Gahow Wang
ad569f1cea
Register us_momentum_qqq_trend: half overnight_recovery_top10, half a QQQ trend sleeve (strategies/qqq_trend.py: 200-day switch times a 30% vol target, held as TQQQ + SHY). Mixed stock+ETF, US only (extra tickers QQQ, SHY, TQQQ). Research (research/lab/): alpha against QQQ with a 2006-16 design / 2017-26 holdout split (alpha.py, qqq_scan.py, beat_qqq.py); leverage and drawdown frontier (levered_book.py, us_target50.py); synthetic 3x ETFs from 1990 and TrendRider/QQQ-trend stress tests (fetch_long_history.py, letf_history.py, vol_trend.py); short-horizon sleeves, diversifiers, industry momentum, earnings-announcement returns; new point-in-time data sources: SEC Form 4 insider trades (fetch_insider.py, insider.py), 10-K/10-Q text similarity, 8-K earnings dates and SIC codes (fetch_lazy_prices.py, lazy_prices.py), and an S&P 400/600 universe from iShares holdings (fetch_us_mid.py, panel_mid.py). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
373 lines
19 KiB
Python
373 lines
19 KiB
Python
"""
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Fetch SEC "Insider Transactions Data Sets" (structured Form 3/4/5 data,
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quarterly zips from 2006Q1) and keep the open-market trades of officers and
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directors of S&P 500 ever-members in the research price panel.
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uv run python -m research.lab.fetch_insider
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Source: https://www.sec.gov/data-research/sec-markets-data/insider-transactions-data-sets
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The zip links are read from that page (the 2026Q2 file moved to a new path).
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Zips are cached in data/research/ext/insider/ and never downloaded twice.
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Every request sends the SEC User-Agent, goes out directly (no proxy) and is
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spaced >= 0.25 s from the previous one (SEC allows 10/s; we stay under 5/s).
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Kept rows (NONDERIV_TRANS joined to SUBMISSION and REPORTINGOWNER):
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* DOCUMENT_TYPE "4". Amendments (4/A, ~3% of Form 4s) are dropped: they
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mostly restate the original's trades, so keeping both double counts, and
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the original is what the market saw on its filing date.
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* TRANS_CODE P (open-market purchase, acquired) or S (open-market sale,
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disposed) of a non-preferred equity security, no equity swap.
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* The reporting owner is an officer and/or director. Pure 10% owners and
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"other" are dropped. A joint filing (rare) is attributed to one officer/
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director owner (lowest CIK) so a trade is never counted twice.
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* Exact duplicate lines within a filing (same date, code, shares, price,
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shares owned after, ownership form) are dropped.
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Issuer -> panel ticker: the current SEC ticker map (company_tickers.json) by
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issuer CIK; share classes of one CIK (GOOG/GOOGL, ...) all map to it. Issuers
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missing from it (renamed, reorganized, taken private) fall back to the
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filing's own ISSUERTRADINGSYMBOL, limited to the dates that symbol was used
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and to dates before the ticker's current owner first filed, and kept only if
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trade prices match the ticker's unadjusted closes (median error < 25%).
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Writes data/research/insider_trades.pkl:
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{"trades": DataFrame, one row per kept transaction: ticker, cik,
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insider_cik, role, title, ten_pct, filed, trans_date, code,
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shares, price, value, price_ok, plan_10b51, plan_fn, ownership,
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accession
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"tickers": DataFrame[ticker, cik, source, first, last, n_filings, price_err]
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(which issuer CIK covers each panel ticker, and when),
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"unmapped": panel tickers with no issuer}
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Point in time: a trade is usable from the first trading day strictly after
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``filed`` (FILING_DATE). ``trans_date`` is only used to classify traders
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(calendar month and year), never to decide when a trade is known.
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``value`` = shares x reported price; where the price is missing or more than
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2x off the issuer's usual price / unadjusted-close ratio (``price_ok`` False:
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mostly total proceeds typed as the price), shares x the unadjusted close.
