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N10: universe artefacts shipped with SHA-256; holdings CSV / company_tickers.json not archived; 2,573 -> 2,555 reconciled; missing filings; annual_panel = 2,499 CIKs

Items: FIRC0001-ADVE-15 AI_C-12 DATA-01 ECON-10 EDIT-16 LITE-09 THEO-14 (THEO-07, ECON-18).
data/metadata/{universe,russell3000_constituents,sp500_constituents}.json + SHA256SUMS; 18 dual-class CIKs explain 2,573 lines -> 2,555 CIKs;
7 unmatched tickers absent from ticker_to_cik (10,430 entries); 2,587 and the 14/21 drop counts not reconstructible;
2 filings missing on disk (XEL 10-K 2014, LNT 10-Q 2012) + 14 parse errors; annual_panel/quarterly_panel carry 2,499 CIKs (paper: 2,501).

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Simon-Pierre Boucher committed (Nov 5, 2026) parent c5c8715 Browse files

3 changed files +20,001 −0

added code/audit/a10_universe_artefacts.py +77 −0
@@ -0,0 +1,77 @@
1 +"""N10 - universe artefacts: hashes, reconstructible counts, missing files, 'missing' filings, annual_panel CIKs (cluster H, I). Read-only."""
2 +from __future__ import annotations
3 +import json, sys, subprocess
4 +from pathlib import Path
5 +sys.path.insert(0, str(Path(__file__).resolve().parent))
6 +from common import *
7 +
8 +r = Run("universe")
9 +META = RDB / "data/metadata"
10 +U = {}
11 +files = ["universe.json", "russell3000_constituents.json", "sp500_constituents.json", "cik_mapping.json", "shards.json"]
12 +U["sha256"] = {f: {"sha256": sha256_file(META / f), "bytes": (META / f).stat().st_size, "mtime": dt.datetime.fromtimestamp((META / f).stat().st_mtime).isoformat()} for f in files if (META / f).exists()}
13 +with open(r.out / "SHA256SUMS", "w") as fh:
14 + for f, v in U["sha256"].items():
15 + fh.write(f"{v['sha256']} {f}\n")
16 +# not archived?
17 +found = subprocess.run(["find", str(RDB), "-maxdepth", "6", "(", "-iname", "*holding*", "-o", "-iname", "company_tickers*", "-o", "-iname", "*IWV*", ")", "-not", "-path", "*/.venv/*"], capture_output=True, text=True).stdout.strip().splitlines()
18 +U["holdings_or_company_tickers_files_found"] = found
19 +uni = json.load(open(META / "universe.json")); ru = json.load(open(META / "russell3000_constituents.json")); sp = json.load(open(META / "sp500_constituents.json")); cm = json.load(open(META / "cik_mapping.json"))
20 +U["universe_keys"] = sorted(uni.keys()); U["russell_keys"] = sorted(ru.keys()); U["sp500_keys"] = sorted(sp.keys())
21 +U["cik_mapping_type"] = type(cm).__name__; U["cik_mapping_size"] = len(cm)
22 +U["cik_mapping_scalars"] = {k: v for k, v in cm.items() if not isinstance(v, (list, dict))} if isinstance(cm, dict) else None
23 +U["cik_mapping_dict_sizes"] = {k: len(v) for k, v in cm.items() if isinstance(v, (list, dict))} if isinstance(cm, dict) else None
24 +U["universe_scalars"] = {k: v for k, v in uni.items() if not isinstance(v, (list, dict))}
25 +U["russell_scalars"] = {k: v for k, v in ru.items() if not isinstance(v, (list, dict))}
26 +U["sp500_scalars"] = {k: v for k, v in sp.items() if not isinstance(v, (list, dict))}
27 +cons = ru.get("constituents", [])
28 +U["russell_constituent_lines"] = len(cons)
29 +U["russell_distinct_ciks"] = len({c.get("cik") for c in cons})
