Greg Pomerantz
9458e316cb
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.
433 lines
17 KiB
Python
433 lines
17 KiB
Python
"""Tax-location plan: which funds belong in the TAXABLE account vs an IRA.
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The user's premise: current LTCG rate < ordinary-income rate expected
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after retirement. So a fund's right home depends on the CHARACTER of
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its distributions:
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qualified dividends + LTCG -> TAXABLE (the LTCG rate is the benefit)
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tax-exempt interest (munis) -> TAXABLE (wasted in an IRA)
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ordinary interest / STCG /
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non-qualified (REIT, K-1) -> IRA (deferring it is the benefit)
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cash -> FLEXIBLE (no character to place)
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We don't have 1099-DIV characterizations on file for 2,400 funds, so
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this is a STRUCTURAL estimate from what the fund actually holds:
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1. 16-fund shortlist -> nport_cache/*.json buckets (keyword-based)
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2. 22 cross-checked -> xcheck_report.json buckets (SEC
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assetCat/issuerCat codes, precise)
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3. 250 candidates -> factor_results.json sleeve loadings (proxy:
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a fund that trades like 90% Treasuries earns
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~90% ordinary income)
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plus name-based strategy overrides (merger arb gains are mostly
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SHORT-TERM, munis are tax-exempt, FOFs are pass-through, ...).
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The output says where a fund's income character points; the 1099-DIV
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for the most recent year is the final arbiter for MIXED funds.
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Run: python -m fundlab.taxplan
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Output: fundlab/taxplan_results.json
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"""
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from __future__ import annotations
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import json
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import re
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from pathlib import Path
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HERE = Path(__file__).parent
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EXTRA_BETAS = HERE / "universe_cache" / "betas_extra.json"
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SHORTLIST = HERE / "decompose_results.json"
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XCHECK = HERE / "xcheck_report.json"
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FACTORS = HERE / "factor_results.json"
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RESULTS = HERE / "taxplan_results.json"
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# ------------------------------------------------------------------------
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# bucket -> (fraction of distributions that are tax-favorable, character)
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# tax-favorable = qualified dividends + LTCG + tax-exempt interest.
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# A None fraction means "pass-through / unclassified - unknown".
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# ------------------------------------------------------------------------
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BUCKET_FRAC: dict[str, tuple[float | None, str]] = {
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# equity: qualified dividends + LTCG
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"Equity (US)": (1.0, "qualified div + LTCG"),
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"Equity (intl)": (1.0, "qualified div + LTCG"),
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"Equity (common)": (1.0, "qualified div + LTCG"),
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"EC": (1.0, "qualified div + LTCG"),
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# tax-exempt
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"Municipal bond": (1.0, "tax-exempt interest"),
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# mostly non-qualified
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"Real estate": (0.15, "REIT income (mostly ordinary)"),
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"RE": (0.15, "REIT income (mostly ordinary)"),
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"Preferred stock": (0.15, "preferred div (mostly ordinary)"),
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"EP": (0.15, "preferred div (mostly ordinary)"),
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# commodities: 60/40 LTCG if section 1256 futures
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"Commodities": (0.60, "commodity (60/40 if 1256)"),
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"Commodity": (0.60, "commodity (60/40 if 1256)"),
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"COMM": (0.60, "commodity (60/40 if 1256)"),
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# derivatives / structured
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"Futures": (0.40, "derivatives (mixed)"),
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"Derivative / hedge": (0.40, "derivatives (mixed)"),
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"Structured note": (0.30, "structured (mixed)"),
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"SN": (0.30, "structured (mixed)"),
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# debt: ordinary interest
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"US Treasury": (0.0, "ordinary interest"),
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"US agency": (0.0, "ordinary interest"),
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"US GSE (Fed/FF)": (0.0, "ordinary interest"),
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"Corporate bond": (0.05, "ordinary interest"),
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"Debt": (0.05, "ordinary interest"),
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"Foreign sovereign": (0.0, "ordinary interest"),
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"High yield": (0.0, "ordinary interest"),
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"Loan (leveraged/private credit)": (0.0, "ordinary / K-1"),
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"LON": (0.0, "ordinary / K-1"),
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"CLO (collateralized debt)": (0.0, "ordinary interest"),
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"ABS-CBDO": (0.0, "ordinary interest"),
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"ABS-O": (0.0, "ordinary interest"),
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"ABS-MBS": (0.0, "ordinary interest"),
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"ABS-APCP": (0.0, "ordinary interest"),
