diff --git a/docs/api/index.md b/docs/api/index.md
index 28ca74b3..30b9fe31 100644
--- a/docs/api/index.md
+++ b/docs/api/index.md
@@ -161,6 +161,20 @@ Auto-generated from source docstrings.
     options:
       show_root_heading: true
 
+## Prediction-Market Fees
+
+::: ml4t.backtest.prediction_market_fees
+    options:
+      show_root_heading: true
+      members:
+        - kalshi_fee
+        - kalshi_order_fees
+        - forecastex_fee
+        - polymarket_us_fee
+        - KalshiCommission
+        - ForecastExCommission
+        - PolymarketUSCommission
+
 ## Results
 
 ::: ml4t.backtest.result.BacktestResult
diff --git a/docs/user-guide/market-impact.md b/docs/user-guide/market-impact.md
index 60bb5e3f..1d55c548 100644
--- a/docs/user-guide/market-impact.md
+++ b/docs/user-guide/market-impact.md
@@ -84,6 +84,66 @@ its opposite is charged once for its full quantity. The fee is allocated between
 and opening trade records in proportion to their quantities. Partial fills are
 charged separately when they execute.
 
+### Prediction-Market Venue Fees
+
+`ml4t.backtest.prediction_market_fees` implements the published fee schedules
+of Kalshi, ForecastEx (via IBKR) and Polymarket US. Prices are dollars per
+contract in `[0, 1]`; buying NO at `q` is a purchase at `q`.
+
+| Venue | Fee per fill | Model |
+|-------|--------------|-------|
+| Kalshi | taker `round_up(M * 0.07 * C * P * (1 - P))`; maker 0, 0.25x or 0.5x taker by series `fee_type` | `KalshiCommission` |
+| ForecastEx | 0.01 per contract per side; no IBKR commission | `ForecastExCommission` |
+| Polymarket US | taker `0.0695 * C * p * (1 - p)`; maker rebate `0.0125 * C * p * (1 - p)` | `PolymarketUSCommission` |
+
+Kalshi rounds the trade fee up to 0.000001. A non-direct member's balance is
+then aligned to whole cents, so one contract at 0.50 costs 0.02 instead of
+0.0175 while 100 contracts cost exactly 1.75; `rounding="cent"` reproduces
+this, and `kalshi_order_fees` adds the per-order rebate of accumulated
+overpayment. Polymarket US rounds each fill to the cent, half to even. The
+engine charges only non-negative fees, so `PolymarketUSCommission` charges a
+maker fill 0 and leaves out the rebate.
+
+Assign a model to the broker before running:
+
+```python
+from ml4t.backtest.prediction_market_fees import KalshiCommission
+
+engine.broker.commission_model = KalshiCommission(
+    liquidity="taker", fee_type="quadratic", fee_multiplier=1.0, rounding="cent"
+)
+```
+
+For research over many fills, the vectorized functions take NumPy arrays or
+Polars Series and return an array of fees in dollars:
+
+```python
+import polars as pl
+
+from ml4t.backtest.prediction_market_fees import kalshi_fee, polymarket_us_fee
+
+fills = pl.DataFrame(
+    {
+        "price": [0.50, 0.50, 0.90],
+        "contracts": [1, 100, 10],
+        "liquidity": ["taker", "taker", "maker"],
+    }
+)
+kalshi = kalshi_fee(
+    fills["price"],
+    fills["contracts"],
+    liquidity=fills["liquidity"],
+    fee_type="quadratic_with_maker_fees",
+    rounding="cent",
+)
+# array([0.02, 1.75, 0.02])
+polymarket = polymarket_us_fee(fills["price"], fills["contracts"], liquidity=fills["liquidity"])
+# array([ 0.017375,  1.7375  , -0.01125 ])
+```
+
+Each function's docstring names the venue documents it follows and the date
+they were read; schedules change, so check them before relying on a result.
+
 ## Slippage Models
 
 Slippage adjusts the configured execution price in the adverse direction. Use the spread model for a bar-only estimate of bid-ask crossing, or a percentage or fixed amount for other execution drag.
diff --git a/src/ml4t/backtest/prediction_market_fees.py b/src/ml4t/backtest/prediction_market_fees.py
new file mode 100644
index 00000000..4afb3f53
--- /dev/null
+++ b/src/ml4t/backtest/prediction_market_fees.py
@@ -0,0 +1,526 @@
+"""Trading-fee schedules for prediction-market venues.
+
+Each venue has a vectorized function for research over arrays of fills and a
+``CommissionModel`` for the engine:
+
+| Venue | Function | Model |
+|---|---|---|
+| Kalshi | `kalshi_fee`, `kalshi_order_fees` | `KalshiCommission` |
+| ForecastEx (via IBKR) | `forecastex_fee` | `ForecastExCommission` |
+| Polymarket US | `polymarket_us_fee` | `PolymarketUSCommission` |
+
+Prices are dollars per contract in ``[0, 1]`` and contracts pay 1.00 at
+settlement. Buying NO at price ``q`` is a purchase at ``q``; the quadratic
+formulas use ``q * (1 - q)``, which is symmetric, so a fee needs no YES/NO flag.
+Contracts are non-negative counts. ``side`` (``"buy"``/``"sell"``) matters only
+where a venue's rounding depends on the cash moved (Kalshi balance alignment).
+``liquidity`` is ``"taker"`` (the fill removed a resting order) or ``"maker"``
+(the fill executed a resting order).
+
+The vectorized functions accept scalars, NumPy arrays, Python sequences and
+Polars Series, broadcast them against each other and return a float64
+``numpy.ndarray`` of fees in dollars (0-d for all-scalar input).
+
+Fee figures were read from the venues' published schedules on 2026-10-08. They
+change; the module constants name the value in force on that date.
+"""
+
+from typing import Any, Literal
+
+import numpy as np
+
+__all__ = [
+    "FORECASTEX_EXCHANGE_FEE",
+    "KALSHI_MAKER_FEE_FACTORS",
+    "KALSHI_TAKER_RATE",
+    "POLYMARKET_US_MAKER_REBATE_COEFFICIENT",
+    "POLYMARKET_US_TAKER_COEFFICIENT",
+    "ForecastExCommission",
+    "KalshiCommission",
+    "KalshiFeeType",
+    "KalshiRounding",
+    "Liquidity",
+    "PolymarketUSCommission",
+    "PolymarketUSRounding",
+    "Side",
+    "forecastex_fee",
+    "kalshi_fee",
+    "kalshi_order_fees",
+    "polymarket_us_fee",
+]
+
+Liquidity = Literal["taker", "maker"]
+Side = Literal["buy", "sell"]
+KalshiFeeType = Literal["quadratic", "quadratic_with_maker_fees", "quadratic_with_combo_maker_fees"]
+KalshiRounding = Literal["exact", "cent", "direct"]
+PolymarketUSRounding = Literal["exact", "cent"]
+
+KALSHI_TAKER_RATE = 0.07
+"""Kalshi general trading fee rate: ``round_up(M * 0.07 * C * P * (1 - P))``."""
