns: rename_own[dir_col]   = 'is_director'
+    if off_col   and off_col   in owner_df.columns: rename_own[off_col]   = 'is_officer'
+    if pct_col   and pct_col   in owner_df.columns: rename_own[pct_col]   = 'is_ten_pct_owner'
+    if title_col and title_col in owner_df.columns: rename_own[title_col] = 'officer_title'
+    owner_df.rename(columns=rename_own, inplace=True)
+
+    # Deduplicate: one row per accession + rptownercik
+    acc_col_own = next((c for c in owner_df.columns if 'accession' in c), None)
+    if acc_col_own:
+        owner_df = owner_df.drop_duplicates(subset=[acc_col_own, 'rptownercik'])
+
+        print(f"  Owner rows fetched: {len(owner_df):,}")
+        print(f"  Owner columns: {owner_df.columns.tolist()}")
+
+        # Merge onto main df
+        df = df.merge(
+            owner_df,
+            left_on=['accession_number', 'rptownercik'],
+            right_on=[acc_col_own, 'rptownercik'],
+            how='left'
+        )
+        # Drop duplicate accession col if created
+        if acc_col_own != 'accession_number' and acc_col_own in df.columns:
+            df.drop(columns=[acc_col_own], inplace=True)
+
+        print(f"  is_officer populated: {df['is_officer'].notna().sum():,} rows" if 'is_officer' in df.columns else "  [WARN] is_officer not in merged result")
+else:
+    print("  [WARN] No owner role data retrieved — skipping merge")
+
+# ── 6. Final column rename + reorder ──────────────────────────────────────────
+rename_final = {
+    'shrs_ownd_folwng_trans': 'shares_after_txn',
+    'valu_ownd_folwng_trans': 'value_after_txn',
+    'direct_indirect_ownership': 'ownership_type',
+    'trans_form_type': 'form_type',
+    'nonderiv_trans_sk': 'trans_sk',
+}
+df.rename(columns={k: v for k, v in rename_final.items() if k in df.columns}, inplace=True)
+
+# Preferred column order
+priority = ['ticker', 'issuer_cik', 'accession_number', 'filing_date' if 'filing_date' in df.columns else None,
+            'owner_name', 'rptownercik', 'is_officer', 'is_director', 'is_ten_pct_owner', 'officer_title',
+            'transaction_date', 'transaction_code', 'acquired_disposed',
+            'shares', 'price_per_share', 'shares_after_txn', 'value_after_txn',
+            'security_title', 'ownership_type', 'form_type']
+priority = [c for c in priority if c and c in df.columns]
+rest     = [c for c in df.columns if c not in priority]
+df       = df[priority + rest]
+
+# ── 7. Save ────────────────────────────────────────────────────────────────────
+df.to_csv(OUT_PATH, index=False)
+
+print(f"\n{'='*60}")
+print(f"Post-processing complete.")
+print(f"  Final shape    : {df.shape}")
+print(f"  Size on disk   : {os.path.getsize(OUT_PATH)/1024/1024:.1f} MB")
+print(f"  Columns        : {df.columns.tolist()}")
+print(f"\nSample (first 5 rows):")
+preview = [c for c in ['ticker','owner_name','officer_title','is_officer',
+                        'transaction_date','transaction_code','acquired_disposed',
+                        'shares','price_per_share'] if c in df.columns]
+print(df[preview].head().to_string(index=False))
+print(f"\nSaved -> {OUT_PATH}")