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"ABS other (CLO/CMBS/AB)": (0.0, "ordinary interest"),
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"ABS auto/personal loan": (0.0, "ordinary interest"),
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"MBS (mortgage-backed)": (0.0, "ordinary interest"),
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"Agency MBS": (0.0, "ordinary interest"),
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"DBT": (0.05, "debt (ordinary)"),
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# cash: ordinary, no placement value
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"Cash & T-bills": (0.0, "cash (ordinary)"),
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"Cash/MMF (short-term)": (0.0, "cash (ordinary)"),
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"STIV": (0.0, "cash (ordinary)"),
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"Repurchase agreement": (0.0, "ordinary interest"),
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"RA": (0.0, "ordinary interest"),
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# pass-through / unclassified
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"Fund holdings": (None, "FOF - pass-through"),
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"Fund/ETF holdings": (None, "FOF - pass-through"),
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"PF": (None, "private fund - pass-through"),
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"RF": (None, "fund - pass-through"),
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# nport.py lump: corporates + munis together
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"IG credit / munis": (0.45, "IG credit + munis (mixed)"),
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"Other": (None, "unclassified"),
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}
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# ------------------------------------------------------------------------
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# sleeve -> same fraction (proxy for the candidate set, no N-PORT on
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# file). Equity sleeves = qualified div + LTCG; bond sleeves = ordinary;
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# commodity/CTA sleeves = 60/40 if section 1256.
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# ------------------------------------------------------------------------
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SLEEVE_FRAC: dict[str, float] = {
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"qqq": 1.0, "ivv": 1.0, "iwm": 1.0, "vea": 1.0, "efa": 1.0,
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"vwo": 1.0, "vug": 1.0, "vtv": 1.0,
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"xlk": 1.0, "xlf": 1.0, "xle": 0.90, "xlv": 0.95, "xlp": 1.0,
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"xlu": 1.0, "xly": 0.95, "xlb": 0.95,
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"vnq": 0.15,
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"bil": 0.0, "shv": 0.0, "shy": 0.0, "ief": 0.0, "tlt": 0.0,
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"agg": 0.0, "lqd": 0.05, "hyg": 0.0, "pff": 0.15, "emb": 0.0,
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"tip": 0.10, "vweax": 0.0, "vmbix": 0.0, "finux": 0.0,
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"fxe": 0.0, "fxy": 0.0, "vblix": 0.0,
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"djp": 0.60, "gsg": 0.60, "gld": 0.60, "dbb": 0.60, "dbmf": 0.60,
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}
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# name-based strategy overrides: (regex, action, note)
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# action: "cap" -> score capped at the given number; None -> note only
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OVERRIDES: list[tuple[re.Pattern, float | None, str]] = [
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(re.compile(r"\bmerger\b", re.I), 0.35,
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"merger arb: gains are largely SHORT-TERM (deals close <1 yr) - "
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"1099 will show STCG despite the equity book"),
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(re.compile(r"style premia|style and valuation", re.I), 0.50,
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"long/short factor strategy: gains mix STCG/LTCG - check 1099"),
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(re.compile(r"event[- ]?driven", re.I), 0.45,
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"event-driven: gains mostly short-term"),
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(re.compile(r"market neutral", re.I), 0.35,
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"market-neutral: gains from short-dated option/systematic trades "
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"- often STCG"),
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(re.compile(r"global macro", re.I), None,
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"macro: 60% LTCG if section-1256 futures; OTC swaps -> STCG - "
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"check 1099"),
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(re.compile(r"managed futures|\bCTA\b|systematic", re.I), None,
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"CTA/systematic: 60/40 if section-1256 regulated futures"),
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(re.compile(r"hedge (fund|strategy|strategies)", re.I), 0.50,
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"hedge fund: gains often short-term - check 1099"),
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(re.compile(r"absolute return", re.I), None,
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"absolute-return: character varies - check 1099"),
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(re.compile(r"multi[- ]?asset", re.I), None,
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"multi-asset: mixed qualified/LTCG + ordinary interest - "
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"check 1099"),
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(re.compile(r"fund of funds", re.I), None,
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"FOF: pass-through of underlying character - check 1099"),
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]
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MUNI_RX = re.compile(r"municipal|tax[- ]?exempt|munis\b", re.I)
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MMF_RX = re.compile(r"money market", re.I)
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TAXABLE_AT = 0.60 # score >= this -> taxable
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IRA_AT = 0.35 # score <= this -> IRA
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FOF_UNKNOWN_AT = 0.40 # >40% pass-through -> can't clear either bar
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def bucket_score(buckets: list[dict]) -> tuple[float, float, list[str]]:
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"""(score, unknown_frac, unknown_names) from N-PORT-style buckets."""
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score = 0.0
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known = 0.0
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unknown = 0.0
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unk_names: list[str] = []
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for b in buckets:
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pct = (b.get("pct") or 0.0) / 100.0
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frac, _ = BUCKET_FRAC.get(b["name"], (None, b["name"]))
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if frac is None:
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unknown += pct
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if pct >= 0.05:
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unk_names.append(f"{b['name']} {pct*100:.0f}%")
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else:
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score += frac * pct
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known += pct
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if known > 0:
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score /= known # renormalize over classified assets
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return score, unknown, unk_names
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def sleeve_score(betas: dict) -> float | None:
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"""Score from sleeve loadings (proxy when no N-PORT is on file)."""
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num = 0.0
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den = 0.0
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for s, b in (betas or {}).items():
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if not isinstance(b, (int, float)) or b <= 0:
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continue
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f = SLEEVE_FRAC.get(s)
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if f is None:
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continue
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num += f * b
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den += b
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if den < 0.05:
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return None
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return num / den
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def classify(name: str,
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buckets: list[dict] | None = None,
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betas: dict | None = None) -> dict:
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"""Location decision for one fund. Returns a result dict."""
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notes: list[str] = []
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unknown = 0.0
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used_sleeves = False
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if buckets:
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score, unknown, unk_names = bucket_score(buckets)
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basis = "N-PORT"
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if unk_names:
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notes.append("unclassified: " + ", ".join(unk_names))
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# keyword parser left most of the book unclassified ("Other") -
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# trust the return sleeves instead
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if unknown > 0.5 and betas:
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s2 = sleeve_score(betas)
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if s2 is not None:
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score, basis, used_sleeves = s2, "N-PORT+sleeves", True
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notes.append("holdings mostly unclassified - used "
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"return sleeves")
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else:
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score = sleeve_score(betas)
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basis = "sleeves"
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if score is None:
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score = 0.5
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notes.append("no clean sleeve match - treat as mixed")
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score = min(max(score, 0.0), 1.0)
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is_muni = bool(MUNI_RX.search(name or ""))
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is_mmf = bool(MMF_RX.search(name or ""))
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if is_muni:
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score = 1.0 # all distributions are tax-exempt = tax-favorable
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# strategy overrides
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cap: float | None = None
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for rx, act, note in OVERRIDES:
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if rx.search(name or ""):
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notes.append(note)
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if act is not None:
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cap = min(cap, act) if cap is not None else act
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if cap is not None:
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score = min(score, cap)
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# location
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if is_muni:
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loc = "TAXABLE (munis)"
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notes.append("tax-exempt interest - keep OUT of the IRA")
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elif is_mmf:
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loc = "FLEXIBLE (cash)"
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notes.append("ordinary interest, no placement value either way")
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elif not used_sleeves and unknown > FOF_UNKNOWN_AT:
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loc = "MIXED (check 1099)"
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notes.append(f"{unknown*100:.0f}% pass-through/unclassified - "
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"character is the underlying funds'")
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elif score >= TAXABLE_AT:
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loc = "TAXABLE"
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elif score <= IRA_AT:
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loc = "IRA"
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else:
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loc = "MIXED (check 1099)"
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return {"name": name, "basis": basis, "score": round(score, 2),
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"unknown": round(unknown, 2), "location": loc,
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"notes": "; ".join(dict.fromkeys(notes))}
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# Manual overrides for funds the structural data can't resolve - each
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# with the reason it's needed. (LCORX/LCRIX are new share classes with
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# no return history; the NPORT shows they are a 91.7% wrapper around the
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# Leuthold Core ETF, a US equity fund.)