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``plan_10b51`` is the Rule 10b5-1 checkbox (Form 4 from April 2023, NaN
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before); ``plan_fn`` is True when a footnote or remark of the filing mentions
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Rule 10b5-1 (available in every year).
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"""
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from __future__ import annotations
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import csv
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import os
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import pickle
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import re
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import time
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import zipfile
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from urllib.parse import urljoin
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import numpy as np
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import pandas as pd
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import requests
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from research.lab.fetch_us_fundamentals import UA, cik_map
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PAGE = "https://www.sec.gov/data-research/sec-markets-data/insider-transactions-data-sets"
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ZIP_DIR = os.path.join("data", "research", "ext", "insider")
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OUT = os.path.join("data", "research", "insider_trades.pkl")
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FUND_RAW = os.path.join("data", "research", "us_fund_raw.pkl")
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SUB_COLS = ["ACCESSION_NUMBER", "FILING_DATE", "DOCUMENT_TYPE", "ISSUERCIK",
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"ISSUERTRADINGSYMBOL", "REMARKS", "AFF10B5ONE"]
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OWN_COLS = ["ACCESSION_NUMBER", "RPTOWNERCIK", "RPTOWNER_RELATIONSHIP", "RPTOWNER_TITLE"]
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TRN_COLS = ["ACCESSION_NUMBER", "SECURITY_TITLE", "TRANS_DATE", "TRANS_CODE", "EQUITY_SWAP_INVOLVED",
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"TRANS_SHARES", "TRANS_PRICEPERSHARE", "TRANS_ACQUIRED_DISP_CD",
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"SHRS_OWND_FOLWNG_TRANS", "DIRECT_INDIRECT_OWNERSHIP"]
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PLAN_RE = r"10b5\s*-\s*1|10b-5-1|10b5one"
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NOT_COMMON = r"prefer|pfd|note|debenture|warrant|bond|option"
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_last = [0.0]
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def _session() -> requests.Session:
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s = requests.Session()
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s.trust_env = False # direct connection: ignore proxy env vars
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s.headers.update(UA)
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return s
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def _get(sess: requests.Session, url: str, **kw) -> requests.Response:
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for attempt in range(5):
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wait = 0.25 - (time.time() - _last[0])
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if wait > 0:
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time.sleep(wait)
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_last[0] = time.time()
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try:
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r = sess.get(url, timeout=300, **kw)
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r.raise_for_status()
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return r
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except Exception as exc: # noqa: BLE001 — retry transient SEC errors
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print(f" {url}: {exc}; retry {attempt + 1}", flush=True)
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time.sleep(5 * (attempt + 1))
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raise RuntimeError(f"failed: {url}")
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def zip_urls(sess: requests.Session) -> dict[str, str]:
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html = _get(sess, PAGE).text
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found = re.findall(r'href="([^"]*?(\d{4}q[1-4])_form345\.zip)"', html)
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return {q: urljoin(PAGE, href) for href, q in sorted(found, key=lambda x: x[1])}
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def download(sess: requests.Session) -> list[str]:
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os.makedirs(ZIP_DIR, exist_ok=True)
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urls = zip_urls(sess)
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print(f"{len(urls)} quarterly zips listed ({min(urls)} .. {max(urls)})", flush=True)
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paths = []
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for q, url in urls.items():
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path = os.path.join(ZIP_DIR, f"{q}_form345.zip")
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if not (os.path.exists(path) and zipfile.is_zipfile(path)):
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r = _get(sess, url)
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with open(path + ".part", "wb") as fh:
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fh.write(r.content)
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os.replace(path + ".part", path)
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print(f" {q}: {len(r.content) / 1e6:.1f} MB", flush=True)
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paths.append(path)
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return paths
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# ---------------------------------------------------------------------------
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# parse one quarter
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# ---------------------------------------------------------------------------
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def _read(z: zipfile.ZipFile, name: str, cols: list[str]) -> pd.DataFrame:
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with z.open(name) as fh:
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return pd.read_csv(fh, sep="\t", dtype=str, quoting=csv.QUOTE_NONE,
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usecols=lambda c: c in cols, encoding_errors="replace")
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def _date(s: pd.Series) -> pd.Series:
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return pd.to_datetime(s, format="%d-%b-%Y", errors="coerce")
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def parse_quarter(path: str) -> tuple[pd.DataFrame, pd.DataFrame]:
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"""(kept trades, every Form 4 filing's issuer CIK / symbol / date)."""
