Greg Pomerantz
19b33d5a18
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.
232 lines
9.9 KiB
Python
232 lines
9.9 KiB
Python
"""After-tax portfolio value: "what do I keep if I sell everything today?"
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Model (simplified DAS)
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----------------------
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The account starts at 1.0 (growth-ratio units; no fixed capital).
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- Holdings are valued on RAW (close) prices, in raw share units.
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Distributions (per-share dollars from the event files) are taxed on
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their event date and the after-tax remainder is REINVESTED at the same
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day's raw close (the ex-div price): a fund paying d drops by d, the
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investor keeps d(1-tau) and buys it back cheaper. The tax's effect is
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carried by the (smaller) reinvested units; it is recorded in `taxes`
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but not separately deducted from cash (that would double-count it).
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dividend income -> taxed at (div_rate + niit + sl_rate)
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capital gain dist-> taxed at (lt_rate + niit + sl_rate)
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`niit` = federal 3.8% Net Investment Income Tax (0 if MAGI is under
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the threshold). `sl_rate` = STATE+LOCAL marginal rate (e.g. NY+NYC)
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applied at ORDINARY rates to every flow: state and local have no
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preferential capital-gains rate, so a NYC resident pays city + state
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on top of the federal rate for the same dollar. The after-tax
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remainder also increases each lot's cost basis proportionally
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(standard after-tax-IRR convention, so a liquidation doesn't re-tax
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the distribution).
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- On rebalance, sells are FIFO. A lot is long-term if held more than
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365 days at sale, else short-term; realized gains/losses are taxed at
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`lt_rate` / `st_rate`.
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- THE headline number, `equity[t]`, is the after-tax value if you
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LIQUIDATE the entire account on day t:
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equity[t] = market value + cash - tax_on_unrealized_gains(t)
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where tax_on_unrealized_gains marks every remaining lot to market,
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classifies it LT/ST by age, nets losses against gains, and applies the
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rates. This is what you would actually have in your pocket after
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selling everything and filing your taxes.
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NOTE: distribution event dates must be EX-DIV dates for the reinvestment
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pricing to be right (the raw close on that day already reflects the
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drop). Yahoo usually dates events on the ex-div date; the record-date
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misalignments found by scripts/scan_adj_misalign.py are corrected by
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remove/add ops in overrides/corrections/ where a symbol is analyzed.
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Deliberately NOT modeled: loss carryover, wash sales, progressive
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brackets (flat marginal rates), SALT-deduction interaction.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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import numpy as np
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import pandas as pd
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ST_WINDOW_DAYS = 365
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@dataclass
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class _Lot:
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units: float
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cost: float
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date: pd.Timestamp
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@dataclass
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class TaxResult:
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equity: pd.Series # after-tax value if you sell everything today (ratio)
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liq_tax: pd.Series # tax that a full liquidation today would owe (unrealized)
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taxes: pd.DataFrame # daily taxes actually PAID: div_tax, capg_tax, realized_tax, total
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realized: pd.DataFrame # daily realized (at rebalances): lt_gain, st_gain, lt_loss, st_loss
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lots_outstanding: int # final lot count (sanity check)
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def _liquidation_tax(lt_gain: float, lt_loss: float,
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st_gain: float, st_loss: float,
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lt_rate: float, st_rate: float) -> float:
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"""Tax on selling all lots now; losses offset gains (same/other type)."""
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lt_net = lt_gain - lt_loss
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st_net = st_gain - st_loss
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if lt_net >= 0 and st_net >= 0:
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return lt_net * lt_rate + st_net * st_rate
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if lt_net < 0 and st_net < 0:
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return 0.0
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if st_net < 0: # ST losses offset LT gains
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return max(lt_net + st_net, 0.0) * lt_rate
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return max(st_net + lt_net, 0.0) * st_rate
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def after_tax_portfolio(close: pd.DataFrame, div: pd.DataFrame, capg: pd.DataFrame,
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weights: dict[str, float],
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rebalance: str | None = None, cost_bps: float = 0.0,
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lt_rate: float = 0.20, st_rate: float = 0.15,
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div_rate: float = 0.15, niit: float = 0.0,
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sl_rate: float = 0.0,
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start: str | None = None, end: str | None = None
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) -> TaxResult:
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"""`close` must be RAW close prices (bundle.close), not adj: the
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per-share distribution dollars are raw, so units must be raw
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shares. All rates are decimals; realized gains are taxed at
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(st/lt_rate + niit + sl_rate)."""
