fundlab/taxplan.py categorizes the 16-fund shortlist, the 22 N-PORT
cross-checked candidates, and all 250 screened candidates by the
expected CHARACTER of their distributions, given the user's premise
that the current LTCG rate < the post-retirement ordinary rate:
qualified div + LTCG -> TAXABLE (score >= 0.60)
tax-exempt (munis) -> TAXABLE
ordinary / STCG / REIT -> IRA (score <= 0.35)
in between -> MIXED (pull the 1099-DIV)
cash -> FLEXIBLE
score = estimated share of distributions that are tax-favorable,
from three tiers of ground truth: N-PORT keyword buckets (16), SEC
assetCat/issuerCat buckets (22), sleeve loadings (250), with a
sleeve fallback when the keyword parser left >50% of a book
unclassified, and a manual override for the Leuthold wrappers
(91.7% Leuthold Core ETF, no return history yet).
Key findings:
- shortlist: TAXABLE = ATESX, JLPSX, LAMHX, LCORX, LCRIX (equity);
IRA = ATRFX, COSIX, CVSIX, PMORX, SVARX, EAGMX/EGRSX;
MIXED = MBXIX, QSPNX, PMAIX/PMFKX (same fund, two classes)
- cross-checked: 4 munis -> TAXABLE; HMEZX + MERVX are the merger-
arb trap - equity-looking books whose distributions are mostly
SHORT-TERM gains -> IRA
- candidates: 109 munis TAXABLE, 127 IRA, 6 equity TAXABLE, 7 MIXED
App: Fund Lab "Tax location" expander. Output:
fundlab/taxplan_results.json. Tests: test_taxplan() (9 checks).
97/32 suites green.
fundlab/drawdown.py detects the severe equity drawdown scenarios from
the index (IVV) rather than hard-coding them: one worst peak->trough
per calendar year since 2022, min depth 8% (a 10% floor would silently
drop the 2023 rate shock at -9.9% and the 2024 Aug-5 dip at -8.4%).
Detected: 2022 bear mkt (-24.5%), 2023 rate shock (-9.9%), 2024 vol
spike (-8.4%), 2025 tariff crash (-18.8%), 2026 Q1 drawdown (-8.9%).
For each of the 2,384 screened funds it computes that fund's own-NAV
return over each peak->trough window (first print after the peak to
the last print on/before the trough) and ranks the 250 CANDIDATEs by
# scenarios positive.
Key finding: positive in all 5 scenarios = only 7 funds, all
ultra-short/cash (BILS, QCMMRX, PULS, FHCOX, FHMIX, SAFEX, COIAX).
Drawdown resilience at the top tier is a duration property, not alpha.
The interesting tier is 4/5 WITH real 5y alpha: HMEZX merger arb
(+1.5% 2022, +3.1% 2023, t5 +7.1), MERVX, CBHCX market-neutral, SCFZX
securitized credit (t5 +8.4), ENIAX (t5 +10.1), WMNUX (t5 +6.9), RCTIX.
App: Fund Lab "Drawdown resilience" expander (scenario table +
candidate table). Output: fundlab/drawdown_results.json.
Tests: test_drawdown() added (4 checks). 88/32 suites green.
fundlab/xcheck.py - for each screen candidate, resolve the fund's OWN
registrant CIK (browse-edgar; the 497-cover CIK is the family/trust),
get the exact series name for the ticker (the only reliable
disambiguator between sibling funds), walk the 4 most recent NPORT-P
filing dates, and parse holdings from the interactive NPORT XML
(primary_doc.xml at the accession root - NOT the XSL-rendered view the
submissions API points at). Exact seriesName match > best htm exhibit
parse. Buckets from the authoritative assetCat+issuerCat codes (ABS-O,
ABS-CBDO, DBT+UST/CORP/MUN/NUSS, LON, STIV, RA, EC+RF=fund, ...), not
position-name keywords. Resumable; raw filings cached under
nport_cache/raw/ (gitignored).
nport.py - _SECTION gains the "INVESTMENT PORTFOLIO (unaudited)"
variant (NPORT-EX Sch-F files); find_section/build gain a frac
token-tolerance param (Yahoo names drift from filing names); CMBS/ABS
bucket gains CLO/CBDO/DAC terms.
app Fund Lab - "N-PORT cross-check" expander: per-candidate table
(as-of, n, t5, top code-bucket, #1 position) + per-fund holdings
detail.