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MANUAL: dict[str, tuple[str, str]] = {
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"LCORX": ("TAXABLE", "wrapper: 91.7% Leuthold Core ETF (US equity) "
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"+ 8% money market"),
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"LCRIX": ("TAXABLE", "wrapper: 91.7% Leuthold Core ETF (US equity) "
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"+ 8% money market"),
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}
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SPLIT = HERE / "taxsplit_results.json"
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APPR_SHARE_MIN = 0.50 # >= half of the 5y return is price appreciation
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def _splits() -> dict:
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"""flattened taxsplit results: sym -> split dict (all groups)."""
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if not SPLIT.exists():
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return {}
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d = json.loads(SPLIT.read_text())
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out: dict = {}
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for grp in d.values():
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for s, v in grp.items():
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if v:
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out.setdefault(s.upper(), v)
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return out
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def finalize(sym: str, r: dict, splits: dict | None = None) -> dict:
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s = (splits or _splits()).get(sym.upper())
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# an ACCUMULATOR: most of its return is price appreciation, realized
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# as the INVESTOR'S own LTCG on a >1y sale (the distributions are
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# small, so the annual ordinary/STCG drag is small too). That is a
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# taxable-account profile even when the distribution character is
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# murky.
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if (s and s.get("appr_share") is not None
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and s["appr_share"] >= APPR_SHARE_MIN
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and r["location"] in ("IRA", "MIXED (check 1099)")):
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r["location"] = "TAXABLE (accrues)"
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r["notes"] = (f"{s['appr_share']*100:.0f}% of 5y return is price "
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f"appreciation (only {s['payout_12m']*100:.1f}% "
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f"payout in the last 12m) - the gain is YOURS on a "
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">1y sale, at the LTCG rate. " + r["notes"]).strip(" ;")
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if sym.upper() in MANUAL:
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loc, note = MANUAL[sym.upper()]
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r["location"] = loc
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r["score"] = 1.0 if loc == "TAXABLE" else 0.0
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r["basis"] = (r["basis"] + "+manual").lstrip("+")
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r["notes"] = (note + "; " + r["notes"]).strip("; ")
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return r
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# ------------------------------------------------------------------------
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def run() -> dict:
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# the appreciation split must exist before finalize() can use it
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if not SPLIT.exists():
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from fundlab import taxsplit
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taxsplit.run()
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# sleeve loadings as a fallback for groups whose N-PORT parse left
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# the book mostly unclassified. The 16-fund shortlist was never in
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# the 2,384 screen, so compute + cache betas for those on demand.