30 +U["russell_constituent_keys"] = sorted(cons[0].keys()) if cons else []
31 +U["russell_unmatched"] = ru.get("unmatched")
32 +dup = {}
33 +for c in cons: dup.setdefault(c.get("cik"), []).append(c.get("ticker"))
34 +U["russell_ciks_with_multiple_lines"] = {k: v for k, v in dup.items() if len(v) > 1}
35 +# mapper view of the unmatched tickers
36 +T2C = cm.get("ticker_to_cik", {}) if isinstance(cm, dict) else {}
37 +def lookup(tkr):
38 + out = {"in_ticker_to_cik": T2C.get(tkr) or T2C.get(tkr.upper()) or T2C.get(tkr.replace(".", "-")) or T2C.get(tkr.replace("-", "."))}
39 + for k, v in (cm.items() if isinstance(cm, dict) else []):
40 + if isinstance(v, dict) and k != "ticker_to_cik" and (tkr in v or tkr.upper() in v):
41 + out[k] = v.get(tkr, v.get(tkr.upper()))
42 + if isinstance(v, list) and any(str(x).upper().startswith(tkr.upper()) for x in v[:100000] if isinstance(x, str)):
43 + out[k] = [x for x in v if isinstance(x, str) and str(x).upper().startswith(tkr.upper())][:3]
44 + return out
45 +U["unmatched_mapper_view"] = {str(u): lookup(str(u).split(" ")[0]) for u in (ru.get("unmatched") or [])}
46 +# is the unmatched ticker's company present in the universe under another ticker/CIK (e.g. HOLX)?
47 +U["unmatched_in_universe_by_name"] = {str(u): [ (c.get("ticker"), c.get("cik"), c.get("company_name") or c.get("name")) for c in ucons0 if str(u).split("(")[-1].rstrip(")").split(" ")[0].upper() in str(c.get("company_name") or c.get("name") or "").upper()][:3] for u in (ru.get("unmatched") or [])} if (ucons0 := uni.get("constituents", [])) is not None else None
48 +U["unmatched_in_edgar_companies"] = {}
49 +ucons = uni.get("constituents", [])
50 +U["universe_counts"] = {"n": len(ucons), "in_sp500": sum(1 for c in ucons if c.get("in_sp500")), "in_russell3000": sum(1 for c in ucons if c.get("in_russell3000")),
51 + "both": sum(1 for c in ucons if c.get("in_sp500") and c.get("in_russell3000")), "sp_only": sum(1 for c in ucons if c.get("in_sp500") and not c.get("in_russell3000")),
52 + "ru_only": sum(1 for c in ucons if c.get("in_russell3000") and not c.get("in_sp500")), "keys": sorted(ucons[0].keys()) if ucons else []}
53 +U["sp500_rows"] = len(sp.get("constituents", [])); U["sp500_distinct_ciks"] = len({c.get("cik") for c in sp.get("constituents", [])})
54 +U["reconciliation"] = {"iwv_equity_lines_reported_in_paper": 2587, "reconstructible": False, "reason": "latest-holdings.csv / company_tickers.json not archived; universe.py drops non-company lines with a bare continue",
55 + "lines_mapped": ru.get("count"), "distinct_ciks": U["russell_distinct_ciks"], "unmatched": len(ru.get("unmatched") or []), "universe_n": uni.get("count")}
56 +# 'missing' filings and annual_panel CIKs
57 +con = connect(SPID_DB); attach_edgar(con)
58 +U["download_parse_status"] = con.execute("select form_type, download_status, parse_status, count(*) from edgar.filings group by 1,2,3 order by 1,2,3").fetchall()
59 +r.dump("filings_status_missing.csv", con, "select accession_number, cik, ticker, form_type, filing_date, download_status, parse_status, local_path, file_size_bytes from edgar.filings where parse_status in ('missing','error') or download_status <> 'complete' order by parse_status, 1")
