Tax engine: fix loss-pool corruption, wash double-subtract, progressive keep
- liquidation is now a pure function of state: the daily mark-to-market netting no longer consumes the carried loss pools or the open wash-sale candidates (unrealized gains/losses were leaking into the carryover pools, so equity[t] depended on past unrealized values) - loss offsetting is now global (chronological, by type) instead of per-symbol: a symbol A gain consuming a symbol B loss marks B's event consumed, so a later B repurchase cannot subtract the same loss a second time (pools could go negative -> over-taxed gains) - progressive mode: the reinvested after-tax remainder now uses the SAME marginal rates the tax is recorded at (was flat d_keep/c_keep, so recorded tax and cash flow diverged); the bracket position also advances by net short-term gains - renames: WASh->WASH, lt_rf/st_rf->lt_fed/st_fed (rf is the project's risk-free-rate convention); buys pruned to the 30d wash window; dropped the per-call sorted() on the constant bracket tables tests: wash-sale disallowance; liquidation purity (no-trade day with unrealized gains must not eat the pool); cross-symbol consumption (no double subtract). All scenarios verified to fail on the pre-fix code.
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parent
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174
tax.py
174
tax.py
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@ -39,11 +39,14 @@ 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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later gains; repurchases within WASH_WINDOW_DAYS of a loss sale of the
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same security disallow the loss (wash sale). The daily liquidation is a
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PURE function of the simulated state (it never consumes loss pools or
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wash-sale candidates). Progressive federal brackets are used when
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`progressive` is on (marginal rate at the investor's taxable income
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position, starting at `fed_base`, less the `salt` deduction, advanced by
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dividend income and net short-term gains); otherwise the flat lt/st/div
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rates are used.
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"""
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from __future__ import annotations
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@ -54,7 +57,7 @@ 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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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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@ -71,7 +74,7 @@ FED_BRACKETS_LT: list[tuple[float, float]] = [
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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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for limit, r in brackets: # brackets are sorted by limit
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if income > limit:
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rate = r
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return rate
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@ -83,11 +86,28 @@ def _loss_wash_disallowed(i: int, ts: pd.Timestamp,
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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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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 _LossEvent:
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"""A realized loss still open to wash-sale disallowance.
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`loss` is the REMAINING (not-yet-offset) amount; `consumed` means it
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was fully offset by a later gain and can no longer be disallowed.
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Losses net globally (not per security), so offsetting is matched in
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chronological order by type across all symbols - a symbol A gain
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consuming a symbol B loss marks B's event consumed, so a later B
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repurchase must not subtract it a second time."""
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sym: int
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loss: float
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ts: pd.Timestamp
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is_lt: bool
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consumed: bool = False
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@dataclass
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class _Lot:
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units: float
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@ -152,10 +172,6 @@ def after_tax_portfolio(close: pd.DataFrame, div: pd.DataFrame, capg: pd.DataFra
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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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@ -172,32 +188,29 @@ def after_tax_portfolio(close: pd.DataFrame, div: pd.DataFrame, capg: pd.DataFra
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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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# realized losses still open to a wash-sale disallowance (chronological)
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open_losses: list[_LossEvent] = []
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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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def _mark_losses_consumed(is_lt: bool, amount: float) -> None:
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"""Mark up to `amount` of the unconsumed losses of the given type
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as offset by a gain, in chronological order (any symbol - losses
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net globally). Offset losses must no longer be wash-sale
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candidates."""