+
+KALSHI_MAKER_FEE_FACTORS: dict[str, float] = {
+    "quadratic": 0.0,
+    "quadratic_with_maker_fees": 0.25,
+    "quadratic_with_combo_maker_fees": 0.5,
+}
+"""Maker fee as a multiple of the taker fee, by Kalshi series ``fee_type``."""
+
+FORECASTEX_EXCHANGE_FEE = 0.01
+"""ForecastEx transaction fee per contract per side, in force from 2026-03-31."""
+
+POLYMARKET_US_TAKER_COEFFICIENT = 0.0695
+"""Polymarket US standard taker coefficient (theta), effective 2026-10-01."""
+
+POLYMARKET_US_MAKER_REBATE_COEFFICIENT = 0.0125
+"""Polymarket US maker rebate coefficient (magnitude of theta = -0.0125)."""
+
+_MICRO = 1_000_000.0
+_KALSHI_BALANCE_PRECISION_MICRO = {"cent": 10_000.0, "direct": 100.0}
+
+ArrayInput = Any
+
+
+# === Shared helpers ===
+
+
+def _floats(name: str, value: ArrayInput) -> np.ndarray:
+    array = np.asarray(value, dtype=np.float64)
+    if not np.all(np.isfinite(array)):
+        raise ValueError(f"{name} must be finite")
+    return array
+
+
+def _prices(value: ArrayInput) -> np.ndarray:
+    price = _floats("price", value)
+    if np.any((price < 0.0) | (price > 1.0)):
+        raise ValueError("price must be in [0, 1] dollars per contract")
+    return price
+
+
+def _contracts(value: ArrayInput) -> np.ndarray:
+    contracts = _floats("contracts", value)
+    if np.any(contracts < 0.0):
+        raise ValueError("contracts must be non-negative; pass the direction as side")
+    return contracts
+
+
+def _labels(name: str, value: ArrayInput, allowed: tuple[str, ...]) -> np.ndarray:
+    labels = np.asarray(value, dtype=np.str_)
+    unknown = sorted(set(np.unique(labels).tolist()) - set(allowed))
+    if unknown:
+        raise ValueError(f"{name} must be one of {allowed}, got {unknown}")
+    return labels
+
+
+def _snap(units: np.ndarray) -> np.ndarray:
+    """Remove floating-point noise from values that are integers in exact arithmetic.
+
+    ``0.07 * 100 * 0.25 * 1e6`` evaluates to ``1750000.0000000002``; ceiling it
+    would charge one micro-dollar too much. A value within ``1e-12`` relative of
+    an integer is treated as that integer.
+    """
+    nearest = np.rint(units)
+    tolerance = 1e-12 * np.maximum(np.abs(units), 1e3)
+    return np.where(np.abs(units - nearest) <= tolerance, nearest, units)
+
+
+def _side_from_quantity(quantity: float) -> Side:
+    return "buy" if quantity >= 0.0 else "sell"
+
+
+# === Kalshi ===
+
+
+def _kalshi_components(
+    price: ArrayInput,
+    contracts: ArrayInput,
+    liquidity: ArrayInput,
+    side: ArrayInput,
+    fee_type: ArrayInput,
+    fee_multiplier: ArrayInput,
+    rounding: str,
+) -> tuple[np.ndarray, np.ndarray, float]:
+    """Return trade fee and rounding fee per fill in micro-dollars, and the precision."""
+    p = _prices(price)
+    c = _contracts(contracts)
+    multiplier = _floats("fee_multiplier", fee_multiplier)
+    if np.any(multiplier < 0.0):
+        raise ValueError("fee_multiplier must be non-negative")
+    liquidity_labels = _labels("liquidity", liquidity, ("taker", "maker"))
+    side_labels = _labels("side", side, ("buy", "sell"))
+    fee_types = np.asarray(fee_type, dtype=np.str_)
+    if np.any(fee_types == "flat"):
+        raise ValueError(
+            "Kalshi fee_type 'flat' uses the Specific Trading Fees Table, which is not modeled"
+        )
+    fee_types = _labels("fee_type", fee_types, tuple(KALSHI_MAKER_FEE_FACTORS))
+    if rounding not in ("exact", "cent", "direct"):
+        raise ValueError(f"rounding must be 'exact', 'cent' or 'direct', got {rounding!r}")
+
+    maker_factor = np.zeros(fee_types.shape)
+    for name, factor in KALSHI_MAKER_FEE_FACTORS.items():
+        maker_factor = np.where(fee_types == name, factor, maker_factor)
+    factor = np.where(liquidity_labels == "taker", 1.0, maker_factor)
+    model_fee = multiplier * KALSHI_TAKER_RATE * factor * c * p * (1.0 - p)
+    trade_fee = np.ceil(_snap(model_fee * _MICRO))
+    if rounding == "exact":
+        return trade_fee, np.zeros_like(trade_fee), 0.0
+
+    precision = _KALSHI_BALANCE_PRECISION_MICRO[rounding]
+    notional = _snap(p * c * _MICRO)
+    revenue = np.where(side_labels == "buy", -notional, notional)
+    before_alignment = revenue - trade_fee
+    aligned = np.floor(_snap(before_alignment / precision)) * precision
+    rounding_fee = before_alignment - aligned
+    trade_fee, rounding_fee = np.broadcast_arrays(trade_fee, rounding_fee)
+    return trade_fee, rounding_fee, precision
+
+
+def kalshi_fee(
+    price: ArrayInput,
+    contracts: ArrayInput,
+    *,
+    liquidity: ArrayInput = "taker",
+    side: ArrayInput = "buy",
+    fee_type: ArrayInput = "quadratic",
+    fee_multiplier: ArrayInput = 1.0,
+    rounding: KalshiRounding = "exact",
+) -> np.ndarray:
+    """Kalshi trading fee per fill, with each fill treated as its own order.
+
+    Taker fee: ``round_up(M * 0.07 * C * P * (1 - P))`` with ``C`` contracts at
+    price ``P`` and ``M`` the series ``fee_multiplier``. A maker fill pays a
+    multiple of the taker formula set by the series ``fee_type``: 0 for
+    ``quadratic``, 0.25 for ``quadratic_with_maker_fees`` and 0.5 for
+    ``quadratic_with_combo_maker_fees``. Both ``fee_type`` and ``fee_multiplier``
+    are on the series record of Kalshi's API (an event may override them).
+    Kalshi does not publish the ``flat`` formula in machine-readable form; that
+    type raises ``ValueError``.
+
+    Rounding:
+
+    - ``"exact"``: the trade fee, rounded up to 0.000001. 100 contracts at 0.50
+      pay 1.75; 1 contract at 0.50 pays 0.0175.