RESEARCH.md - cross-check verdicts. 21/22 resolved (qcmmrx is an MMF,
no holdings). The screen's top names are REAL:
- hmezx/mervx = genuine merger arb (equity in deal targets + escrow)
- egrix = 100% wrapper in one macro managed portfolio (underlying not
NPORT-disclosed); etsix = fund of EV internal multi-strat accounts
- wmnux = discounted/zero-coupon corporate bonds + equity swaps (the
"equity names" are bond issuers/swap underlyings)
- scfzx/rctix/aflix = securitized credit/CLO/distressed/levered loans
- hicox/fhmix/usmsx/btmix (munis), aguax/femdx (EM sovereign), anglx
(agency MBS), lpxax (rotated out of prefs into bank/financial debt)
= genuine missing-factor exposures the 35-sleeve model lacks
- fhcox/dultx/safex = short-duration carry (a short-duration sleeve
would explain them)
tests/test_fundlab.py - test_xcheck (14 checks): parse_interactive,
code buckets, name-match normalization, series-name disambiguation.
Also: untrack fundlab/streamlit.log; gitignore raw/ + xcheck_run.log.
84 fundlab / 32 app / 14 data tests pass.
factors.py: full OLS of all 2,384 funds on a 35-driver basis
(overinclusive, no portfolio-corr screening - corr is a replacement
signal, not a rejection). v1's 21 + lqd/hyg/prefs/emb/tip/shy/vtv/
8 sectors/CTA/commodities. Basis fixes: drop vea/vug (dupes of
efa/qqq), drop finux (TERMINATED 2017 - silently zeroed the
complete-case mask; v1 forward selection never hit this), drop bil
(shv/bil near-null -> offsetting shv+64/bil-54 noise fits),
residualize vblix on ivv+tlt (pure vol axis), ridge 0.02.
cluster.py: hierarchical tree saved but fixed-k cuts degenerate
(most funds are blends -> one 2250-fund blob); k-means++ (deterministic)
is the useful grouping, re-run live in-app for any k.
app: Return-driver clusters expander (k slider 10-60, summary table,
member table sorted by alpha-t). Findings at k=30 in RESEARCH.md.
fundlab/RESEARCH.md - running research log: sources that work/die
(full-index = discovery workhorse; browse-edgar JS-dead;
investment-company-tickers.json nonexistent; company_tickers.json
useless for OTC; Yahoo crumb throttled but chart API fine), 13
hard-won learnings (OTC funds report exchange 'Nasdaq' -> use
instrumentType; 497 SGML cover uses UNCLOSED line-based tags ->
parse before tag-stripping; full-index columns drift -> regex the
line; one quarter != universe -> 4-qtr union; accession paths
relative to /Archives/ not /Archives/edgar/data/; family CIKs
repeat -> dedupe by series name; portfolio is 50% MN so MN alpha
funds are 'correlated', not diversifying).
fundlab/overnight.py - resumable all-stage pipeline (kill/restart safe):
verify (Yahoo chart per non-local ticker, 4-thread, 429 backoff,
local tickers measured from CSV row counts) -> select (pure
select_rows: MUTUALFUND, >=5y, one longest-history class per series
name, alpha_name as TAG not filter) -> download (goget in 200-sym
batches) -> screen (streamed, skip-already-done) -> finalize
(verdict counts + candidates the v1 name-filter would have missed).
Universe: 10,372 class tickers -> 10,260 verified -> 2,384 funds
(407 local, 1,977 external; only 54 match the alpha name pattern -
the v2 point is to screen the other 2,330).
app: alpha table now dedupes by sym with search_all.json winning
(comprehensive superset).
tests: select_rows unit tests (ETF drop, short-history drop, class
collapse, name tagging). 70/70 fundlab.