60 +U["filings_missing_on_disk"] = con.execute("select form_type, count(*) from edgar.filings where parse_status='missing' group by 1").fetchall()
61 +U["filings_parse_error"] = con.execute("select form_type, count(*) from edgar.filings where parse_status='error' group by 1").fetchall()
62 +U["sections_for_error_filings"] = con.execute("select count(distinct accession_number) from edgar.filing_sections where accession_number in (select accession_number from edgar.filings where parse_status in ('error','missing'))").fetchone()[0]
63 +U["paper_annual_panel_firms"] = 2501
64 +U["annual_panel_distinct_ciks"] = con.execute("select count(distinct cik) from edgar.annual_panel").fetchone()[0]
65 +U["filings_distinct_ciks"] = con.execute("select count(distinct cik) from edgar.filings").fetchone()[0]
66 +U["companies_rows"] = con.execute("select count(*) from edgar.companies").fetchone()[0]
67 +U["annual_panel_ciks_not_in_filings"] = con.execute("select list(distinct cik) from edgar.annual_panel where cik not in (select cik from edgar.filings)").fetchone()[0]
68 +U["filings_ciks_not_in_annual_panel"] = con.execute("select list(distinct cik) from edgar.filings where cik not in (select cik from edgar.annual_panel)").fetchone()[0]
69 +U["annual_panel_ciks_not_in_companies"] = con.execute("select list(distinct cik) from edgar.annual_panel where cik not in (select cik from edgar.companies)").fetchone()[0]
70 +U["annual_panel_rows"] = con.execute("select count(*), min(fiscal_year), max(fiscal_year) from edgar.annual_panel").fetchone()
71 +U["quarterly_panel_distinct_ciks"] = con.execute("select count(distinct cik) from edgar.quarterly_panel").fetchone()[0]
72 +for u in (ru.get("unmatched") or []):
73 + tk = str(u).split(" ")[0]; nm = str(u).split("(")[-1].rstrip(")")
74 + U["unmatched_in_edgar_companies"][str(u)] = con.execute("select ticker, cik, company_name, in_sp500, in_russell3000 from edgar.companies where upper(ticker)=? or upper(company_name) like ? limit 3", [tk.upper(), "%" + nm.split(" ")[0].upper() + "%"]).fetchall()
75 +U["companies_in_sp500_in_russell"] = con.execute("select count(*) filter (where in_sp500), count(*) filter (where in_russell3000), count(*) filter (where in_sp500 and in_russell3000), count(*) from edgar.companies").fetchone()
76 +r.write_json("universe_artefacts.json", U)
77 +r.finish()
added data/metadata/SHA256SUMS +5 −0
@@ -0,0 +1,5 @@
1 +3967b29b378e50fbbac139bfc905120749ff09dcf8ec5769c3fbb12cb1c016e2 universe.json
2 +758cbebf58540230ede17fd35890336ba6bf9e29fad58e3792ce7134997c6b41 russell3000_constituents.json
3 +5084075260692130896c3c9a187223e2870b8e73774b35d1e28763fe8ffddb7d sp500_constituents.json
4 +7ec59b276cef779f8f714d29d7b77938ee3731f11ea5bd95526006fd03ed20fc cik_mapping.json
5 +8351ff54bea877175ca4bba51fc043ba279b2bfbcc7ddb80db1da534ff922771 shards.json
added data/metadata/russell3000_constituents.json +19919 −0
@@ -0,0 +1,28320 @@
1 +{
2 + "source": "https://www.ishares.com/us/products/239714/ishares-russell-3000-etf/latest-holdings.csv",
3 + "holdings_as_of": "Oct 23, 2026",
4 + "fetched_at": "2026-10-27T16:14:42.459987+00:00",
5 + "count": 2573,
6 + "unmatched": [
7 + "TOWN (TOWNEBANK)",
8 + "FRBA (FIRST BANK)",
9 + "NBN (NORTHEAST BANK)",
