Greg Pomerantz
2b7d545dc7
- loss pools: realized losses carry forward and offset later gains (net-of-losses taxation); liquidation taxes net of carried pools - wash sales: repurchase within 30d of a loss sale disallows the loss - progressive federal brackets (2025 MFJ): marginal LTCG/ordinary rates at investor's taxable position (fed_base - salt); flat rates otherwise - app: progressive / fed_base / salt sidebar inputs + settings persistence tests: new checks for progressive (incl. flat control), loss carryover
385 lines
17 KiB
Python
385 lines
17 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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Losses realized along the way are carried forward (loss pools) and offset
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later gains; repurchases within WASh_WINDOW_DAYS of a loss sale of the
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same security disallow the loss (wash sale). Progressive federal brackets
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are used when `progressive` is on (marginal rate at the investor's taxable
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income position, starting at `fed_base`, less the `salt` deduction);
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otherwise the flat lt/st/div rates are used.
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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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WASh_WINDOW_DAYS = 30
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# Federal ordinary / LTCG brackets (2025, MFJ) for the progressive mode:
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# marginal federal rate at a given taxable ordinary income. LTCG: 0% to
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# $96,700 / 15% to ~$599k / 20% above.
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FED_BRACKETS_ORD: list[tuple[float, float]] = [
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(0.0, 0.10), (23625.0, 0.12), (95350.0, 0.22), (206700.0, 0.24),
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(394600.0, 0.32), (684950.0, 0.35), (751600.0, 0.37),
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]
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FED_BRACKETS_LT: list[tuple[float, float]] = [
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(0.0, 0.00), (96700.0, 0.15), (599000.0, 0.20),
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]
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def fed_marginal_rate(income: float, brackets: list[tuple[float, float]]) -> float:
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"""Marginal federal rate at a given taxable ordinary income."""
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rate = brackets[0][1]
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for limit, r in sorted(brackets):
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if income > limit:
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rate = r
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return rate
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def _loss_wash_disallowed(i: int, ts: pd.Timestamp,
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buys: list[list[pd.Timestamp]]) -> bool:
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"""True if the security `i` was repurchased within WASh_WINDOW_DAYS
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before (or on) a loss sale on day `ts` - wash sale, loss disallowed."""
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for b in buys[i]:
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delta = ts - b
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if pd.Timedelta(0) <= delta <= pd.Timedelta(days=WASh_WINDOW_DAYS):
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return True
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return False
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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_net: float, st_net: 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 / st_net are net of realized losses (and carried loss pools)."""
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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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progressive: bool = False, fed_base: float = 0.0,
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salt: float = 0.0
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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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# full NYC-style stacks: distributions keep (1 - federal - NIIT - state);
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# a sale (liquidation) gains are taxed at (federal + NIIT + state)
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d_keep = max(0.0, 1.0 - div_rate - niit - sl_rate)
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c_keep = max(0.0, 1.0 - lt_rate - niit - sl_rate)
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# progressive federal brackets: when on, the federal rate for each
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# flow is the marginal rate at the investor's taxable income position
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# (starts at fed_base, less the SALT deduction); ordinary flows push
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# the position up. Flat lt/st/div rates are used when off.
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taxable_income = float(fed_base) - float(salt) if progressive else 0.0
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def _fed(kind: str, flat: float) -> float:
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if not progressive:
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return flat
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brackets = FED_BRACKETS_LT if kind == "ltcg" else FED_BRACKETS_ORD
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return fed_marginal_rate(taxable_income, brackets)
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# loss carryover pools: unused realized losses, offset by later gains
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lt_pool = 0.0
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st_pool = 0.0
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# open losses awaiting a possible wash sale: [loss, ts, is_lt, consumed]
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open_losses: dict[int, list[list]] = {}
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buys: list[list[pd.Timestamp]] = [[] for _ in syms]
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def _mark_losses_consumed(i: int, is_lt: bool, amount: float) -> None:
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"""Mark up to `amount` of this symbol's unconsumed losses (of the
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given type) as consumed - i.e. offset by a gain. Consumed losses
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must no longer be wash-sale candidates."""
