NYC residents pay, on a capital-gain distribution, roughly 20% federal
+ 3.8% NIIT + 6.85-9.65% NY + 3.876% NYC = ~34-37% — the old model's
flat 20% understated the real after-tax drag of high-distribution
funds for this user by ~15 points on exactly the flows that matter.
tax.py: new niit + sl_rate params (decimals). sl_rate is the
state+local marginal rate applied at ORDINARY rates to every flow —
state and local have NO preferential cap-gain rate, so the composite
is lt_rate+niit+sl_rate on cap-gain dists, div_rate+niit+sl_rate on
dividends, and (st/lt_rate)+niit+sl_rate on realized gains.
app.py: two new sidebar fields (persisted in settings.json), wired
through _compute_portfolio's cache key.
tests/test_tax.py: 3 new cases (capg and div composite rates,
net-taxed realized ST gains/losses at st+NIIT+SL).
README: NYC rate note with the 2025 IT-201 schedule values (NYC 3.876%
over $50k; NY 6.85% at $215,400-$1.077M, 9.65% at $1.077M-$5M,
single filer) and the composite example.
The old model valued holdings in ADJ (total-return index) units but
computed distribution flows as raw per-share dollars — so every
distribution, and its tax, was overstated by the raw/adj ratio
(JLPSX: 30.10/11.25 = 2.7x; the Dec-2020 cap-gain tax showed as 12.1%
of the account instead of the true 4.5%). The wiggle in the after-tax
curve was this bug, not a convention issue.
tax.py now:
- values holdings in RAW share units on close prices (bundle.close);
- receives the per-share distribution on its event date, pays the tax
(recorded in TaxResult.taxes), and reinvests the after-tax remainder
at the same day's raw close — the tax's effect lives in the (smaller)
reinvested units and is NOT also deducted from cash (double-count
caught and fixed in review);
- recomputes market value after the reinvestment so equity[t] is the
post-event liquidation value.
With the fix, the 'as-if-liquidated' equity on JLPSX's ex-div day drops
by exactly the true tax cost (4.72% vs 12.1% before); the -22.9% price
drop is offset by the distribution kept.
Also:
- app.py passes bundle.close to the after-tax model (pre-tax
portfolio_returns still uses adj);
- JLPSX/JLPYX: the 2020-12-11 6.824 capital-gain distribution is moved
to the true ex-div date 2020-12-14 (remove/add correction ops), so
the reinvestment prices at the post-drop close;
- tests/test_tax.py: 8 synthetic regression tests (tax magnitude,
reinvestment MV, no double-count, ex-div equity step, per-component
rates); run_tests.sh now runs it.
fundlab/styletilt.py: 22 style/asset sleeves regressed on excess-of-T-bill
returns (full history + 5y); BIC forward selection identifies the tilt
stack; residual-alpha verdict ('factor exposure, not skill' when t<1.75);
data-driven English commentary with sign-specific phrasing. Rendered as a
'Style tilts' block (factor table + prose) in both the app and the HTML
report. All 24 pre-built + 8 ad-hoc fund reports rebuilt.
Any symbol that stands alone in the Symbol box (not a comma-joined
portfolio component) now gets a full report, built on demand and cached
in reports/report_data_adhoc.json. Rendered at the top of the Fund Lab
Summary as group 'A'; pointer at the top of the page. Rendering logic
extracted into render_fund_report() shared by pre-built and on-demand
entries. ~0.3-0.9 s per new fund after one-time panel warmup; instant
afterwards (memory + disk cache).
- per-fund report charts now use chart_widget.equity_chart_html
(st.iframe) instead of static st.plotly_chart: every visible window
re-bases each line to 1.0 at its left edge, so fund/reference/index
compare in any zoom level - same semantics as the Equity curves tab
- removed 'expand all' checkbox (24 expanded sections drowned the page)
- added 'rest of Fund Lab continues below' marker after the report
section; the other Fund Lab sections (alpha search, clusters, N-PORT,
drawdown, tax, CEF) were never deleted and are unchanged
- fundlab/narrative.py: data-driven English prose per fund (performance,
drivers tiered by fit, explicit 'what we do NOT know', bottom line)
- fundlab/reportdata.py: static build -> reports/report_data.json
- app.py Fund Lab Summary: at-a-glance table + per-fund expanders
(narrative, equity curve, period table with fund-ref gap, drivers,
reference mix, tax, cluster peers)
- fundlab/report.py: narrative in the HTML report; forward-selected
reference (weak-fit funds anchor to cash); SLEEVE_DESC exposure
explanations
- BUG: mix_series() never applied the betas (reference curves were raw
sleeve sums; JLPSX 'reference' +407% vs fund +123%) - fixed and all
reference curves/tables regenerated
- reports/fund_report.html + report_data.json regenerated
Raw-intercept alphas absorbed the T-bill yield on uninvested/levered
portions (582 well-fitted funds >2%/yr off; sum-of-betas polluted by
level-matching). Now fund AND sleeves are netted against BIL daily
total return before every regression; a cash position contributes
exactly zero.
