File size: 5,349 Bytes
e3b40d1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | # form4_add_roles.py
# Adds is_officer, is_director, is_ten_pct_owner, officer_title columns
# to the existing form4_transactions.csv.
#
# The SEC's new REPORTINGOWNER format encodes roles as a comma-separated
# string in rptowner_relationship, e.g. "Director,Officer,TenPercentOwner"
# and officer title in rptowner_title.
#
# Re-downloads all 81 quarterly ZIPs (REPORTINGOWNER table only — small),
# merges onto form4_transactions.csv, derives boolean flags, saves.
#
# Run: python scripts/form4_add_roles.py
import io
import time
import zipfile
import requests
import pandas as pd
from tqdm import tqdm
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 clean file ────────────────────────────────────────────────
print("Loading form4_transactions.csv...")
df = pd.read_csv(OUT_PATH, low_memory=False)
print(f" {len(df):,} rows, {len(df.columns)} columns")
our_accessions = set(df['accession_number'].dropna())
# ── 2. Re-download REPORTINGOWNER from all 81 quarters ────────────────────────
def quarter_urls():
urls, year, qtr = [], 2006, 1
while (year, qtr) <= (2026, 1):
urls.append((year, qtr,
f"https://www.sec.gov/files/structureddata/data/insider-transactions-data-sets/{year}q{qtr}_form345.zip"))
qtr += 1
if qtr > 4:
qtr, year = 1, year + 1
return urls
print(f"\nFetching REPORTINGOWNER from 81 quarters for {len(our_accessions):,} accessions...")
owner_frames = []
for year, qtr, url in tqdm(quarter_urls(), desc="Quarters"):
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()
own = own[own['accession_number'].isin(our_accessions)]
if own.empty:
continue
keep = [c for c in ['accession_number', 'rptownercik',
'rptowner_relationship', 'rptowner_title'] if c in own.columns]
owner_frames.append(own[keep].copy())
except Exception as e:
tqdm.write(f" [WARN] {year} Q{qtr}: {e}")
# ── 3. Build roles lookup table ────────────────────────────────────────────────
print("\nBuilding roles lookup...")
owner_df = pd.concat(owner_frames, ignore_index=True)
owner_df = owner_df.drop_duplicates(subset=['accession_number', 'rptownercik'])
# Parse rptowner_relationship → boolean flags
rel = owner_df['rptowner_relationship'].fillna('')
owner_df['is_officer'] = rel.str.contains('Officer', case=False).astype(int)
owner_df['is_director'] = rel.str.contains('Director', case=False).astype(int)
owner_df['is_ten_pct_owner'] = rel.str.contains('TenPercentOwner', case=False).astype(int)
if 'rptowner_title' in owner_df.columns:
owner_df.rename(columns={'rptowner_title': 'officer_title'}, inplace=True)
owner_df.drop(columns=['rptowner_relationship'], inplace=True)
print(f" Owner rows: {len(owner_df):,}")
print(f" Officers : {owner_df['is_officer'].sum():,}")
print(f" Directors : {owner_df['is_director'].sum():,}")
# ── 4. Merge onto main dataframe ───────────────────────────────────────────────
print("\nMerging roles onto transactions...")
# Drop any stale role columns if re-running
for col in ['is_officer', 'is_director', 'is_ten_pct_owner', 'officer_title']:
if col in df.columns:
df.drop(columns=[col], inplace=True)
df = df.merge(owner_df, on=['accession_number', 'rptownercik'], how='left')
# Move role cols next to owner_name
cols = df.columns.tolist()
role_cols = [c for c in ['is_officer', 'is_director', 'is_ten_pct_owner', 'officer_title'] if c in cols]
for rc in reversed(role_cols):
cols.remove(rc)
insert_at = cols.index('rptownercik') + 1
cols.insert(insert_at, rc)
df = df[cols]
# ── 5. Save ────────────────────────────────────────────────────────────────────
df.to_csv(OUT_PATH, index=False)
print(f"\n{'='*60}")
print(f" Final shape : {df.shape}")
print(f" Columns : {df.columns.tolist()}")
print(f"\nSample:")
preview = [c for c in ['ticker', 'owner_name', 'officer_title', 'is_officer',
'is_director', 'transaction_date', 'transaction_code',
'acquired_disposed', 'shares', 'price_per_share'] if c in df.columns]
print(df[preview].head(8).to_string(index=False))
print(f"\nSaved -> {OUT_PATH}")
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