scripts/collect_form4.py

# collect_form4.py
# Collects SEC Form 4 (insider trading) data for all 656 S&P 500 CIKs.
#
# Source: SEC EDGAR Insider Transactions Data Sets (official quarterly flat files)
#   https://www.sec.gov/data-research/sec-markets-data/insider-transactions-data-sets
#
# The SEC pre-parses all Form 3/4/5 XML submissions into quarterly ZIP files.
# This is vastly faster than scraping individual XMLs (~20 min vs ~11 hours).
#
# Method:
#   1. Download quarterly ZIP files for 2006 Q1 → 2026 Q1 (~80 files)
#   2. From each ZIP, read SUBMISSION.tsv + REPORTINGOWNER.tsv + NONDERIV_TRANS.tsv
#   3. Filter SUBMISSION rows where issuer_cik is in our 656-company universe
#   4. Join owner and transaction details
#   5. Output clean form4_transactions.csv
#
# Output:
#   datasets/form4_transactions.csv  — one row per non-derivative transaction
#
# Run: python scripts/collect_form4.py

import io
import os
import time
import zipfile
import requests
import pandas as pd
from tqdm import tqdm

# ── Paths ──────────────────────────────────────────────────────────────────────
UNIVERSE_PATH = r"D:\UoE AI\Dissertation\IPP Draft\datasets\sp500_union_constituents(1).csv"
OUT_PATH      = r"D:\UoE AI\Dissertation\IPP Draft\datasets\form4_transactions.csv"

# ── Config ─────────────────────────────────────────────────────────────────────
HEADERS    = {'User-Agent': 'S2880814 University of Edinburgh s.g.vishnu@sms.ed.ac.uk'}
SLEEP      = 0.5      # conservative — these are bulk files, not per-filing requests
TIMEOUT    = 120      # ZIP files can be large, allow more time

# Date range: 2006 Q1 → 2026 Q1
START_YEAR, START_QTR = 2006, 1
END_YEAR,   END_QTR   = 2026, 1


# ── 1. Load S&P 500 universe ───────────────────────────────────────────────────
print("Loading S&P 500 universe...")
universe  = pd.read_csv(UNIVERSE_PATH)
cik_col   = [c for c in universe.columns if 'cik' in c.lower()][0]
tkr_col   = [c for c in universe.columns if any(k in c.lower() for k in ('ticker', 'symbol', 'tic'))][0]
universe[cik_col] = universe[cik_col].astype(int)
cik_set   = set(universe[cik_col].dropna().astype(int))
cik2tkr   = dict(zip(universe[cik_col], universe[tkr_col]))
print(f"  {len(cik_set)} unique CIKs loaded")


# ── 2. Build list of quarterly ZIP URLs ───────────────────────────────────────
# SEC URL pattern: https://www.sec.gov/files/dera/data/insider-transactions/{YYYY}q{Q}_form345.zip
def quarter_urls():
    urls = []
    year, qtr = START_YEAR, START_QTR
    while (year, qtr) <= (END_YEAR, END_QTR):
        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 = 1
            year += 1
    return urls

quarters = quarter_urls()
print(f"\n{len(quarters)} quarterly ZIP files to process (2006 Q1 → 2026 Q1)")


# ── 3. Process each quarterly ZIP ─────────────────────────────────────────────
all_records = []
failed_quarters = []

for year, qtr, url in tqdm(quarters, desc="Quarters"):
    time.sleep(SLEEP)

    # Download ZIP into memory
    try:
        r = requests.get(url, headers=HEADERS, timeout=TIMEOUT)
    except requests.RequestException as e:
        tqdm.write(f"  [SKIP] {year} Q{qtr} — request error: {e}")
        failed_quarters.append((year, qtr))
        continue

    if r.status_code == 404:
        tqdm.write(f"  [SKIP] {year} Q{qtr} — not yet published (404)")
        continue
    if r.status_code != 200:
        tqdm.write(f"  [SKIP] {year} Q{qtr} — HTTP {r.status_code}")
        failed_quarters.append((year, qtr))
        continue

    try:
        zf = zipfile.ZipFile(io.BytesIO(r.content))
    except zipfile.BadZipFile:
        tqdm.write(f"  [SKIP] {year} Q{qtr} — bad ZIP file")
        failed_quarters.append((year, qtr))
        continue

    zip_names = zf.namelist()

    # ── Read SUBMISSION table (issuer CIK + filing metadata) ──────────────────
    sub_file = next((n for n in zip_names if 'submission' in n.lower()), None)
    if not sub_file:
        tqdm.write(f"  [SKIP] {year} Q{qtr} — SUBMISSION file not found in ZIP")
        continue

    sub = pd.read_csv(
        io.BytesIO(zf.read(sub_file)),
        sep='\t', low_memory=False,
        on_bad_lines='skip'
    )
    sub.columns = sub.columns.str.lower().str.strip()