+    - ``"cent"``: what a non-direct (FCM-cleared, retail) member is charged. The
+      balance change (cash for the contracts minus the trade fee) is aligned
+      down to 0.01, and the difference is added to the fee. 1 contract at 0.50
+      pays 0.02. Kalshi rebates accumulated overpayment across the fills of one
+      order; this function treats every row as a one-fill order, where no rebate
+      can arise. Use `kalshi_order_fees` for the fills of one multi-fill order.
+    - ``"direct"``: as ``"cent"`` with the direct-member precision 0.0001.
+
+    ``side`` changes the result only under ``"cent"`` and ``"direct"``, where a
+    sub-cent notional is part of the alignment.
+
+    Sources: Kalshi fee schedule, "Fee Schedule for July 2026 - 7.7.26 Update"
+    (https://kalshi.com/docs/kalshi-fee-schedule.pdf); maker multipliers from
+    the ``FeeType`` description in https://docs.kalshi.com/openapi.yaml;
+    rounding from https://docs.kalshi.com/getting_started/fee_rounding. All read
+    2026-10-08.
+
+    Args:
+        price: Execution price in dollars per contract, in ``[0, 1]``.
+        contracts: Contracts filled, non-negative.
+        liquidity: ``"taker"`` or ``"maker"``, scalar or per fill.
+        side: ``"buy"`` or ``"sell"``, scalar or per fill.
+        fee_type: Series fee type, scalar or per fill.
+        fee_multiplier: Series fee multiplier ``M``, scalar or per fill.
+        rounding: ``"exact"``, ``"cent"`` or ``"direct"``.
+
+    Returns:
+        Fee in dollars per fill.
+    """
+    trade_fee, rounding_fee, _ = _kalshi_components(
+        price, contracts, liquidity, side, fee_type, fee_multiplier, rounding
+    )
+    return (trade_fee + rounding_fee) / _MICRO
+
+
+def kalshi_order_fees(
+    price: ArrayInput,
+    contracts: ArrayInput,
+    *,
+    liquidity: ArrayInput = "taker",
+    side: ArrayInput = "buy",
+    fee_type: ArrayInput = "quadratic",
+    fee_multiplier: ArrayInput = 1.0,
+    rounding: KalshiRounding = "cent",
+) -> np.ndarray:
+    """Net Kalshi fee per fill for the fills of one order, in execution order.
+
+    Each fill pays its trade fee plus a rounding fee that aligns the balance to
+    the member's precision (see `kalshi_fee`). The rounding fees accumulate per
+    order, across taker and maker fills alike. Whenever the accumulator holds at
+    least one precision step, whole steps are rebated on that fill, capped so
+    the fill's net fee stays non-negative. Three fills that each overpay 0.004
+    pay 0.004, 0.004, and on the third fill 0.010 less (accumulator 0.012,
+    0.002 carried forward).
+
+    Source: https://docs.kalshi.com/getting_started/fee_rounding (read
+    2026-10-08).
+
+    Args:
+        price: Execution price per fill, in ``[0, 1]``.
+        contracts: Contracts per fill, non-negative.
+        liquidity: ``"taker"`` or ``"maker"``, scalar or per fill.
+        side: ``"buy"`` or ``"sell"``; one order has one side.
+        fee_type: Series fee type.
+        fee_multiplier: Series fee multiplier ``M``.
+        rounding: ``"cent"`` (non-direct member), ``"direct"`` or ``"exact"``
+            (no balance alignment, so no accumulator).
+
+    Returns:
+        One-dimensional array of net fees in dollars, one per fill.
+    """
+    trade_fee, rounding_fee, precision = _kalshi_components(
+        price, contracts, liquidity, side, fee_type, fee_multiplier, rounding
+    )
+    trade_fee = np.atleast_1d(trade_fee)
+    rounding_fee = np.atleast_1d(rounding_fee)
+    if trade_fee.ndim != 1:
+        raise ValueError("kalshi_order_fees takes the fills of one order as 1-D input")
+    if precision == 0.0:
+        return trade_fee / _MICRO
+
+    net = np.empty_like(trade_fee)
+    accumulator = 0.0
+    for index, (fee, overpayment) in enumerate(zip(trade_fee, rounding_fee, strict=True)):
+        charged = fee + overpayment
+        accumulator += overpayment
+        available = np.array([accumulator, charged], dtype=np.float64) / precision
+        rebate = float(np.floor(_snap(available)).min()) * precision
+        accumulator -= rebate
+        net[index] = charged - rebate
+    return net / _MICRO
+
+
+class KalshiCommission:
+    """Kalshi trading fee as an engine ``CommissionModel``.
+
+    Each ``calculate`` call is one fill and is treated as a one-fill order: the
+    per-order rebate of rounding overpayment (`kalshi_order_fees`) needs order
+    identity, which the protocol does not pass. A positive quantity is a buy, a
+    negative quantity a sell; buying NO is a buy at the NO price.
+
+    Formula, rounding modes and sources: see `kalshi_fee`.
+
+    Args:
+        liquidity: ``"taker"`` or ``"maker"`` for every fill.
+        fee_type: Series ``fee_type``.
+        fee_multiplier: Series ``fee_multiplier``.
+        rounding: ``"exact"``, ``"cent"`` (non-direct member) or ``"direct"``.
+    """
+
+    def __init__(
+        self,
+        liquidity: Liquidity = "taker",
+        fee_type: KalshiFeeType = "quadratic",
+        fee_multiplier: float = 1.0,
+        rounding: KalshiRounding = "exact",
+    ):
+        kalshi_fee(
+            0.5,
+            1.0,
+            liquidity=liquidity,
+            fee_type=fee_type,
+            fee_multiplier=fee_multiplier,
+            rounding=rounding,
+        )
+        self.liquidity = liquidity
+        self.fee_type = fee_type
+        self.fee_multiplier = fee_multiplier
+        self.rounding = rounding
+
+    def calculate(self, asset: str, quantity: float, price: float) -> float:
+        return float(
+            kalshi_fee(
+                price,
+                abs(quantity),
+                liquidity=self.liquidity,
+                side=_side_from_quantity(quantity),
+                fee_type=self.fee_type,
+                fee_multiplier=self.fee_multiplier,
+                rounding=self.rounding,
+            )
+        )
+
+
+# === ForecastEx ===
+
+
+def forecastex_fee(
+    price: ArrayInput,
+    contracts: ArrayInput,
+    *,
+    exchange_fee: ArrayInput = FORECASTEX_EXCHANGE_FEE,
+    broker_commission: ArrayInput = 0.0,
+) -> np.ndarray:
+    """ForecastEx fee per fill: a flat amount per contract per side.
+
+    ForecastEx charges 0.01 per contract per side at execution, "independent of
+    contract resolution, netting, or settlement"; IBKR charges no commission on
+    ForecastEx contracts. The fee does not depend on price or liquidity; price
+    is validated so that the inputs match the other venues.