10 + "HIFS (HINGHAM INSTITUTION FOR SAVINGS)",
11 + "ADRO (CHINOOK THERAPEUTICS INC)",
12 + "THRD (THIRD HARMONIC BIO INC)",
13 + "HOLX (HOLOGIC INC)"
14 + ],
15 + "constituents": [
16 + {
17 + "ticker_raw": "AAPL",
18 + "ticker": "AAPL",
19 + "company_name": "APPLE",
20 + "sector": "Information Technology",
21 + "location": "United States",
22 + "exchange": "NASDAQ",
23 + "weight_pct": 6.88,
24 + "market_value_usd": 1395685391.45,
25 + "cik": "0000320193"
26 + },
27 + {
28 + "ticker_raw": "NVDA",
29 + "ticker": "NVDA",
30 + "company_name": "NVIDIA",
31 + "sector": "Information Technology",
32 + "location": "United States",
33 + "exchange": "NASDAQ",
34 + "weight_pct": 6.74,
35 + "market_value_usd": 1366190461.44,
36 + "cik": "0001045810"
37 + },
38 + {
39 + "ticker_raw": "MSFT",
40 + "ticker": "MSFT",
41 + "company_name": "MICROSOFT",
42 + "sector": "Information Technology",
43 + "location": "United States",
44 + "exchange": "NASDAQ",
45 + "weight_pct": 5.33,
46 + "market_value_usd": 1081809742.65,
47 + "cik": "0000789019"
48 + },
49 + {
50 + "ticker_raw": "AMZN",
51 + "ticker": "AMZN",
52 + "company_name": "AMAZON.COM INC",
53 + "sector": "Consumer Discretionary",
54 + "location": "United States",
55 + "exchange": "NASDAQ",
56 + "weight_pct": 3.29,
57 + "market_value_usd": 667878136.0,
58 + "cik": "0001018724"
59 + },
60 + {
61 + "ticker_raw": "GOOGL",
62 + "ticker": "GOOGL",
63 + "company_name": "ALPHABET CLASS A",
64 + "sector": "Communication Services",
65 + "location": "United States",
66 + "exchange": "NASDAQ",
67 + "weight_pct": 2.55,
68 + "market_value_usd": 517974577.89,
69 + "cik": "0001652044"
70 + },
71 + {
72 + "ticker_raw": "GOOG",
73 + "ticker": "GOOG",
74 + "company_name": "ALPHABET CLASS C",
75 + "sector": "Communication Services",
76 + "location": "United States",
77 + "exchange": "NASDAQ",
78 + "weight_pct": 2.06,
79 + "market_value_usd": 418801350.72,
80 + "cik": "0001652044"
81 + },
82 + {
83 + "ticker_raw": "AVGO",
84 + "ticker": "AVGO",
85 + "company_name": "BROADCOM INC",
86 + "sector": "Information Technology",
87 + "location": "United States",
88 + "exchange": "NASDAQ",
89 + "weight_pct": 2.05,
90 + "market_value_usd": 415446503.2,
91 + "cik": "0001730168"
92 + },
93 + {
94 + "ticker_raw": "META",
95 + "ticker": "META",
96 + "company_name": "META PLATFORMS CLASS A",
97 + "sector": "Communication Services",
98 + "location": "United States",
99 + "exchange": "NASDAQ",
100 + "weight_pct": 1.93,
101 + "market_value_usd": 392148573.08,
102 + "cik": "0001326801"
103 + },
104 + {
105 + "ticker_raw": "TSLA",
106 + "ticker": "TSLA",
107 + "company_name": "TESLA INC",
108 + "sector": "Consumer Discretionary",
109 + "location": "United States",
110 + "exchange": "NASDAQ",
111 + "weight_pct": 1.49,
112 + "market_value_usd": 301863347.82,
113 + "cik": "0001318605"
114 + },
115 + {
116 + "ticker_raw": "LLY",
117 + "ticker": "LLY",
118 + "company_name": "ELI LILLY",
119 + "sector": "Health Care",
120 + "location": "United States",
121 + "exchange": "NYSE",
122 + "weight_pct": 1.39,
123 + "market_value_usd": 282243307.1,
124 + "cik": "0000059478"
125 + },
126 + {
127 + "ticker_raw": "BRK B",
128 + "ticker": "BRK-B",
129 + "company_name": "BERKSHIRE HATHAWAY INC CLASS B",
