Greg Pomerantz
9f657dc9f4
The full rate stack (federal + NIIT + state) was used for the recorded
tax columns and for realized gains at rebalances, but the equity path
itself deducted only the FEDERAL rate in two places:
1. distribution reinvestment kept (1 - div_rate)/(1 - lt_rate) instead of
(1 - div_rate - niit - sl_rate);
2. the per-date liquidation tax ('sell everything today') passed bare
(lt_rate, st_rate) to _liquidation_tax.
Symptom: FLCSX 10Y showed a 1.4pt after-tax drag instead of the true
~2.8pt. Fix: d_keep/c_keep factors and stacked liquidation rates.
Regression tests pin the equity path: reinvested net with NIIT+state,
ST and LT liquidation tax at the full stack (all fail on the old code).
Corrected 2015-2026 NYC after-tax: SPY 13.82->11.23, IVV 13.81->11.22,
JLPSX 13.68->9.18 (previously reported 12.51/12.50/11.31, superseded).
158 lines
7.7 KiB
Python
158 lines
7.7 KiB
Python
"""Unit tests for the after-tax model (tax.py) — synthetic, no data bundle.
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Key regression: distributions are RAW per-share dollars, so the account
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must hold RAW share units (valued on close prices). The old code valued
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units on ADJ (total-return index) prices, which overstates every
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distribution — and its tax — by the raw/adj ratio (e.g. a fund that has
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paid many distributions: adj 11 vs raw 30 -> 2.7x overcharge).
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Run: .venv/bin/python tests/test_tax.py
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import pandas as pd
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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import tax # noqa: E402
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PASS, FAIL = 0, 0
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def check(name: str, ok: bool) -> None:
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global PASS, FAIL
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PASS += bool(ok)
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FAIL += not ok
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print((" ok " if ok else " FAIL ") + name)
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def main() -> int:
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# One fund, raw prices 30 -> 34, ex-div 5.00 on day 6 (34 -> 29),
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# then 29.5. Account starts at 1.0 = 1/30 share.
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dates = pd.to_datetime(["2024-01-0%d" % i for i in range(1, 8)])
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close = pd.DataFrame({"f": [30.0, 31.0, 32.0, 33.0, 34.0, 29.0, 29.5]},
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index=dates)
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div = pd.DataFrame({"f": [0, 0, 0, 0, 0, 0.0, 0.0]}, index=dates)
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capg = pd.DataFrame({"f": [0, 0, 0, 0, 0, 5.0, 0.0]}, index=dates)
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r = tax.after_tax_portfolio(close, div, capg, {"f": 1.0}, lt_rate=0.20,
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st_rate=0.15, div_rate=0.15)
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t5 = dates[5]
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# --- the regression: tax on the distribution is 20% of 5/30 of the
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# account (the distribution as a fraction of the RAW price), i.e.
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# 3.333% — not 5/adj (which the old code implied, ~4% with this
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# adj path and up to 2.7x for high-history funds).
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check("cap-gain tax is 20% of (5/30) of the account",
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abs(r.taxes["capg_tax"][t5] - 0.2 * 5.0 / 30.0) < 1e-12)
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check("no dividend tax", r.taxes["div_tax"].abs().sum() == 0.0)
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# --- reinvestment: the after-tax remainder (5*0.8 of the 1/30 share)
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# is bought at the ex-div close 29, so MV on the event day is
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# (1/30)*(29 + 4) = 33/30. The tax is already inside the (smaller)
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# units — it must NOT also show up in equity as a second deduction.
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mv5 = r.equity[t5] + r.liq_tax[t5] # no cash component on this day
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check("market value on ex-div day = 33/30 (after-tax reinvested)",
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abs(mv5 - 33.0 / 30.0) < 1e-9)
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# --- liquidation value: price drop (5/30) is exactly offset by the
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# distribution kept, so equity falls by the true tax cost only.
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# Day-5 equity = MV(33/30) - liq_tax(0: basis 1+4/30 > MV, a "loss"
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# under the after-tax basis rule).
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check("equity on ex-div day = 33/30 (tax cost lives in the units)",
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abs(r.equity[t5] - 33.0 / 30.0) < 1e-9)
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# --- pre-event day: plain ST-marked liquidation value.
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t4 = dates[4]
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check("equity day before = MV - 15% ST gain tax",
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abs(r.equity[t4] - (34.0 / 30.0) * (1 - 0.15 * (4.0 / 34.0))) < 1e-9)
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# --- cross-day consistency: equity(5)/equity(4) from the explicit
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# values (day 4 = 34/30 MV less 15% ST gain tax; day 5 = 33/30).
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check("ex-div equity step matches explicit values",
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abs(r.equity[t5] / r.equity[t4]
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- (33.0 / 30.0) / (34.0 / 30.0 - 0.15 * 4.0 / 30.0)) < 1e-9)
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# --- no rebalancing: one lot survives, units grew by the reinvested
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# net amount at the ex-div price.
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check("single lot outstanding", r.lots_outstanding == 1)
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# --- dividend leg taxed at div_rate, cap-gain leg at lt_rate:
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# split a 1.00 distribution 0.4 div / 0.6 capg on day 6.