+
+    Before 2026-03-31 the fee was embedded in prices (a YES and a NO paired at
+    1.01) instead of charged explicitly. Prices recorded before that date
+    already contain it; pass ``exchange_fee=0.0`` for them. Block trades are
+    exempt from 2026-09-22 to 2026-12-01; pass ``exchange_fee=0.0`` for those
+    too.
+
+    Sources: ForecastEx fee schedule, CFTC filing
+    https://www.cftc.gov/filings/orgrules/rules09082624298.pdf; change of model,
+    https://www.cftc.gov/filings/orgrules/rules03022640231.pdf; IBKR commission,
+    https://www.interactivebrokers.com/en/pricing/commissions-events.php. All
+    read 2026-10-08.
+
+    Args:
+        price: Execution price in ``[0, 1]`` (validated, not used).
+        contracts: Contracts filled, non-negative.
+        exchange_fee: Exchange fee per contract.
+        broker_commission: Broker commission per contract.
+
+    Returns:
+        Fee in dollars per fill.
+    """
+    p = _prices(price)
+    c = _contracts(contracts)
+    per_contract = _floats("exchange_fee", exchange_fee) + _floats(
+        "broker_commission", broker_commission
+    )
+    if np.any(per_contract < 0.0):
+        raise ValueError("exchange_fee + broker_commission must be non-negative")
+    fee = per_contract * c
+    return np.broadcast_arrays(fee, p)[0].astype(np.float64, copy=True)
+
+
+class ForecastExCommission:
+    """ForecastEx fee via IBKR as an engine ``CommissionModel``.
+
+    Formula and sources: see `forecastex_fee`.
+
+    Args:
+        exchange_fee: Exchange fee per contract per side.
+        broker_commission: Broker commission per contract per side.
+    """
+
+    def __init__(
+        self,
+        exchange_fee: float = FORECASTEX_EXCHANGE_FEE,
+        broker_commission: float = 0.0,
+    ):
+        forecastex_fee(0.5, 1.0, exchange_fee=exchange_fee, broker_commission=broker_commission)
+        self.exchange_fee = exchange_fee
+        self.broker_commission = broker_commission
+
+    def calculate(self, asset: str, quantity: float, price: float) -> float:
+        return float(
+            forecastex_fee(
+                price,
+                abs(quantity),
+                exchange_fee=self.exchange_fee,
+                broker_commission=self.broker_commission,
+            )
+        )
+
+
+# === Polymarket US ===
+
+
+def polymarket_us_fee(
+    price: ArrayInput,
+    contracts: ArrayInput,
+    *,
+    liquidity: ArrayInput = "taker",
+    fee_coefficient: ArrayInput = POLYMARKET_US_TAKER_COEFFICIENT,
+    maker_rebate_coefficient: ArrayInput = POLYMARKET_US_MAKER_REBATE_COEFFICIENT,
+    rounding: PolymarketUSRounding = "exact",
+) -> np.ndarray:
+    """Polymarket US fee per fill; a maker rebate is a negative fee.
+
+    Taker: ``theta * C * p * (1 - p)`` with ``theta = 0.0695`` (1,000 contracts
+    at 0.50 pay 17.38 after rounding). Maker: ``-0.0125 * C * p * (1 - p)``,
+    paid at the trade. ``fee_coefficient`` is the taker theta; pass the
+    ``fee_coefficient`` column of ml4t-data's ``PolymarketUSProvider`` market
+    table (the venue's ``feeCoefficient``) to apply a per-market value.
+
+    Rounding: ``"exact"`` returns the formula value; ``"cent"`` rounds each fill
+    to 0.01 with round-half-to-even, as the venue charges it, so small fees can
+    round to 0. The venue also caps the sum over one aggressive order's fills
+    at the rounded cumulative fee; per-fill rounding can exceed that cap by up
+    to 0.005 per fill.
+
+    Not modeled: the combo taker curve ``C * p * [0.0695 * (1 - p) + 0.06 *
+    (1 - p)^4]`` and the prior-month taker volume rebates (10%, 25%, 50%).
+
+    Source: Polymarket US fee schedule, https://docs.polymarket.us/fees (read
+    2026-10-08).
+
+    Args:
+        price: Execution price in ``[0, 1]``.
+        contracts: Contracts filled, non-negative.
+        liquidity: ``"taker"`` or ``"maker"``, scalar or per fill.
+        fee_coefficient: Taker theta, scalar or per fill.
+        maker_rebate_coefficient: Maker rebate magnitude, scalar or per fill.
+        rounding: ``"exact"`` or ``"cent"``.
+
+    Returns:
+        Signed fee in dollars per fill: positive is paid, negative is received.
+    """
+    p = _prices(price)
+    c = _contracts(contracts)
+    taker = _floats("fee_coefficient", fee_coefficient)
+    maker = _floats("maker_rebate_coefficient", maker_rebate_coefficient)
+    if np.any(taker < 0.0) or np.any(maker < 0.0):
+        raise ValueError("fee_coefficient and maker_rebate_coefficient must be non-negative")
+    liquidity_labels = _labels("liquidity", liquidity, ("taker", "maker"))
+    if rounding not in ("exact", "cent"):
+        raise ValueError(f"rounding must be 'exact' or 'cent', got {rounding!r}")
+    coefficient = np.where(liquidity_labels == "taker", taker, -maker)
+    fee = coefficient * c * p * (1.0 - p)
+    if rounding == "cent":
+        half_cents = _snap(fee * 200.0)
+        fee = np.round(half_cents / 2.0) / 100.0
+    return np.asarray(fee, dtype=np.float64)
+
+
+class PolymarketUSCommission:
+    """Polymarket US taker fee as an engine ``CommissionModel``.
+
+    The engine charges only non-negative commissions, so a maker fill is
+    charged 0 and its rebate (0.0125 x C x p x (1 - p)) is left out, which
+    overstates maker costs. Use `polymarket_us_fee` where the rebate matters.
+
+    Formula, rounding and sources: see `polymarket_us_fee`.
+
+    Args:
+        liquidity: ``"taker"`` or ``"maker"`` for every fill.
+        fee_coefficient: Taker theta of the market.
+        rounding: ``"exact"`` or ``"cent"``.