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evs = open_losses.get(i)
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if not evs or amount <= 0:
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return
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remaining = amount
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for e in evs:
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if remaining <= 0:
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break
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loss, ts, lt, consumed = e
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if consumed or lt != is_lt or loss <= 0:
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continue
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take = min(loss, remaining)
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if take >= loss:
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e[3] = True
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remaining -= loss
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else:
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e[0] = loss - take
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remaining -= take
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open_losses[i] = [e for e in evs if not e[3]]
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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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fed_div = _fed("ord", div_rate) + niit + sl_rate
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fed_c = _fed("ltcg", lt_rate) + niit + sl_rate
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d_tax = float(dinc.sum() * fed_div)
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c_tax = float(cinc.sum() * fed_c)
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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] * d_keep + cv[t, i] * c_keep) * 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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lt_rf = _fed("ltcg", lt_rate)
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st_rf = _fed("ord", st_rate)
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if progressive:
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taxable_income += float(dinc.sum())
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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_rf if li == 1 else st_rf)) + niit + sl_rate
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if gain >= 0:
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pool = lt_pool if li == 1 else st_pool
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net = gain - pool
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if net > 0:
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tax = net * r_eff
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real_rows[t, li - 1] += gain
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# the whole carried pool was consumed by this
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# gain -> mark those losses as used
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_mark_losses_consumed(i, li == 1, pool)
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if li == 1:
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lt_pool = 0.0
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else:
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st_pool = 0.0
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else:
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# the gain is absorbed by carried losses; the
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# remainder is a loss
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real_rows[t, li - 1] += gain
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_mark_losses_consumed(i, li == 1, gain)
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if li == 1:
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lt_pool = pool - gain
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else:
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st_pool = pool - gain
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tax = 0.0
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cash -= tax
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tax_rows[t, 2] += tax
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else:
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loss = -gain
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real_rows[t, 2 if li == 1 else 3] += loss
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if not _loss_wash_disallowed(i, idx[t], buys):
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if li == 1:
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lt_pool += loss
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else:
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st_pool += loss
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open_losses.setdefault(i, []).append(
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[loss, idx[t], li == 1, False])
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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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buys[i].append(idx[t])
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# wash sale: a repurchase within WASh_WINDOW_DAYS after
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# a loss sale of the same security disallows that loss
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evs = open_losses.get(i)
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if evs:
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keep: list[list] = []
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for e in evs:
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loss, ts, is_lt, consumed = e
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if (not consumed) and ts < idx[t] and \
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(idx[t] - ts).days <= WASh_WINDOW_DAYS:
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if is_lt:
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lt_pool -= loss
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else:
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st_pool -= loss
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else:
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keep.append(e)
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open_losses[i] = keep
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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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sym_lt_gain = sym_lt_loss = sym_st_gain = sym_st_loss = 0.0
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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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sym_lt_gain += gain
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else:
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sym_lt_loss += -gain
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else:
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if gain >= 0:
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sym_st_gain += gain
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else:
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sym_st_loss += -gain
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sym_lt_avail = sym_lt_loss + lt_pool
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sym_lt_consumed = min(sym_lt_gain, sym_lt_avail) if sym_lt_gain > 0 else 0.0
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lt_gain += sym_lt_gain
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lt_loss += sym_lt_loss
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if sym_lt_consumed > 0:
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_mark_losses_consumed(i, True, sym_lt_consumed)
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sym_st_avail = sym_st_loss + st_pool
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sym_st_consumed = min(sym_st_gain, sym_st_avail) if sym_st_gain > 0 else 0.0
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st_gain += sym_st_gain
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st_loss += sym_st_loss
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if sym_st_consumed > 0:
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_mark_losses_consumed(i, False, sym_st_consumed)
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lt_avail = lt_loss + lt_pool
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lt_net = lt_gain - min(lt_gain, lt_avail) if lt_gain > 0 else 0.0
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lt_pool = lt_avail - min(lt_gain, lt_avail) if lt_gain > 0 else lt_pool
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st_avail = st_loss + st_pool
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st_net = st_gain - min(st_gain, st_avail) if st_gain > 0 else 0.0
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st_pool = st_avail - min(st_gain, st_avail) if st_gain > 0 else st_pool
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liq_tax[t] = _liquidation_tax(lt_net, st_net,
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lt_rf + niit + sl_rate,
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st_rf + niit + sl_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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