130 + "sector": "Financials",
131 + "location": "United States",
132 + "exchange": "NYSE",
133 + "weight_pct": 1.38,
134 + "market_value_usd": 280307892.48,
135 + "cik": "0001067983"
136 + },
137 + {
138 + "ticker_raw": "MU",
139 + "ticker": "MU",
140 + "company_name": "MICRON TECHNOLOGY",
141 + "sector": "Information Technology",
142 + "location": "United States",
143 + "exchange": "NASDAQ",
144 + "weight_pct": 1.35,
145 + "market_value_usd": 274617864.35,
146 + "cik": "0000723125"
147 + },
148 + {
149 + "ticker_raw": "JPM",
150 + "ticker": "JPM",
151 + "company_name": "JPMORGAN CHASE & CO",
152 + "sector": "Financials",
153 + "location": "United States",
154 + "exchange": "NYSE",
155 + "weight_pct": 1.21,
156 + "market_value_usd": 246127855.04,
157 + "cik": "0000019617"
158 + },
159 + {
160 + "ticker_raw": "AMD",
161 + "ticker": "AMD",
162 + "company_name": "ADVANCED MICRO DEVICES",
163 + "sector": "Information Technology",
164 + "location": "United States",
165 + "exchange": "NASDAQ",
166 + "weight_pct": 1.15,
167 + "market_value_usd": 233362725.98,
168 + "cik": "0000002488"
169 + },
170 + {
171 + "ticker_raw": "XOM",
172 + "ticker": "XOM",
173 + "company_name": "EXXONMOBIL HOLDINGS CORP",
174 + "sector": "Energy",
175 + "location": "United States",
176 + "exchange": "NYSE",
177 + "weight_pct": 1.03,
178 + "market_value_usd": 209400785.55,
179 + "cik": "0002115436"
180 + },
181 + {
182 + "ticker_raw": "V",
183 + "ticker": "V",
184 + "company_name": "VISA CLASS A",
185 + "sector": "Financials",
186 + "location": "United States",
187 + "exchange": "NYSE",
188 + "weight_pct": 0.95,
189 + "market_value_usd": 191991627.2,
190 + "cik": "0001403161"
191 + },
192 + {
193 + "ticker_raw": "JNJ",
194 + "ticker": "JNJ",
195 + "company_name": "JOHNSON & JOHNSON",
196 + "sector": "Health Care",
197 + "location": "United States",
198 + "exchange": "NYSE",
199 + "weight_pct": 0.87,
200 + "market_value_usd": 176472269.97,
201 + "cik": "0000200406"
202 + },
203 + {
204 + "ticker_raw": "WMT",
205 + "ticker": "WMT",
206 + "company_name": "WALMART",
207 + "sector": "Consumer Staples",
208 + "location": "United States",
209 + "exchange": "NASDAQ",
210 + "weight_pct": 0.72,
211 + "market_value_usd": 146095076.64,
212 + "cik": "0000104169"
213 + },
214 + {
215 + "ticker_raw": "ABBV",
216 + "ticker": "ABBV",
217 + "company_name": "ABBVIE",
218 + "sector": "Health Care",
219 + "location": "United States",
220 + "exchange": "NYSE",
221 + "weight_pct": 0.69,
222 + "market_value_usd": 140397487.16,
223 + "cik": "0001551152"
224 + },
225 + {
226 + "ticker_raw": "MA",
227 + "ticker": "MA",
228 + "company_name": "MASTERCARD CLASS A",
229 + "sector": "Financials",
230 + "location": "United States",
231 + "exchange": "NYSE",
232 + "weight_pct": 0.69,
233 + "market_value_usd": 139845418.0,
234 + "cik": "0001141391"
235 + },
236 + {
237 + "ticker_raw": "COST",
238 + "ticker": "COST",
239 + "company_name": "COSTCO WHOLESALE CORP",
240 + "sector": "Consumer Staples",
241 + "location": "United States",
242 + "exchange": "NASDAQ",
243 + "weight_pct": 0.61,
244 + "market_value_usd": 123612033.96,
245 + "cik": "0000909832"
246 + },
247 + {
248 + "ticker_raw": "PLTR",
249 + "ticker": "PLTR",
250 + "company_name": "PALANTIR TECHNOLOGIES CLASS A",