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z = zipfile.ZipFile(path)
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sub = _read(z, "SUBMISSION.tsv", SUB_COLS)
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sub = sub[sub["DOCUMENT_TYPE"].str.strip() == "4"].copy()
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sub["filed"] = _date(sub["FILING_DATE"])
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sub["cik"] = pd.to_numeric(sub["ISSUERCIK"], errors="coerce")
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sub = sub.dropna(subset=["filed", "cik"])
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filings = sub[["ACCESSION_NUMBER", "cik", "ISSUERTRADINGSYMBOL", "filed"]]
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own = _read(z, "REPORTINGOWNER.tsv", OWN_COLS)
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own = own[own["ACCESSION_NUMBER"].isin(sub["ACCESSION_NUMBER"])]
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rel = own["RPTOWNER_RELATIONSHIP"].fillna("").str.lower()
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own = own.assign(officer=rel.str.contains("officer"), director=rel.str.contains("director"),
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ten_pct=rel.str.contains("tenpercent"),
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insider_cik=pd.to_numeric(own["RPTOWNERCIK"], errors="coerce"))
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own = own[(own["officer"] | own["director"]) & own["insider_cik"].notna()]
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own = own.sort_values(["ACCESSION_NUMBER", "insider_cik"]).drop_duplicates("ACCESSION_NUMBER")
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own["role"] = np.where(own["officer"] & own["director"], "officer+director",
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np.where(own["officer"], "officer", "director"))
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tr = _read(z, "NONDERIV_TRANS.tsv", TRN_COLS)
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code = tr["TRANS_CODE"].str.strip()
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ad = tr["TRANS_ACQUIRED_DISP_CD"].str.strip()
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keep = (((code == "P") & (ad == "A")) | ((code == "S") & (ad == "D")))
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keep &= tr["ACCESSION_NUMBER"].isin(own["ACCESSION_NUMBER"])
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keep &= ~tr["SECURITY_TITLE"].fillna("").str.contains(NOT_COMMON, case=False, regex=True)
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keep &= ~tr["EQUITY_SWAP_INVOLVED"].fillna("0").str.strip().isin(["1", "true"])
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tr = tr[keep].copy()
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tr["code"] = tr["TRANS_CODE"].str.strip()
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tr["trans_date"] = _date(tr["TRANS_DATE"])
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for src, dst in (("TRANS_SHARES", "shares"), ("TRANS_PRICEPERSHARE", "price"),
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("SHRS_OWND_FOLWNG_TRANS", "owned_after")):
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tr[dst] = pd.to_numeric(tr[src], errors="coerce")
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tr["ownership"] = tr["DIRECT_INDIRECT_OWNERSHIP"].str.strip()
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tr = tr[tr["shares"] > 0]
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tr = tr.drop_duplicates(["ACCESSION_NUMBER", "trans_date", "code", "shares", "price",
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"owned_after", "ownership"])
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# Rule 10b5-1: the 2023 checkbox, and any footnote / remark that cites the rule
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fn = _read(z, "FOOTNOTES.tsv", ["ACCESSION_NUMBER", "FOOTNOTE_TXT"])
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fn = fn[fn["ACCESSION_NUMBER"].isin(tr["ACCESSION_NUMBER"])]
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cited = set(fn.loc[fn["FOOTNOTE_TXT"].str.contains(PLAN_RE, case=False, na=False, regex=True),
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"ACCESSION_NUMBER"])
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cited |= set(sub.loc[sub["REMARKS"].str.contains(PLAN_RE, case=False, na=False, regex=True),
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"ACCESSION_NUMBER"])
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box = (sub["AFF10B5ONE"].str.strip().str.lower().map({"1": 1.0, "true": 1.0, "0": 0.0, "false": 0.0})
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if "AFF10B5ONE" in sub else pd.Series(np.nan, index=sub.index))
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sub = sub.assign(plan_10b51=box)