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if 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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for e in open_losses:
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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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if e.consumed or e.is_lt != is_lt or e.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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take = min(e.loss, remaining)
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e.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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if e.loss <= 0:
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e.consumed = True
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open_losses[:] = [e for e in open_losses if not e.consumed]
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# state (account = 1.0 at start)
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units = w0 / pv[0]
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@ -225,12 +238,16 @@ def after_tax_portfolio(close: pd.DataFrame, div: pd.DataFrame, capg: pd.DataFra
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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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# the rates used to REINVEST must equal the rates the tax is
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# recorded at (they diverge in progressive mode)
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d_rate = _fed("ord", div_rate) + niit + sl_rate
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c_rate = _fed("ltcg", lt_rate) + niit + sl_rate
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d_tax = float(dinc.sum() * d_rate)
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c_tax = float(cinc.sum() * c_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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d_keep = max(0.0, 1.0 - d_rate)
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c_keep = max(0.0, 1.0 - c_rate)
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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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@ -242,8 +259,8 @@ def after_tax_portfolio(close: pd.DataFrame, div: pd.DataFrame, capg: pd.DataFra
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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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lt_fed = _fed("ltcg", lt_rate)
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st_fed = _fed("ord", st_rate)
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if progressive:
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taxable_income += float(dinc.sum())
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@ -269,7 +286,7 @@ def after_tax_portfolio(close: pd.DataFrame, div: pd.DataFrame, capg: pd.DataFra
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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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r_eff = (lt_fed if li == 1 else st_fed) + 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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@ -277,22 +294,25 @@ def after_tax_portfolio(close: pd.DataFrame, div: pd.DataFrame, capg: pd.DataFra
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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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# gain -> those losses are no longer wash
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# candidates
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_mark_losses_consumed(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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# remainder stays in the pool
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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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_mark_losses_consumed(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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if progressive and li == 2:
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taxable_income += max(net, 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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@ -303,8 +323,8 @@ def after_tax_portfolio(close: pd.DataFrame, div: pd.DataFrame, capg: pd.DataFra
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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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open_losses.append(
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_LossEvent(i, loss, idx[t], li == 1))
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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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@ -316,61 +336,49 @@ def after_tax_portfolio(close: pd.DataFrame, div: pd.DataFrame, capg: pd.DataFra
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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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while buys[i] and \
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(idx[t] - buys[i][0]).days > WASH_WINDOW_DAYS:
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buys[i].pop(0)
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# wash sale: a repurchase within WASH_WINDOW_DAYS
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# after a loss sale of the same security disallows
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# that loss (only its remaining, unoffset amount)
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disallowed = [e for e in open_losses
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if not e.consumed and e.sym == i
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and e.ts < idx[t]
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and (idx[t] - e.ts).days <= WASH_WINDOW_DAYS]
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if disallowed:
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for e in disallowed:
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if e.is_lt:
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lt_pool -= e.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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st_pool -= e.loss
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open_losses[:] = [e for e in open_losses
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if e not in disallowed]
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# --- tax owed if we liquidate everything today ------------------
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# PURE: netting against the carried pools is done on local copies
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# only - the liquidation is counterfactual and must never consume
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# the pools or the open (wash-sale-eligible) losses.
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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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lt_gain += gain
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else:
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sym_lt_loss += -gain
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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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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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st_loss += -gain
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lt_net = lt_gain - min(lt_gain, lt_loss + lt_pool) if lt_gain > 0 else 0.0
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st_net = st_gain - min(st_gain, st_loss + st_pool) if st_gain > 0 else 0.0
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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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lt_fed + niit + sl_rate,
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st_fed + 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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@ -187,6 +187,64 @@ def main() -> int:
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- (float(r7c.realized["st_gain"][d7[2]])
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- float(r7c.realized["st_loss"][d7[1]])) * 0.15) < 1e-9)
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# --- wash sale: a loss sale of `a` on day 3, then `a` is repurchased
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# on day 4 (within 30d) -> the carried loss is disallowed and the day-5
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# gain is taxed in FULL (no offset).