251 + "sector": "Information Technology",
252 + "location": "United States",
253 + "exchange": "NASDAQ",
254 + "weight_pct": 0.6,
255 + "market_value_usd": 121216297.75,
256 + "cik": "0001321655"
257 + },
258 + {
259 + "ticker_raw": "CVX",
260 + "ticker": "CVX",
261 + "company_name": "CHEVRON",
262 + "sector": "Energy",
263 + "location": "United States",
264 + "exchange": "NYSE",
265 + "weight_pct": 0.59,
266 + "market_value_usd": 120042282.19,
267 + "cik": "0000093410"
268 + },
269 + {
270 + "ticker_raw": "CSCO",
271 + "ticker": "CSCO",
272 + "company_name": "CISCO SYSTEMS INC",
273 + "sector": "Information Technology",
274 + "location": "United States",
275 + "exchange": "NASDAQ",
276 + "weight_pct": 0.58,
277 + "market_value_usd": 116812714.5,
278 + "cik": "0000858877"
279 + },
280 + {
281 + "ticker_raw": "INTC",
282 + "ticker": "INTC",
283 + "company_name": "INTEL CORPORATION",
284 + "sector": "Information Technology",
285 + "location": "United States",
286 + "exchange": "NASDAQ",
287 + "weight_pct": 0.54,
288 + "market_value_usd": 110299803.12,
289 + "cik": "0000050863"
290 + },
291 + {
292 + "ticker_raw": "UNH",
293 + "ticker": "UNH",
294 + "company_name": "UNITEDHEALTH GROUP",
295 + "sector": "Health Care",
296 + "location": "United States",
297 + "exchange": "NYSE",
298 + "weight_pct": 0.52,
299 + "market_value_usd": 104858644.36,
300 + "cik": "0000731766"
301 + },
302 + {
303 + "ticker_raw": "PG",
304 + "ticker": "PG",
305 + "company_name": "PROCTER & GAMBLE",
306 + "sector": "Consumer Staples",
307 + "location": "United States",
308 + "exchange": "NYSE",
309 + "weight_pct": 0.51,
310 + "market_value_usd": 104239839.44,
311 + "cik": "0000080424"
312 + },
313 + {
314 + "ticker_raw": "KO",
315 + "ticker": "KO",
316 + "company_name": "COCA-COLA",
317 + "sector": "Consumer Staples",
318 + "location": "United States",
319 + "exchange": "NYSE",
320 + "weight_pct": 0.49,
321 + "market_value_usd": 99972530.56,
322 + "cik": "0000021344"
323 + },
324 + {
325 + "ticker_raw": "MRK",
326 + "ticker": "MRK",
327 + "company_name": "MERCK & CO INC",
328 + "sector": "Health Care",
329 + "location": "United States",
330 + "exchange": "NYSE",
331 + "weight_pct": 0.49,
332 + "market_value_usd": 99715652.32,
333 + "cik": "0000310158"
334 + },
335 + {
336 + "ticker_raw": "BAC",
337 + "ticker": "BAC",
338 + "company_name": "BANK OF AMERICA",
339 + "sector": "Financials",
340 + "location": "United States",
341 + "exchange": "NYSE",
342 + "weight_pct": 0.45,
343 + "market_value_usd": 91093446.4,
344 + "cik": "0000070858"
345 + },
346 + {
347 + "ticker_raw": "HD",
348 + "ticker": "HD",
349 + "company_name": "HOME DEPOT",
350 + "sector": "Consumer Discretionary",
351 + "location": "United States",
352 + "exchange": "NYSE",
353 + "weight_pct": 0.44,
354 + "market_value_usd": 88567963.26,
355 + "cik": "0000354950"
356 + },
357 + {
358 + "ticker_raw": "CAT",
359 + "ticker": "CAT",
360 + "company_name": "CATERPILLAR INC",
361 + "sector": "Industrials",
362 + "location": "United States",
363 + "exchange": "NYSE",
364 + "weight_pct": 0.43,
365 + "market_value_usd": 86583984.0,
366 + "cik": "0000018230"
367 + },
368 + {
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