+    """
+
+    def __init__(
+        self,
+        liquidity: Liquidity = "taker",
+        fee_coefficient: float = POLYMARKET_US_TAKER_COEFFICIENT,
+        rounding: PolymarketUSRounding = "exact",
+    ):
+        polymarket_us_fee(
+            0.5, 1.0, liquidity=liquidity, fee_coefficient=fee_coefficient, rounding=rounding
+        )
+        self.liquidity = liquidity
+        self.fee_coefficient = fee_coefficient
+        self.rounding = rounding
+
+    def calculate(self, asset: str, quantity: float, price: float) -> float:
+        if self.liquidity == "maker":
+            return 0.0
+        return float(
+            polymarket_us_fee(
+                price,
+                abs(quantity),
+                liquidity="taker",
+                fee_coefficient=self.fee_coefficient,
+                rounding=self.rounding,
+            )
+        )
diff --git a/tests/compatibility/snapshots/v0.1.json b/tests/compatibility/snapshots/v0.1.json
index a9060759..0aee1886 100644
--- a/tests/compatibility/snapshots/v0.1.json
+++ b/tests/compatibility/snapshots/v0.1.json
@@ -636,6 +636,8 @@
     "from ml4t.backtest.execution.rebalancer import TargetWeightExecutor, RebalanceConfig",
     "from ml4t.backtest.models import NoCommission",
     "from ml4t.backtest.models import TieredCommission, CombinedCommission",
+    "from ml4t.backtest.prediction_market_fees import KalshiCommission",
+    "from ml4t.backtest.prediction_market_fees import kalshi_fee, polymarket_us_fee",
     "from ml4t.backtest.profiles import list_profiles",
     "from ml4t.backtest.result import BacktestResult",
     "from ml4t.backtest.risk.portfolio import MaxDrawdownLimit, RiskManager",
@@ -2923,6 +2925,30 @@
       "qualname": "TieredCommission",
       "signature": "(tiers: list[tuple[float, float]])"
     },
+    "ml4t.backtest.prediction_market_fees:KalshiCommission": {
+      "kind": "class",
+      "members": {
+        "calculate": {
+          "kind": "method",
+          "signature": "(self, asset: str, quantity: float, price: float) -> float"
+        }
+      },
+      "module": "ml4t.backtest.prediction_market_fees",
+      "qualname": "KalshiCommission",
+      "signature": "(liquidity: Literal['taker', 'maker'] = 'taker', fee_type: Literal['quadratic', 'quadratic_with_maker_fees', 'quadratic_with_combo_maker_fees'] = 'quadratic', fee_multiplier: float = 1.0, rounding: Literal['exact', 'cent', 'direct'] = 'exact')"
+    },
+    "ml4t.backtest.prediction_market_fees:kalshi_fee": {
+      "kind": "function",
+      "module": "ml4t.backtest.prediction_market_fees",
+      "qualname": "kalshi_fee",
+      "signature": "(price: Any, contracts: Any, *, liquidity: Any = 'taker', side: Any = 'buy', fee_type: Any = 'quadratic', fee_multiplier: Any = 1.0, rounding: Literal['exact', 'cent', 'direct'] = 'exact') -> numpy.ndarray"
+    },
+    "ml4t.backtest.prediction_market_fees:polymarket_us_fee": {
+      "kind": "function",
+      "module": "ml4t.backtest.prediction_market_fees",
+      "qualname": "polymarket_us_fee",
+      "signature": "(price: Any, contracts: Any, *, liquidity: Any = 'taker', fee_coefficient: Any = 0.0695, maker_rebate_coefficient: Any = 0.0125, rounding: Literal['exact', 'cent'] = 'exact') -> numpy.ndarray"
+    },
     "ml4t.backtest.profiles:list_profiles": {
       "kind": "function",
       "module": "ml4t.backtest.profiles",
diff --git a/tests/test_prediction_market_fees.py b/tests/test_prediction_market_fees.py
new file mode 100644
index 00000000..ed715bc0
--- /dev/null
+++ b/tests/test_prediction_market_fees.py
@@ -0,0 +1,300 @@
+"""Prediction-market venue fees against the published schedules and worked examples."""
+
+from copy import deepcopy
+from datetime import datetime, timedelta
+from decimal import ROUND_CEILING, ROUND_FLOOR, ROUND_HALF_EVEN, Decimal
+
+import numpy as np
+import polars as pl
+import pytest
+
+from ml4t.backtest import BacktestConfig, DataFeed, Engine, Strategy
+from ml4t.backtest.models import CommissionModel, estimate_commission
+from ml4t.backtest.prediction_market_fees import (
+    ForecastExCommission,
+    KalshiCommission,
+    PolymarketUSCommission,
+    forecastex_fee,
+    kalshi_fee,
+    kalshi_order_fees,
+    polymarket_us_fee,
+)
+from ml4t.backtest.types import OrderSide
+
+CENT_PRICES = [k / 100 for k in range(1, 100)]
+
+
+def _kalshi_reference(cents: int, contracts: int, side: str, precision: str | None) -> Decimal:
+    """Kalshi fee in exact decimal arithmetic, following the fee-rounding page step by step."""
+    price = Decimal(cents) / 100
+    trade_fee = (Decimal("0.07") * contracts * price * (1 - price)).quantize(
+        Decimal("0.000001"), ROUND_CEILING
+    )
+    if precision is None:
+        return trade_fee
+    revenue = price * contracts * (-1 if side == "buy" else 1)
+    aligned = (revenue - trade_fee).quantize(Decimal(precision), ROUND_FLOOR)
+    return revenue - aligned
+
+
+# === Kalshi: formula ===
+
+
+def test_kalshi_taker_fee_matches_the_schedule_peak_and_tail():
+    # Guards a wrong rate: 0.07 x P x (1 - P) is 0.0175 at 0.50 and 0.0063 at 0.90.
+    assert kalshi_fee(0.50, 100).item() == 1.75
+    assert kalshi_fee(0.50, 1).item() == 0.0175
+    assert kalshi_fee(0.90, 1).item() == 0.0063
+
+
+def test_kalshi_fee_is_symmetric_between_yes_and_no_prices():
+    # Buying NO at q is a purchase at q; the fee must not depend on which side is YES.
+    prices = np.array(CENT_PRICES)
+    np.testing.assert_array_equal(kalshi_fee(prices, 37), kalshi_fee(1.0 - prices, 37))
+
+
+def test_kalshi_series_fee_multiplier_scales_the_fee():
+    # Guards a dropped multiplier M, scalar or joined per row from series metadata.
+    assert kalshi_fee(0.50, 100, fee_multiplier=2.0).item() == 3.5
+    np.testing.assert_array_equal(
+        kalshi_fee([0.50, 0.50], [100, 100], fee_multiplier=[1.0, 0.5]), [1.75, 0.875]
+    )
+
+
+@pytest.mark.parametrize(
+    ("fee_type", "expected"),
+    [
+        ("quadratic", 0.0),
+        ("quadratic_with_maker_fees", 0.4375),
+        ("quadratic_with_combo_maker_fees", 0.875),
+    ],
+)
+def test_kalshi_maker_fee_follows_series_fee_type(fee_type, expected):
+    # Guards charging makers on taker-only series and using the wrong maker factor.
