# 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}")