    # Identify issuer CIK column (varies slightly across years)
    issuer_col = next((c for c in sub.columns if 'issuer' in c and 'cik' in c), None) \
              or next((c for c in sub.columns if c == 'issuercik'), None)
    if not issuer_col:
        tqdm.write(f"  [SKIP] {year} Q{qtr} — can't find issuer CIK column. Cols: {sub.columns.tolist()[:8]}")
        continue

    sub[issuer_col] = pd.to_numeric(sub[issuer_col], errors='coerce')
    sub_filtered = sub[sub[issuer_col].isin(cik_set)].copy()

    if sub_filtered.empty:
        continue   # none of our companies filed this quarter

    accessions = set(sub_filtered['accession_number'] if 'accession_number' in sub_filtered.columns
                     else sub_filtered[sub_filtered.columns[0]])

    # ── Read REPORTINGOWNER table (insider details) ────────────────────────────
    owner_file = next((n for n in zip_names if 'reportingowner' in n.lower()), None)
    if owner_file:
        own = pd.read_csv(
            io.BytesIO(zf.read(owner_file)),
            sep='\t', low_memory=False,
            on_bad_lines='skip'
        )
        own.columns = own.columns.str.lower().str.strip()
        acc_col_own = next((c for c in own.columns if 'accession' in c), None)
        if acc_col_own:
            own = own[own[acc_col_own].isin(accessions)]
    else:
        own = pd.DataFrame()

    # ── Read NONDERIV_TRANS table (actual buy/sell transactions) ───────────────
    txn_file = next((n for n in zip_names if 'nonderiv_trans' in n.lower()), None)
    if not txn_file:
        continue

    txn = pd.read_csv(
        io.BytesIO(zf.read(txn_file)),
        sep='\t', low_memory=False,
        on_bad_lines='skip'
    )
    txn.columns = txn.columns.str.lower().str.strip()
    acc_col_txn = next((c for c in txn.columns if 'accession' in c), None)
    if not acc_col_txn:
        continue

    txn = txn[txn[acc_col_txn].isin(accessions)].copy()
    if txn.empty:
        continue

    # ── Join: txn + submission (issuer info) + owner (insider info) ───────────
    # Merge txn with submission on accession number
    merge_on = acc_col_txn
    sub_cols = [issuer_col, 'accession_number' if 'accession_number' in sub_filtered.columns
                else sub_filtered.columns[0],
                'periodofreport', 'filingdate'] if 'filingdate' in sub_filtered.columns \
               else [issuer_col, sub_filtered.columns[0]]

    # Keep only useful submission columns that exist
    useful_sub = [c for c in [issuer_col, 'accession_number', 'periodofreport',
                               'filingdate', 'reporttype'] if c in sub_filtered.columns]
    merged = txn.merge(
        sub_filtered[useful_sub].rename(columns={issuer_col: 'issuer_cik'}),
        left_on=acc_col_txn, right_on='accession_number', how='left'
    )

    # Merge with owner info if available
    if not own.empty and acc_col_own in own.columns:
        useful_own = [c for c in [acc_col_own, 'rptownername', 'rptownercik',
                                   'isofficer', 'isdirector', 'istenpercentowner',
                                   'officertitle'] if c in own.columns]
        merged = merged.merge(
            own[useful_own],
            left_on=acc_col_txn, right_on=acc_col_own, how='left'
        )

    # Add ticker from our universe mapping
    if 'issuer_cik' in merged.columns:
        merged['ticker'] = merged['issuer_cik'].map(cik2tkr)

    all_records.append(merged)
    tqdm.write(f"  {year} Q{qtr}: {len(merged):,} transactions for {sub_filtered[issuer_col].nunique()} companies")


# ── 4. Combine and save ────────────────────────────────────────────────────────
if not all_records:
    print("\nNo records collected — check URL pattern or network connection.")
else:
    df = pd.concat(all_records, ignore_index=True)

    # Standardise key column names where possible
    rename_map = {
        'trans_date':        'transaction_date',
        'transaction_date':  'transaction_date',
        'trans_shares':      'shares',
        'transaction_shares':'shares',
        'trans_pricepershare': 'price_per_share',
        'trans_acquired_disp_cd': 'acquired_disposed',
        'trans_code':        'transaction_code',
        'rptownername':      'owner_name',
        'isofficer':         'is_officer',
        'isdirector':        'is_director',
        'istenpercentowner': 'is_ten_pct_owner',
        'officertitle':      'officer_title',
        'filingdate':        'filing_date',
    }
    df.rename(columns={k: v for k, v in rename_map.items() if k in df.columns}, inplace=True)

    df.to_csv(OUT_PATH, index=False)

    print(f"\n{'='*60}")
    print(f"Form 4 collection complete.")
    print(f"  Total transactions : {len(df):,}")
    print(f"  Unique companies   : {df['issuer_cik'].nunique() if 'issuer_cik' in df.columns else 'N/A'}")
    print(f"  Columns            : {df.columns.tolist()}")
    if failed_quarters:
        print(f"  Failed quarters    : {failed_quarters}")
    print(f"\nSaved -> {OUT_PATH}")

    print("\nSample (first 5 rows):")
    preview_cols = [c for c in ['ticker', 'owner_name', 'officer_title',
                                  'transaction_date', 'transaction_code',
                                  'acquired_disposed', 'shares', 'price_per_share']
                    if c in df.columns]
    print(df[preview_cols].head().to_string(index=False))