+    assert kalshi_fee(0.50, 100, liquidity="maker", fee_type=fee_type).item() == expected
+    assert kalshi_fee(0.50, 100, liquidity="taker", fee_type=fee_type).item() == 1.75
+
+
+def test_kalshi_per_row_liquidity_and_fee_type_from_polars():
+    fees = kalshi_fee(
+        pl.Series([0.50, 0.50, 0.50]),
+        pl.Series([100, 100, 100]),
+        liquidity=pl.Series(["taker", "maker", "maker"]),
+        fee_type=pl.Series(["quadratic", "quadratic", "quadratic_with_maker_fees"]),
+    )
+    np.testing.assert_array_equal(fees, [1.75, 0.0, 0.4375])
+
+
+# === Kalshi: rounding ===
+
+
+def test_kalshi_trade_fee_rounds_up_to_a_micro_dollar():
+    # 0.07 x 0.055 x 0.945 = 0.00363825: rounding up gives 0.003639, down 0.003638.
+    assert kalshi_fee(0.055, 1).item() == 0.003639
+
+
+def test_kalshi_exact_fee_on_the_micro_grid_is_not_bumped_by_float_noise():
+    # 0.07 x 100 x 0.25 evaluates to 1.7500000000000002; a naive ceiling charges 1.750001.
+    assert kalshi_fee(0.50, 100).item() == 1.75
+
+
+def test_kalshi_cent_rounding_applies_to_the_fill_not_each_contract():
+    # 100 contracts at 0.50 cost 50.00 + 1.75 = 51.75, already on the cent grid.
+    # Rounding each contract's 0.0175 to 0.02 would charge 2.00.
+    assert kalshi_fee(0.50, 100, rounding="cent").item() == 1.75
+    assert kalshi_fee(0.50, 1, rounding="cent").item() == 0.02
+    assert kalshi_fee(0.50, 1, rounding="exact").item() == 0.0175
+
+
+def test_kalshi_cent_rounding_matches_the_published_fcm_example():
+    # docs.kalshi.com fee_rounding: buy, revenue -0.055, trade fee 0.003639, total 0.005.
+    assert kalshi_fee(0.055, 1, side="buy", rounding="cent").item() == 0.005
+
+
+def test_kalshi_direct_member_precision_is_a_hundredth_of_a_cent():
+    # -0.055 - 0.003639 aligns down to -0.0587 at 0.0001 precision: fee 0.0037.
+    assert kalshi_fee(0.055, 1, rounding="direct").item() == 0.0037
+    assert kalshi_fee(0.50, 1, rounding="direct").item() == 0.0175
+
+
+@pytest.mark.parametrize("rounding", ["exact", "cent", "direct"])
+@pytest.mark.parametrize("side", ["buy", "sell"])
+def test_kalshi_fee_matches_decimal_reference_over_the_price_grid(rounding, side):
+    precision = {"exact": None, "cent": "0.01", "direct": "0.0001"}[rounding]
+    for contracts in (1, 3, 7, 100, 2500):
+        expected = [
+            float(_kalshi_reference(cents, contracts, side, precision)) for cents in range(1, 100)
+        ]
+        actual = kalshi_fee(CENT_PRICES, contracts, side=side, rounding=rounding)
+        np.testing.assert_array_equal(actual, expected)
+
+
+def test_kalshi_order_accumulator_rebates_whole_cents_of_overpayment():
+    # Each 1-contract fill at 0.10 has trade fee 0.0063 and rounding fee 0.0037.
+    # After the third fill the accumulator holds 0.0111: 0.01 is rebated, 0.0011 carries.
+    fills = kalshi_order_fees([0.10, 0.10, 0.10], [1, 1, 1], rounding="cent")
+    np.testing.assert_allclose(fills, [0.01, 0.01, 0.0], rtol=0, atol=1e-12)
+    single_fill = kalshi_fee(0.10, 3, rounding="cent").item()
+    assert fills.sum() == pytest.approx(single_fill, abs=1e-12)
+
+
+def test_kalshi_order_without_alignment_has_no_rebate():
+    fills = kalshi_order_fees([0.10, 0.10, 0.10], [1, 1, 1], rounding="exact")
+    np.testing.assert_array_equal(fills, [0.0063, 0.0063, 0.0063])
+
+
+@pytest.mark.parametrize(
+    ("kwargs", "message"),
+    [
+        ({"price": 1.2}, "price"),
+        ({"price": float("nan")}, "price"),
+        ({"contracts": -1}, "contracts"),
+        ({"fee_type": "flat"}, "flat"),
+        ({"fee_type": "linear"}, "fee_type"),
+        ({"liquidity": "passive"}, "liquidity"),
+        ({"side": "short"}, "side"),
+        ({"fee_multiplier": -1.0}, "fee_multiplier"),
+        ({"rounding": "up"}, "rounding"),
+    ],
+)
+def test_kalshi_rejects_inputs_outside_the_schedule(kwargs, message):
+    arguments = {"price": 0.5, "contracts": 1} | kwargs
+    price = arguments.pop("price")
+    contracts = arguments.pop("contracts")
+    with pytest.raises(ValueError, match=message):
+        kalshi_fee(price, contracts, **arguments)
+
+
+# === ForecastEx ===
+
+
+def test_forecastex_fee_is_one_cent_per_contract_at_any_price():
+    np.testing.assert_array_equal(
+        forecastex_fee([0.05, 0.50, 0.95], [1, 10, 100]), [0.01, 0.1, 1.0]
+    )
+
+
+def test_forecastex_embedded_fee_era_can_be_priced_at_zero():
+    assert forecastex_fee(0.5, 10, exchange_fee=0.0).item() == 0.0
+
+
+# === Polymarket US ===
+
+
+def test_polymarket_us_taker_fee_matches_the_schedule():
+    assert polymarket_us_fee(0.50, 1).item() == pytest.approx(0.017375, abs=1e-15)
+    assert polymarket_us_fee(0.50, 1000, rounding="cent").item() == 17.38
+
+
+def test_polymarket_us_maker_rebate_is_a_negative_fee():
+    # Published example: maker at 0.10 on 1,000 contracts receives 1.12.
+    assert polymarket_us_fee(0.10, 1000, liquidity="maker", rounding="cent").item() == -1.12
+
+
+def test_polymarket_us_cent_rounding_is_half_to_even_on_exact_values():
+    # 0.0695 x 1000 x 0.3 x 0.7 = 14.595 exactly; in floats 14.594999999999997,
+    # which a naive round(x, 2) turns into 14.59. 6.255 rounds to 6.26 (even).
+    fees = polymarket_us_fee([0.30, 0.10, 0.90], 1000, rounding="cent")
+    np.testing.assert_array_equal(fees, [14.60, 6.26, 6.26])
+    # 0.125 rounds down to the even cent, where half-up would give 0.13.