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syms = [s for s in weights if s in close.columns]
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p = close[syms]
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d = div[[s for s in syms if s in div.columns]].reindex(index=p.index, columns=syms).fillna(0.0)
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c = capg[[s for s in syms if s in capg.columns]].reindex(index=p.index, columns=syms).fillna(0.0)
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if start or end:
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p = p.loc[start:end]
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p = p.dropna()
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d, c = d.reindex(p.index).fillna(0.0), c.reindex(p.index).fillna(0.0)
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if len(p) < 2:
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raise ValueError("no overlapping data for the given symbols/period")
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idx = p.index
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pv = p.values
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dv = d.values
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cv = c.values
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n = len(p)
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w0 = np.array([weights[s] for s in syms])
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w0 = w0 / w0.sum()
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cost = cost_bps / 1e4
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# state (account = 1.0 at start)
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units = w0 / pv[0]
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lots: list[list[_Lot]] = [[_Lot(units[i], float(units[i] * pv[0][i]), idx[0])]
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if units[i] > 0 else [] for i in range(len(syms))]
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cash = -float(w0.sum() * cost) # initial purchase cost
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tax_cols = ["div_tax", "capg_tax", "realized_tax", "total"]
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tax_rows = np.zeros((n, len(tax_cols)))
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real_cols = ["lt_gain", "st_gain", "lt_loss", "st_loss"]
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real_rows = np.zeros((n, len(real_cols)))
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equity = np.empty(n)
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liq_tax = np.zeros(n)
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# rebalance schedule (skip first day)
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from portfolio import _rebalance_dates
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rebal = (_rebalance_dates(idx, rebalance) - {idx[0]}) if rebalance else set()
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for t in range(n):
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prices = pv[t]
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# --- distributions: pay tax, reinvest the after-tax remainder at
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# today's close. The tax is recorded in tax_rows but NOT also
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# deducted from cash: it is already reflected in the (smaller)
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# reinvested units, so a second deduction would double-count it.
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dinc = units * dv[t]
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cinc = units * cv[t]
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d_tax = float(dinc.sum() * (div_rate + niit + sl_rate))
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c_tax = float(cinc.sum() * (lt_rate + niit + sl_rate))
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tax_rows[t, 0] = d_tax
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tax_rows[t, 1] = c_tax
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for i in range(len(syms)):
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net = (dv[t, i] * (1 - div_rate) + cv[t, i] * (1 - lt_rate)) * units[i]
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if net <= 0:
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continue
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px_i = float(prices[i])
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if px_i > 0:
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units[i] += net / px_i # reinvest the after-tax cash
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if lots[i]: # grow cost basis
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tot = sum(l.units for l in lots[i])
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for l in lots[i]:
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l.cost += net * (l.units / tot)
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market_value = float(np.dot(units, prices)) # post-reinvestment
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# --- rebalance to target weights --------------------------------
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if idx[t] in rebal:
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value = market_value
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target_val = w0 * value
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for i in range(len(syms)):
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cur_val = units[i] * prices[i]
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trade = target_val[i] - cur_val # + buy, - sell
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if abs(trade) < 1e-9:
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continue
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c_cost = cost * abs(trade)
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cash -= c_cost
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if trade < 0: # sell |trade| at price, FIFO
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to_sell = -trade / prices[i]
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for l in lots[i]:
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if to_sell <= 1e-12:
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break
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u = min(l.units, to_sell)
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gain = u * prices[i] - (l.cost * u / l.units)
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held = (idx[t] - l.date).days
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li = 1 if held > ST_WINDOW_DAYS else 2
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r_eff = (lt_rate if li == 1 else st_rate) + niit + sl_rate
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if gain >= 0:
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tax = gain * r_eff
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real_rows[t, li - 1] += gain
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else:
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tax = gain * r_eff
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real_rows[t, 3 if li == 1 else 2] += -gain
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cash -= tax
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tax_rows[t, 2] += tax
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l.units -= u
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l.cost *= (l.units / (l.units + u)) if l.units > 0 else 0.0
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to_sell -= u
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lots[i] = [l for l in lots[i] if l.units > 1e-12]
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units[i] = cur_val / prices[i] + trade / prices[i]
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else: # buy
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spend = trade + c_cost
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u = trade / prices[i]
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lots[i].append(_Lot(u, spend, idx[t]))
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units[i] += u
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# --- tax owed if we liquidate everything today ------------------
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lt_gain = lt_loss = st_gain = st_loss = 0.0
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for i in range(len(syms)):
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px = prices[i]
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for l in lots[i]:
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gain = l.units * px - l.cost
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if (idx[t] - l.date).days > ST_WINDOW_DAYS:
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if gain >= 0:
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lt_gain += gain
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else:
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lt_loss += -gain
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else:
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if gain >= 0:
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st_gain += gain
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else:
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st_loss += -gain
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liq_tax[t] = _liquidation_tax(lt_gain, lt_loss, st_gain, st_loss,
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lt_rate, st_rate)
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equity[t] = market_value + cash - liq_tax[t]
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tax_rows[t, 3] = tax_rows[t, 0] + tax_rows[t, 1] + tax_rows[t, 2]
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return TaxResult(
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equity=pd.Series(equity, index=idx, name="after_tax_liquidation"),
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liq_tax=pd.Series(liq_tax, index=idx, name="liquidation_tax"),
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taxes=pd.DataFrame(tax_rows, index=idx, columns=tax_cols),
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realized=pd.DataFrame(real_rows, index=idx, columns=real_cols),
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lots_outstanding=sum(len(l) for l in lots),
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)
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