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fac = json.loads(FACTORS.read_text())
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extra = (json.loads(EXTRA_BETAS.read_text())
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if EXTRA_BETAS.exists() else {})
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def betas_of(sym: str) -> dict:
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v = fac.get(sym.lower()) or fac.get(sym.upper()) or {}
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b = (v.get("full") or {}).get("betas")
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if b:
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return b
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key = sym.lower()
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if key in extra:
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return extra[key]
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from fundlab import factors
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r = factors.factor_screen(sym)
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if r and r.get("full"):
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extra[key] = r["full"]["betas"]
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EXTRA_BETAS.parent.mkdir(exist_ok=True)
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EXTRA_BETAS.write_text(json.dumps(extra, indent=1))
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return extra.get(key) or {}
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# 16-fund shortlist (N-PORT keyword buckets + names from funds.json)
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funds = json.loads((HERE.parent / "funds.json").read_text())
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shortlist: dict = {}
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dr = json.loads(SHORTLIST.read_text())
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for sym in dr:
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snap = json.loads((HERE / "nport_cache" / f"{sym}.json").read_text())
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name = (funds.get(sym.lower()) or {}).get("name") or sym.upper()
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r = classify(name, buckets=snap.get("buckets") or None,
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betas=betas_of(sym))
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r["as_of"] = snap.get("as_of", "")
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shortlist[sym.upper()] = finalize(sym, r)
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# 22 cross-checked (precise code-based buckets)
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xcheck: dict = {}
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xc = json.loads(XCHECK.read_text())
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for sym, v in xc.items():
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r = classify(v.get("name") or sym.upper(),
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buckets=v.get("buckets") or None,
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betas=betas_of(sym))
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r["as_of"] = v.get("as_of", "")
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xcheck[sym.upper()] = finalize(sym, r)
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# 250 candidates (sleeve loadings + name)
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cands: dict = {}
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for sym, v in fac.items():
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if not (v.get("verdict") or "").startswith("CANDIDATE"):
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continue
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betas = (v.get("full") or {}).get("betas") or {}
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cands[sym.upper()] = finalize(sym, classify(v.get("name") or "",
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betas=betas))
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res = {"shortlist": shortlist, "xcheck": xcheck, "candidates": cands}
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RESULTS.write_text(json.dumps(res, indent=1))
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_print(res)
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return res
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def _print(res: dict) -> None:
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from collections import Counter
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def row(s: str, r: dict) -> str:
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return (f" {s:<7} {r['location']:<20} {r['score']:<5.2f} "
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f"{r['basis']:<14} {r['name'][:34]:<34} {r['notes'][:64]}")
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hdr = (f" {'fund':<7} {'location':<20} score basis "
|
|
f"{'name':<34} notes")
|
|
for key, title in (("shortlist", "16-fund shortlist"),
|
|
("xcheck", "22 cross-checked")):
|
|
funds = res[key]
|
|
print(f"\n== {title} ({len(funds)}) ==")
|
|
print(hdr)
|
|
for s, r in sorted(funds.items()):
|
|
print(row(s, r))
|
|
|
|
c = res["candidates"]
|
|
locs = Counter(r["location"] for r in c.values())
|
|
print(f"\n== 250 candidates: {dict(locs)} ==")
|
|
sections = [
|
|
("TAXABLE - equity character (by score)",
|
|
lambda r: r["location"] == "TAXABLE", -18, 18),
|
|
("TAXABLE - munis (sample)",
|
|
lambda r: r["location"] == "TAXABLE (munis)", 0, 12),
|
|
("MIXED - check the 1099 (by score)",
|
|
lambda r: r["location"] == "MIXED (check 1099)", -12, 12),
|
|
("IRA - most ordinary income (by score)",
|
|
lambda r: r["location"] == "IRA", 12, 12),
|
|
("FLEXIBLE - cash",
|
|
lambda r: r["location"] == "FLEXIBLE (cash)", 0, 8),
|
|
]
|
|
for title, pick, sortkey, limit in sections:
|
|
sel = [(s, r) for s, r in c.items() if pick(r)]
|
|
if sortkey:
|
|
sel.sort(key=lambda kv: (sortkey * kv[1]["score"], kv[0]))
|
|
else:
|
|
sel.sort(key=lambda kv: kv[0])
|
|
print(f"\n -- {title} ({len(sel)})")
|
|
print(hdr)
|
|
for s, r in sel[:limit]:
|
|
print(row(s, r))
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
run()
|