+    assert polymarket_us_fee(0.5, 1, fee_coefficient=0.5, rounding="cent").item() == 0.12
+
+
+def test_polymarket_us_cent_rounding_matches_decimal_reference():
+    for contracts in (1, 10, 100, 1000, 12345):
+        expected = [
+            float(
+                (
+                    Decimal("0.0695") * contracts * Decimal(k) / 100 * (1 - Decimal(k) / 100)
+                ).quantize(Decimal("0.01"), ROUND_HALF_EVEN)
+            )
+            for k in range(1, 100)
+        ]
+        np.testing.assert_array_equal(
+            polymarket_us_fee(CENT_PRICES, contracts, rounding="cent"), expected
+        )
+
+
+def test_polymarket_us_uses_the_per_market_fee_coefficient():
+    fees = polymarket_us_fee([0.5, 0.5], [100, 100], fee_coefficient=[0.0695, 0.10])
+    np.testing.assert_allclose(fees, [1.7375, 2.5], rtol=0, atol=1e-12)
+
+
+# === CommissionModel adapters ===
+
+
+@pytest.mark.parametrize(
+    ("model", "quantity", "price", "expected"),
+    [
+        (KalshiCommission(), 100, 0.50, 1.75),
+        (KalshiCommission(rounding="cent"), 1, 0.50, 0.02),
+        (KalshiCommission(rounding="cent"), -1, 0.055, 0.005),
+        (KalshiCommission(liquidity="maker"), 100, 0.50, 0.0),
+        (
+            KalshiCommission(liquidity="maker", fee_type="quadratic_with_maker_fees"),
+            100,
+            0.5,
+            0.4375,
+        ),
+        (KalshiCommission(fee_multiplier=2.0), 100, 0.50, 3.5),
+        (ForecastExCommission(), -25, 0.40, 0.25),
+        (PolymarketUSCommission(rounding="cent"), 1000, 0.50, 17.38),
+        (PolymarketUSCommission(liquidity="maker"), 1000, 0.10, 0.0),
+    ],
+)
+def test_commission_models_charge_the_venue_fee(model, quantity, price, expected):
+    assert isinstance(model, CommissionModel)
+    assert model.calculate("EVT", quantity, price) == pytest.approx(expected, abs=1e-12)
+    assert estimate_commission(deepcopy(model), "EVT", quantity, price) == pytest.approx(
+        expected, abs=1e-12
+    )
+
+
+def test_commission_model_rejects_invalid_configuration_at_construction():
+    with pytest.raises(ValueError, match="flat"):
+        KalshiCommission(fee_type="flat")  # type: ignore[arg-type]
+    with pytest.raises(ValueError, match="rounding"):
+        PolymarketUSCommission(rounding="direct")  # type: ignore[arg-type]
+
+
+def test_engine_charges_kalshi_cent_fee_on_each_fill():
+    class BuyThenSell(Strategy):
+        def on_data(self, timestamp, data, context, broker):
+            if timestamp == datetime(2024, 1, 1):
+                broker.submit_order("EVT", 1, OrderSide.BUY)
+            elif timestamp == datetime(2024, 1, 3):
+                broker.submit_order("EVT", 100, OrderSide.BUY)
+
+    dates = [datetime(2024, 1, 1) + timedelta(days=i) for i in range(5)]
+    prices = pl.DataFrame(
+        {
+            "timestamp": dates,
+            "asset": ["EVT"] * len(dates),
+            "open": [0.5] * len(dates),
+            "high": [0.5] * len(dates),
+            "low": [0.5] * len(dates),
+            "close": [0.5] * len(dates),
+            "volume": [100000] * len(dates),
+        }
+    )
+    engine = Engine(
+        feed=DataFeed(prices_df=prices),
+        strategy=BuyThenSell(),
+        config=BacktestConfig(initial_cash=1000),
+    )
+    engine.broker.commission_model = KalshiCommission(rounding="cent")
+    result = engine.run()
+
+    assert [fill.commission for fill in result.fills] == [0.02, 1.75]
+
+
+def test_negative_fee_parameters_are_rejected():
+    with pytest.raises(ValueError, match="non-negative"):
+        forecastex_fee(0.5, 1, exchange_fee=-0.01)
+    with pytest.raises(ValueError, match="non-negative"):
+        polymarket_us_fee(0.5, 1, maker_rebate_coefficient=-0.0125)
+
+
+def test_kalshi_order_fees_takes_one_order_as_one_dimension():
+    with pytest.raises(ValueError, match="1-D"):
+        kalshi_order_fees([[0.5, 0.5]], [[1, 1]])
diff --git a/validation/CORRECTNESS_RESULTS.json b/validation/CORRECTNESS_RESULTS.json
index 9c8f4ada..bf0ddd7f 100644
--- a/validation/CORRECTNESS_RESULTS.json
+++ b/validation/CORRECTNESS_RESULTS.json
@@ -63,7 +63,7 @@
       "version": "3.1.1"
     }
   },
-  "generated_at": "2026-09-24T01:09:13.329860+00:00",
+  "generated_at": "2026-10-08T19:42:55.916972+00:00",
   "input_policy": {
     "float_quantum": "0.00000001",
     "rounding": "ROUND_HALF_EVEN",
@@ -704,7 +704,7 @@
         "scenario_id": "01"
       },
       "detail": null,
-      "duration_seconds": 2.4247171190800145,
+      "duration_seconds": 2.3915587529772893,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -1361,7 +1361,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "553e3e351f2de59bc17fc4f42b67e4e4f923d059b44eecd6a69e114df4b4a8fe",
+          "engine": "2a0d9b9b0570f22772090e2204b97fb7aa970b99ca6814b63f443b47539bf41f",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "4d2cacb23e64dacdbb2eda228691671d3a5d327dbbdd899de310551b244bae54",
@@ -1374,7 +1374,7 @@
         },
         "input_digest": "465e20dbdd050a15410d58b7391e304491566407369632fbda07da88e4cc1cee",
         "ml4t": {
-          "commit": "fde7254403b60f4a556c5f7beabaf62274f1ae13",
+          "commit": "16e7ffabc15360de01638d662160eb41ef7d34d8",
           "dirty": false
         },
         "python": {
@@ -2034,7 +2034,7 @@
         "scenario_id": "02"
       },
       "detail": null,
-      "duration_seconds": 2.42568556801416,
+      "duration_seconds": 2.389512272027787,
       "framework": "vectorbt_pro",
       "framework_result": {
         "capabilities": {
@@ -2691,7 +2691,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "553e3e351f2de59bc17fc4f42b67e4e4f923d059b44eecd6a69e114df4b4a8fe",
+          "engine": "2a0d9b9b0570f22772090e2204b97fb7aa970b99ca6814b63f443b47539bf41f",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "8901a4a7a750c26fb43ceb5d89b4c6ccf9279503da8659b8398cce30d90c8d9c",