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div2 = pd.DataFrame({"f": [0, 0, 0, 0, 0, 0.4, 0.0]}, index=dates)
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capg2 = pd.DataFrame({"f": [0, 0, 0, 0, 0, 0.6, 0.0]}, index=dates)
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r2 = tax.after_tax_portfolio(close, div2, capg2, {"f": 1.0}, lt_rate=0.20,
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st_rate=0.15, div_rate=0.10)
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check("split distribution taxed by component rate",
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abs(r2.taxes["capg_tax"][t5] - 0.20 * 0.6 / 30.0) < 1e-12
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and abs(r2.taxes["div_tax"][t5] - 0.10 * 0.4 / 30.0) < 1e-12)
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# --- state/local + NIIT: composite rates add to each component
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# (NY/NYC tax cap gains at ordinary rates — the caller passes the
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# composite sl_rate; the model must not preferentiate cap gains).
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r3 = tax.after_tax_portfolio(close, div2, capg2, {"f": 1.0}, lt_rate=0.20,
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st_rate=0.15, div_rate=0.10, niit=0.038,
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sl_rate=0.14)
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check("capg dist taxed at lt + NIIT + state/local",
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abs(r3.taxes["capg_tax"][t5] - (0.20 + 0.038 + 0.14) * 0.6 / 30.0) < 1e-12)
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check("div taxed at div + NIIT + state/local",
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abs(r3.taxes["div_tax"][t5] - (0.10 + 0.038 + 0.14) * 0.4 / 30.0) < 1e-12)
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# realized-gain leg: two symbols, daily rebalance forces ST sells;
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# every realized dollar is taxed at (st_rate + niit + sl_rate).
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d3 = pd.to_datetime(["2024-01-0%d" % i for i in (1, 2, 3)])
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close3 = pd.DataFrame({"a": [30.0, 34.0, 29.0], "b": [10.0, 10.0, 10.0]},
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index=d3)
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zero3 = pd.DataFrame(0.0, index=d3, columns=["a", "b"])
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r4 = tax.after_tax_portfolio(close3, zero3, zero3, {"a": 0.5, "b": 0.5},
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rebalance="1D", lt_rate=0.20, st_rate=0.15,
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div_rate=0.10, niit=0.038, sl_rate=0.14)
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real_tax = float(r4.taxes["realized_tax"].sum())
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net_st = float(r4.realized["st_gain"].sum()) - \
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float(r4.realized["st_loss"].sum())
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check("realized ST gains/losses net-taxed at st + NIIT + state/local",
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float(r4.realized["st_gain"].sum()) > 0 and
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abs(real_tax - net_st * (0.15 + 0.038 + 0.14)) < 1e-9)
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# --- EQUITY PATH (not just recorded taxes) with the full stack:
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# the reinvested net must deduct (federal + NIIT + state), and the
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# liquidation tax on each date must use the same stacked rates.
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# A: cap-gain dist 5.0 on a 30-priced fund; keep = 1-0.20-0.038-0.14
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zero_d = pd.DataFrame(0.0, index=dates, columns=["f"])
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r5 = tax.after_tax_portfolio(close, zero_d, capg, {"f": 1.0},
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lt_rate=0.20, st_rate=0.15, div_rate=0.10,
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niit=0.038, sl_rate=0.14)
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keep = 1.0 - 0.20 - 0.038 - 0.14
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mv5 = float(r5.equity[t5]) + float(r5.liq_tax[t5])
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check("reinvested net deducts NIIT + state (MV = 29/30 + net/30)",
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abs(mv5 - (29.0 / 30.0 + (5.0 / 30.0) * keep)) < 1e-9)
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# B: sale tax on each calculation date — ST window (2 days)
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dlt = pd.to_datetime(["2024-01-01", "2024-01-02"])
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c2 = pd.DataFrame({"f": [30.0, 34.0]}, index=dlt)
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r6 = tax.after_tax_portfolio(c2, pd.DataFrame(0.0, index=dlt, columns=["f"]),
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pd.DataFrame(0.0, index=dlt, columns=["f"]),
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{"f": 1.0}, lt_rate=0.20, st_rate=0.15,
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div_rate=0.10, niit=0.038, sl_rate=0.14)
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check("liquidation tax (ST) uses st + NIIT + state",
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abs(float(r6.equity[dlt[1]])
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- (34.0 / 30.0 - (0.15 + 0.038 + 0.14) * 4.0 / 30.0)) < 1e-9)
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# B2: ...and the LT leg
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dlt2 = pd.to_datetime(["2022-01-03", "2024-01-02"])
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c2b = pd.DataFrame({"f": [30.0, 34.0]}, index=dlt2)
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r7 = tax.after_tax_portfolio(c2b, pd.DataFrame(0.0, index=dlt2,
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columns=["f"]),
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pd.DataFrame(0.0, index=dlt2, columns=["f"]),
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{"f": 1.0}, lt_rate=0.20, st_rate=0.15,
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div_rate=0.10, niit=0.038, sl_rate=0.14)
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check("liquidation tax (LT) uses lt + NIIT + state",
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abs(float(r7.equity[dlt2[1]])
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- (34.0 / 30.0 - (0.20 + 0.038 + 0.14) * 4.0 / 30.0)) < 1e-9)
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print(f"\n{PASS} passed, {FAIL} failed")
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return 1 if FAIL else 0
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if __name__ == "__main__":
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sys.exit(main())
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