- decompose: rf_series()/excess(); shv+bil dropped from regressors
(~0 columns in excess space); FULL_WINDOW -> 2007-06-01 (BIL
inception; mixing raw pre-2007 with excess breaks the fit).
- factors: same excess treatment; shv out of DRIVERS.
- CASH axis redefined: alpha/cash_yield -> net cash position = 1 -
sum(betas) (label 'cash (net posn)').
- CANDIDATE list 250 -> 11: the old list was mostly under-invested
funds whose 'alpha' was cash yield, not skill.
- refback.py: per-fund fitted reference (forward-selected sleeves)
stored as ref_5y/ref_full in search_all.json; app alpha-search
table gains a 'reference (5y)' column - the answer to 'what is
alpha computed against' (the fund's OWN fitted sleeve mix, not one
index).
- App captions updated; raw-alpha-era results backed up as
*_rawalpha.json (not deleted).
A fund's yield is a near-constant in return space, so OLS puts it in
the intercept (alpha_ann), not in the shv/bil betas (which only
measure rate-CHANGE sensitivity). Cash funds therefore showed no
dominant driver.
- factors: cash_yield() (shv trailing-1y total return = local
stand-in for the 13-wk T-bill / 0-3m Treasury index), AXES =
DRIVERS + ['cash'] (alpha_ann / cash_yield, 1.0 = earns the cash
rate); a display/clustering axis, not a regressor.
- cluster: cash column in the loading matrix; k-means distances use
the cash-2x-emphasized matrix (a pure cash fund's level axis was
otherwise swallowed by the low-exposure cloud); labels use raw
values; label() cash-aware (0.25 threshold, 1.5x runner-up).
- app: cluster view over AXES with the emphasized distance.
The 331-fund 'no dominant driver' grab-bag now splits into
'cash (yield) +0.64' (n=187, MM + ultra-short) and 'cash (yield)
+1.26' (n=59 pure). 123/123 tests.
- Fund Lab tab: CEF tax-arb shortlist table (char actual-vs-model,
discount, 5y return/vol, scenario hits, tenders, score) built from
cef_character.json + cef_annual.json.
- fundlab/server_watchdog.sh: relaunches Streamlit on :8599 if the
health endpoint stops answering (the old watchdog only covered the
overnight screen and had exited).
- fundlab/cef_universe_run.py: resumable batch extending per-share
verification to all 295 CEFs -> cef_annual_all.json.
appreciation - a taxable-account feature, not an IRA one
Correction after user pushback. The earlier note "ROC does not help
the taxable case - the deferral replicates the IRA" was WRONG: a
traditional IRA defers to the ORDINARY rate at withdrawal, while ROC
in a taxable account defers to the LTCG rate on a >1y sale (the
distribution is basis-reducing and reappears inside the shareholder's
own capital gain). Under the premise LTCG rate < future ordinary
rate, ROC - like NAV appreciation - favors the taxable account.
Contrast ordinary income (interest, ordinary divs, STCG): taxed at
the ordinary rate in BOTH accounts, so only the IRA's deferral wins.
taxplan.py:
- _deferred_share(): per fund, share of 5y total return that defers
to the investor = (NAV change + ROC) / total return, from the
parsed per-share N-CSR table, max'd with the taxsplit appreciation
share.
- >= 50% deferred -> location "TAXABLE (defers to LTCG)" (renamed
from "TAXABLE (accrues)"); 10-50% ROC in distributions -> note.
- Merger-arb cap 0.35 -> 0.50: HMEZX's per-share table (52% NII /
30% gains / 18% ROC over 5y) refutes "mostly STCG" - HMEZX/MERVX
are now MIXED (check 1099), not clean IRA.
- RESEARCH.md: corrected placement write-up, incl. the distinction
between the tax question and the fund-quality question (heavy ROC
can mean principal erosion - PGSIX NAV -34%/5y - which affects
selection, not the optimal account).
App: order dict + filter updated to the new location name.
Tests: merger-arb expectation updated to MIXED; 2 new checks for
the ROC upgrade/note logic. 99/99 fundlab + 32/32 app.
Follow-up to the tax-location plan: the taxplan score only measured
DISTRIBUTION character. The user rightly noted that NAV appreciation
is also a capital gain (LTCG on a >1y sale). The fund price files
carry both series - Close = raw NAV with distributions paid out,
Adj Close = total return reinvested - so the split is computable
directly per fund (5y window + most-recent-12m payout).
Findings:
- ACCUMULATORS (>=50% of 5y return is price appreciation) get a new
location "TAXABLE (accrues)": MBXIX 76% (0% payout 12m), ATESX 66%,
LAMHX 62%, CVSIX 61%, candidate PBAIX 60% (0% payout 12m). For
these the taxable account's LTCG-on-sale benefit is the dominant
tax event.
- PAY-OUT funds: HMEZX (99% of return distributed - the STCG merger-
arb case), MERVX, COSIX, PMORX, SVARX, SCFZX, DMSZX, munis, credit.
IRA placement stands.