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out = (tr.merge(own[["ACCESSION_NUMBER", "insider_cik", "role", "RPTOWNER_TITLE", "ten_pct"]],
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on="ACCESSION_NUMBER")
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.merge(sub[["ACCESSION_NUMBER", "cik", "ISSUERTRADINGSYMBOL", "filed", "plan_10b51"]],
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on="ACCESSION_NUMBER"))
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out["plan_fn"] = out["ACCESSION_NUMBER"].isin(cited)
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out = out.rename(columns={"ACCESSION_NUMBER": "accession", "RPTOWNER_TITLE": "title",
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"ISSUERTRADINGSYMBOL": "symbol"})
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return (out[["accession", "cik", "symbol", "insider_cik", "role", "title", "ten_pct", "filed",
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"trans_date", "code", "shares", "price", "plan_10b51", "plan_fn", "ownership"]],
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filings)
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# ---------------------------------------------------------------------------
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# issuer CIK -> panel ticker
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# ---------------------------------------------------------------------------
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def norm_symbols(sym) -> list[str]:
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"""'(WST)' -> [WST]; 'NYSE: KRC' -> [KRC]; 'CBS, CBS.A' -> [CBS, CBS-A]."""
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if not isinstance(sym, str):
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return []
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s = re.sub(r'["()\[\]]', "", sym.upper())
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s = re.sub(r"\b(NYSE|NASDAQ|NYSE AMERICAN|AMEX|NMS|OTC)\s*:", "", s)
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parts = re.split(r"[,;/&]| AND ", s)
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return [re.sub(r"\s+", "", x).replace(".", "-") for x in parts if x.strip()]
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def raw_closes(tickers: list[str]) -> pd.DataFrame | None:
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"""Unadjusted closes (split-adjusted close x later split ratios), from the
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fundamentals fetch; used to check that an issuer's trade prices belong to
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the ticker, and to value trades whose reported price is off."""
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if not os.path.exists(FUND_RAW):
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return None
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with open(FUND_RAW, "rb") as fh:
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raw = pickle.load(fh)
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close_sa = raw["close_sa"].reindex(columns=tickers)
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ratio = raw["splits"].reindex(index=close_sa.index, columns=tickers).fillna(0.0).replace(0.0, 1.0)
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after = ratio.iloc[::-1].cumprod().iloc[::-1].shift(-1).fillna(1.0)
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return close_sa * after
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def _raw_at(raw: pd.DataFrame, tickers: pd.Series, dates: pd.Series) -> np.ndarray:
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"""raw close of each (ticker, date) pair as of that date (last close on or before)."""
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out = np.full(len(tickers), np.nan)
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ff = raw.ffill()
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col = {t: i for i, t in enumerate(ff.columns)}
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ci = tickers.map(col)
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ri = ff.index.searchsorted(dates.values, side="right") - 1
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ok = ci.notna().values & (ri >= 0) & dates.notna().values
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out[ok] = ff.values[ri[ok], ci[ok].astype(int)]
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return out
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def map_tickers(trades: pd.DataFrame, filings: pd.DataFrame, panel_tickers: list[str],
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raw: pd.DataFrame | None, close: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame]:
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"""Assign each trade to a panel ticker; returns (trades with ticker, ticker map)."""