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dws = pd.to_datetime(["2024-01-0%d" % i for i in (1, 2, 3, 4, 5)] +
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["2024-01-08"])
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close_ws = pd.DataFrame({"a": [10.0, 10.0, 12.0, 9.0, 9.0, 12.0],
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"b": [10.0, 5.0, 5.0, 4.0, 3.0, 3.0]}, index=dws)
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zero_ws = pd.DataFrame(0.0, index=dws, columns=["a", "b"])
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rws = tax.after_tax_portfolio(close_ws, zero_ws, zero_ws,
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{"a": 0.5, "b": 0.5}, rebalance="1D",
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lt_rate=0.20, st_rate=0.15, div_rate=0.10)
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check("wash sale: repurchase within 30d disallows the carried loss",
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float(rws.realized["st_loss"][dws[3]]) > 0
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and float(rws.realized["st_gain"][dws[5]]) > 0
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and abs(float(rws.taxes["realized_tax"][dws[5]])
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- float(rws.realized["st_gain"][dws[5]]) * 0.15) < 1e-9)
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# --- liquidation is pure: day 2 realizes an ST loss into the pool;
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# day 3 is a NO-TRADE day (all symbols up 1.3x proportionally) with big
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# UNREALIZED gains - the liquidation math must not consume the pool;
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# day 4's realized gain of `a` is still offset exactly by the carried
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# loss (old code under-taxed it: the pool had been eaten by the marks).
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dlp = pd.to_datetime(["2024-01-0%d" % i for i in (1, 2, 3, 4)])
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clp = pd.DataFrame({"a": [10.0, 5.0, 6.5, 14.0],
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"b": [10.0, 8.0, 10.4, 10.4],
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"c": [10.0, 10.0, 13.0, 13.0]}, index=dlp)
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zero_lp = pd.DataFrame(0.0, index=dlp, columns=["a", "b", "c"])
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rlp = tax.after_tax_portfolio(clp, zero_lp, zero_lp,
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{"a": 1 / 3, "b": 1 / 3, "c": 1 / 3},
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rebalance="1D", lt_rate=0.20, st_rate=0.15,
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div_rate=0.10)
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check("liquidation is pure: unrealized gains do not consume the pool",
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float(rlp.realized["st_loss"][dlp[1]]) > 0
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and float(rlp.realized["st_gain"][dlp[3]]) > 0
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and abs(float(rlp.taxes["realized_tax"][dlp[3]])
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- (float(rlp.realized["st_gain"][dlp[3]])
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- float(rlp.realized["st_loss"][dlp[1]])) * 0.15) < 1e-9)
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# --- global loss offset vs per-symbol wash bookkeeping: b's loss (day 2)
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# is offset by a's gain (day 3, `a` is processed first) -> b's loss
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# event is consumed, so the b repurchase on day 3 must NOT subtract the
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# loss a second time. Day 4's b gain is therefore taxed in full
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# (no phantom extra offset from a negative pool).
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dcs = pd.to_datetime(["2024-01-0%d" % i for i in (1, 2, 3, 4)])
|
||||
ccs = pd.DataFrame({"a": [10.0, 6.0, 15.0, 10.0],
|
||||
"b": [10.0, 8.0, 6.0, 12.0],
|
||||
"c": [10.0, 6.0, 6.0, 10.0]}, index=dcs)
|
||||
zero_cs = pd.DataFrame(0.0, index=dcs, columns=["a", "b", "c"])
|
||||
rcs = tax.after_tax_portfolio(ccs, zero_cs, zero_cs,
|
||||
{"a": 1 / 3, "b": 1 / 3, "c": 1 / 3},
|
||||
rebalance="1D", lt_rate=0.20, st_rate=0.15,
|
||||
div_rate=0.10)
|
||||
check("cross-symbol offset marks the loss consumed (no double subtract)",
|
||||
float(rcs.realized["st_loss"][dcs[1]]) > 0
|
||||
and float(rcs.realized["st_gain"][dcs[3]]) > 0
|
||||
and abs(float(rcs.taxes["realized_tax"][dcs[3]])
|
||||
- float(rcs.realized["st_gain"][dcs[3]]) * 0.15) < 1e-9)
|
||||
|
||||
print(f"\n{PASS} passed, {FAIL} failed")
|
||||
return 1 if FAIL else 0
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user