- Data artifacts caught: JLPSX/QSPNX one-time NAV gap events ~2022
(special distribution or reorg) skew the 5y payout average; the
12m payout column reflects current behavior. QCMMRX (MMF) series
is not NAV-based - flagged.
App: tax-location expander gains 5y price / 5y payout / 12m payout
columns and the "TAXABLE (accrues)" filter. RESEARCH.md documents
the capital-loss question: registered RICs cannot distribute net
capital losses; the usable benefit is the fund's internal harvest
reserve (low capital-gain distributions after up-years), which needs
N-CSR/1099 history to verify. 97/32 suites green.
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.
Answer to 'find other alpha-driven funds that complement the portfolio':
- fundlab/search.py: complementarity screen - each fund's daily total
returns vs the same 21 broad sleeve axes (BIC forward selection,
|t|>2), full + 5y; alpha (intercept t), R2, rolling 6m alpha
persistence, correlation vs the current qspnx/pmaix portfolio and the
spy/agg/tlt benchmark mix. Verdict tiers: CANDIDATE (alpha +
persistent + portfolio-corr<0.3) / semi-alpha / alpha-but-correlated /
sleeve mix / weak.
- fundlab/dbmine.py: the actual search universe - the local stocks DB
already holds ~100 US open-end alternatives (AQR, PIMCO, JPM,
Principal, Calamos, GMO, Franklin K2, ...). Name-pattern miner with
share-class family dedupe (keeps the longest-history class).
- fundlab/tickers.py + searchlist.py: external longlist resolution
(chart-API name gate + EDGAR 497 cover tickers). Finding: the famous
multi-strategy/macro names (Millennium, Balyasny, Two Sigma, Winton,
Marshall Wace, Brevan Howard, AQR Event-Driven) are private/offshore
or terminated - not US open-end accessible. Fidelity Multi-Asset
Income (FMSDX) resolved and screens as weak alpha.
- app Fund Lab: 'Alpha search - all screened funds, ranked' table
(80 funds: 13 shortlist + 59 mined + 1 external).
- results (ranked candidates, 5y alpha / t / portfolio-corr):
wmnix Westwood Alt Income +3.8% t6.5 c0.09 | pyaix Payden ARB +3.0%
t4.8 c0.13 | srdax Stone Ridge Div Alts +7.7% t4.2 c0.10 | padqx PGIM
ARB +2.3% t2.4 c0.27 | bxmdx Blackstone Alt MS +3.5% t2.4 c0.30 |
aqmix AQR Mngd Futures +8.0% t2.2 c0.21 | cmnix/gioix semi-alpha.
Key insight: AQR MN / L/S-equity / Vanguard MN show strong alpha but
corr 0.35-0.76 with the portfolio - it is already 50% market-neutral
(qspnx), so more MN is not diversifying.
- tests: 59/59 fundlab (resolver gates, query ladder, family dedupe,
ticker regex), 32/32 app, 14/14 data
- fundlab/nport.py: parse the fund's own category/percentage lines,
as-of date, net assets and dollar-valued positions from N-PORT
schedules of investments (handles per-fund and combined multi-fund
family filings; conservative keyword bucketing of positions)
- fundlab/nport_cache/<sym>.json: parsed snapshots for 15 of 16 funds
(raw SOI HTML kept locally, gitignored; source URLs + filing dates in
nport_manifest.json, md5-verified against EDGAR)
- atesx: no current SOI found (Anchor's recent filings cover the Income
fund) - listed with an honest note
- app.py: new 'Fund Lab' tab - pick a fund, see objective, reported
composition (bar + table), rough keyword buckets, top positions, and
the prospectus strategy excerpt
- tests: parser unit tests (section finding, category regex, buckets)
- data.py: non-blocking load_bundle(); background watcher thread refreshes
the parquet cache (5s scan, 30s min rebuild cadence); refresh()/
up_to_date()/generation()
- statistics tab: one table per benchmark (vs <label>), plain column names
(beta/alpha/return/vol...), selectable+reorderable stat list in
settings.json
- correlation tab: per-portfolio components-vs-benchmarks +
all-portfolios-vs-benchmarks; numbered columns
- global date range (window radio + start/end boxes) applied to all tabs;
metrics.xcorr(); equity window radio gains YTD/3M/1M
- single spec grammar for symbol and benchmark fields: commas join one
portfolio (MSFT:0.6,V:0.4), spaces separate distinct symbols/portfolios;
both fields accept one or many entries
- benchmarks simulated with the same scheme/cost/tax rules; per-benchmark
beta/alpha columns; after-tax benchmark curves
- global Curve mode (pre/after/both) above the tabs; clean names in
single-curve mode
- live updates: field commits on Enter/blur, page recomputes per rerun;
portfolio+tax sims cached (st.cache_data); plotly.js from CDN (4.6MB ->
browser-cached) with F_INLINE_PLOTLY=1 offline fallback
- chart: legend underneath, solid lines, pan sticks to data edges
(width-preserving), zoom edge-clamped
- inputs persist in settings.json across reloads/restarts/devices
- tests: tests/test_app.py (AppTest) + tests/test_e2e_browser.py
(Playwright) via ./run_tests.sh