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cmap = cik_map()
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pset = set(panel_tickers)
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rev: dict[int, list[str]] = {}
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for t, c in cmap.items():
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if t in pset:
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rev.setdefault(c, []).append(t)
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span = filings.groupby("cik")["filed"].agg(["min", "max", "count"])
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rows = []
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for c, ts in rev.items():
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if c in span.index:
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for t in sorted(ts):
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rows.append((t, c, "sec_map", span.at[c, "min"], span.at[c, "max"], span.at[c, "count"]))
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# symbol fallback for issuers the current SEC map does not tie to a panel ticker
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fb = filings[~filings["cik"].isin(list(rev))]
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pairs = (fb.groupby(["cik", "ISSUERTRADINGSYMBOL"])["filed"]
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.agg(first="min", last="max", n="count").reset_index())
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cand = []
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for r in pairs.itertuples(index=False):
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for t in norm_symbols(r.ISSUERTRADINGSYMBOL):
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if t in pset:
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cand.append((t, int(r.cik), r.first, r.last, r.n))
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cand = (pd.DataFrame(cand, columns=["ticker", "cik", "first", "last", "n"])
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.groupby(["ticker", "cik"]).agg(first=("first", "min"), last=("last", "max"), n=("n", "sum"))
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.reset_index())
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px_first = close.apply(lambda s: s.first_valid_index())
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px_last = close.apply(lambda s: s.last_valid_index())
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for r in cand.sort_values("n", ascending=False).itertuples(index=False):
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first, last = r.first, r.last
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owner = cmap.get(r.ticker)
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if owner is not None and owner in span.index: # a predecessor only, never alongside the owner
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last = min(last, span.at[owner, "min"] - pd.Timedelta(days=1))
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if r.ticker in px_first.index and pd.notna(px_first[r.ticker]):
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first, last = max(first, px_first[r.ticker]), min(last, px_last[r.ticker])
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taken = [(a, b) for (t, _, _, a, b, _) in rows if t == r.ticker]
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if last <= first or any(a <= last and first <= b for a, b in taken):
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continue
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rows.append((r.ticker, r.cik, "symbol", first, last, r.n))
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tm = pd.DataFrame(rows, columns=["ticker", "cik", "source", "first", "last", "n_filings"])
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# assign trades: SEC-map issuers to the filing's own share class when it is
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# one of theirs, fallback issuers only inside their window
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prim = {c: ts[0] for c, ts in ((c, sorted(ts)) for c, ts in rev.items())}
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t_sec = trades["cik"].map(prim)
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sym0 = trades["symbol"].map(lambda s: (norm_symbols(s) or [None])[0])
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own_class = [s if (isinstance(s, str) and c in rev and s in rev[c]) else None
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for s, c in zip(sym0.values, trades["cik"].values)]
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trades = trades.assign(ticker=pd.Series(own_class, index=trades.index).fillna(t_sec))
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for r in tm[tm["source"] == "symbol"].itertuples(index=False):
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m = (trades["cik"] == r.cik) & trades["filed"].between(r.first, r.last) & trades["ticker"].isna()
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trades.loc[m, "ticker"] = r.ticker
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trades = trades.dropna(subset=["ticker"]).copy()
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# price check: trade prices vs the ticker's unadjusted close on the trade date
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trades["raw_close"] = _raw_at(raw, trades["ticker"], trades["trans_date"]) if raw is not None else np.nan
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with np.errstate(divide="ignore", invalid="ignore"):
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err = np.abs(np.log(trades["price"] / trades["raw_close"]))
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trades["price_err"] = err.where(trades["price"] > 0)
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chk = trades.groupby(["ticker", "cik"])["price_err"].median().rename("price_err")
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tm = tm.merge(chk, left_on=["ticker", "cik"], right_index=True, how="left")
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bad = tm[(tm["source"] == "symbol") & (tm["price_err"] > np.log(1.25))]
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if len(bad):
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print(" symbol matches rejected on price:", bad[["ticker", "cik", "price_err"]].to_dict("records"))
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tm = tm.drop(bad.index)
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keep = pd.MultiIndex.from_frame(trades[["ticker", "cik"]]).isin(
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pd.MultiIndex.from_frame(bad[["ticker", "cik"]]))
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trades = trades[~keep]
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return trades, tm.reset_index(drop=True)
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def main() -> None:
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for k in ("http_proxy", "https_proxy", "HTTP_PROXY", "HTTPS_PROXY", "all_proxy", "ALL_PROXY"):
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os.environ.pop(k, None) # direct connection for cik_map as well
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sess = _session()
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paths = download(sess)
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parts, fil, t0 = [], [], time.time()
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for path in paths:
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tr, f = parse_quarter(path)
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parts.append(tr)
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fil.append(f)
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print(f" {os.path.basename(path)}: {len(tr):,} P/S lines by officers/directors "
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f"[{time.time() - t0:.0f}s]", flush=True)
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trades = pd.concat(parts, ignore_index=True)
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filings = pd.concat(fil, ignore_index=True).drop_duplicates("ACCESSION_NUMBER")
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trades = trades.drop_duplicates(["accession", "trans_date", "code", "shares", "price", "ownership"])
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trades["cik"] = trades["cik"].astype("int64")
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filings["cik"] = filings["cik"].astype("int64")
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print(f"{len(trades):,} kept lines, {len(filings):,} Form 4 filings", flush=True)
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with open(os.path.join("data", "research", "us_ohlcv.pkl"), "rb") as fh:
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close = pickle.load(fh)["close"]
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tickers = list(close.columns)
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raw = raw_closes(tickers)
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trades, tm = map_tickers(trades, filings, tickers, raw, close)
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# value at the reported price unless it is > 2x off the issuer's usual
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# price / close ratio (~1 normally, ~1500 for BRK-A trades mapped to BRK-B):
|
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# such prices are unit errors (total proceeds typed as the price) and are
|
|
# replaced by the ticker's unadjusted close
|
|
with np.errstate(divide="ignore", invalid="ignore"):
|
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lr = pd.Series(np.log(trades["price"] / trades["raw_close"]), index=trades.index)
|
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lr = lr.where(trades["price"] > 0)
|
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usual = lr.groupby([trades["ticker"], trades["cik"]]).transform("median")
|
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trades["price_ok"] = (lr - usual).abs() < np.log(2)
|
|
px = np.where(trades["price_ok"], trades["price"],
|
|
np.where(trades["raw_close"] > 0, trades["raw_close"], trades["price"]))
|
|
trades["value"] = trades["shares"] * np.where(px > 0, px, np.nan)
|
|
trades["insider_cik"] = trades["insider_cik"].astype("int64")
|
|
cols = ["ticker", "cik", "insider_cik", "role", "title", "ten_pct", "filed", "trans_date", "code",
|
|
"shares", "price", "value", "price_ok", "plan_10b51", "plan_fn", "ownership", "accession"]
|
|
trades = trades[cols].sort_values(["filed", "ticker"]).reset_index(drop=True)
|
|
unmapped = sorted(set(tickers) - set(tm["ticker"]))
|
|
print(f"{len(trades):,} trades in {trades['ticker'].nunique()} panel tickers "
|
|
f"({(trades['code'] == 'P').sum():,} P / {(trades['code'] == 'S').sum():,} S); "
|
|
f"map: {(tm['source'] == 'sec_map').sum()} by CIK, {(tm['source'] == 'symbol').sum()} by symbol; "
|
|
f"unmapped {unmapped}", flush=True)
|
|
with open(OUT, "wb") as fh:
|
|
pickle.dump({"trades": trades, "tickers": tm, "unmapped": unmapped}, fh, protocol=4)
|
|
print(f"Saved {OUT}")
|
|
|
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|
|
if __name__ == "__main__":
|
|
main()
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