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           "profile": "94534fbfeca18ae041b780a18fbfe6884c21c0ce8c84d15d1d8e2096ba59cce7",
@@ -255100,7 +255100,7 @@
         },
         "input_digest": "a0bd681ff924f45192415219403b02e639df271a2aa5987bb9b1e263b2d8c88a",
         "ml4t": {
-          "commit": "fde7254403b60f4a556c5f7beabaf62274f1ae13",
+          "commit": "16e7ffabc15360de01638d662160eb41ef7d34d8",
           "dirty": false
         },
         "python": {
@@ -255247,7 +255247,7 @@
         "scenario_id": "15"
       },
       "detail": null,
-      "duration_seconds": 2.1742781409993768,
+      "duration_seconds": 1.513668409024831,
       "framework": "zipline",
       "framework_result": {
         "capabilities": {
@@ -255395,7 +255395,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "553e3e351f2de59bc17fc4f42b67e4e4f923d059b44eecd6a69e114df4b4a8fe",
+          "engine": "2a0d9b9b0570f22772090e2204b97fb7aa970b99ca6814b63f443b47539bf41f",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "94534fbfeca18ae041b780a18fbfe6884c21c0ce8c84d15d1d8e2096ba59cce7",
@@ -255408,7 +255408,7 @@
         },
         "input_digest": "ce9cac417809758597595322703b30800145707fa0db44bd7f2cd0775790c114",
         "ml4t": {
-          "commit": "fde7254403b60f4a556c5f7beabaf62274f1ae13",
+          "commit": "16e7ffabc15360de01638d662160eb41ef7d34d8",
           "dirty": false
         },
         "python": {
@@ -255783,7 +255783,7 @@
         "scenario_id": "16"
       },
       "detail": null,
-      "duration_seconds": 2.765984929050319,
+      "duration_seconds": 2.052965320996009,
       "framework": "zipline",
       "framework_result": {
         "capabilities": {
@@ -256159,7 +256159,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "553e3e351f2de59bc17fc4f42b67e4e4f923d059b44eecd6a69e114df4b4a8fe",
+          "engine": "2a0d9b9b0570f22772090e2204b97fb7aa970b99ca6814b63f443b47539bf41f",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "94534fbfeca18ae041b780a18fbfe6884c21c0ce8c84d15d1d8e2096ba59cce7",
@@ -256172,7 +256172,7 @@
         },
         "input_digest": "88cfc732983d3fa783e181fd4c97f8d95f7833e20dd03de0c201aaa85e83ca8b",
         "ml4t": {
-          "commit": "fde7254403b60f4a556c5f7beabaf62274f1ae13",
+          "commit": "16e7ffabc15360de01638d662160eb41ef7d34d8",
           "dirty": false
         },
         "python": {
@@ -275471,7 +275471,7 @@
         "scenario_id": "17"
       },
       "detail": null,
-      "duration_seconds": 3.0084816509624943,
+      "duration_seconds": 2.34652952500619,
       "framework": "zipline",
       "framework_result": {
         "capabilities": {
@@ -294771,7 +294771,7 @@
           "canonical_records": "fa1d9846bdc895cf62e6435097a8953d131ac16f6ae910d41ba81a5090c43707",
           "capabilities": "dd0f50965982db8aae711f850740fc1d26c5f09d8b7c3d24221b347a7e14c8aa",
           "comparator": "de6683dd96cab7fc9ce005f4651b7de359b8ddfcd6402b543960133ca35b0f5e",
-          "engine": "553e3e351f2de59bc17fc4f42b67e4e4f923d059b44eecd6a69e114df4b4a8fe",
+          "engine": "2a0d9b9b0570f22772090e2204b97fb7aa970b99ca6814b63f443b47539bf41f",
           "manifest": "38e64faa733ac8412b0a1cabec5357be229852f19b432f2f215a7bf20f3a2780",
           "ml4t_runner": "6e9753811241863f59fc6607f315237aa1caec673372ef3b3a85fc959cf426e0",
           "profile": "4d2cacb23e64dacdbb2eda228691671d3a5d327dbbdd899de310551b244bae54",
@@ -294784,7 +294784,7 @@
         },
         "input_digest": "6fdd33a401576a2adde590f2f8d5986f5893e4663336033ba6c0f8f221d5d526",
         "ml4t": {
-          "commit": "fde7254403b60f4a556c5f7beabaf62274f1ae13",
+          "commit": "16e7ffabc15360de01638d662160eb41ef7d34d8",
           "dirty": false
         },
         "python": {
diff --git a/validation/REAL_STRATEGY_PERFORMANCE.json b/validation/REAL_STRATEGY_PERFORMANCE.json
index 5f1b87fe..9d39e67c 100644
--- a/validation/REAL_STRATEGY_PERFORMANCE.json
+++ b/validation/REAL_STRATEGY_PERFORMANCE.json
@@ -1,5 +1,5 @@
 {
-  "correctness_evidence_generated_at": "2026-09-24T01:50:22.858146+00:00",
+  "correctness_evidence_generated_at": "2026-10-08T20:00:49.510846+00:00",
   "environment": {
     "cpu_count": 24,
     "frameworks": {
@@ -101,6 +101,50 @@
   },
   "generated_at": "2026-09-24T05:37:41.451299+00:00",
   "provenance": {
+    "certified_equivalent_sources": [
+      {
+        "certified_at": "2026-10-08T19:28:11.762869+00:00",
+        "correctness_evidence_generated_at": "2026-10-08T19:27:55.214328+00:00",
+        "correctness_evidence_sha256": "8818058c974a2beac7edc59013224bb0fd818f828e656dac5f5a41650a704aa5",
+        "correctness_pairs_passed": 17,
+        "engine_source_sha256": "b1ebee1ef59f82269fb69cb4103927398254bfa5d8ad620a23c588260de99aca",
+        "ml4t_commit": "a1649d9f4271c56dcb1de9688b97218f8c50cb13",
+        "reason": "The only engine source change is the new module src/ml4t/backtest/prediction_market_fees.py. Nothing in src/ml4t/backtest or the real-strategy runners imports it (grep finds no import outside the module itself), so it is never loaded by the timed region, result = engine.run() in validation/real_strategy_runner.py, and cannot move an engine timing. Real-strategy correctness was rebuilt under this source at f349264: 17/17 pass with no compared value moved.",
+        "sources": {
+          "backtrader": "a456cc3bf9ed6708d239f21ed2c2495a3c19a9a8306634fee24619c2c0484459",
+          "benchmark": "0ccc3c90786dc6bc7b8d5992097cd977e2568c39c20f1490d83531b20e6a5188",
+          "comparison_input": "a00bf9383c3e5561333ef0fa734745296cf9cda6fe7155ce950713c1ba2da4f9",
+          "lean_crypto": "82f1fbb6054fba25d74a4eabbc9ad4eae808caf89c00f560edefa63304da0dc4",
+          "lean_equity": "f566341d0f4103eda45280684be1b8a91f9da1a46a3a06477cba3b49674d89e8",
+          "lean_fx": "85c18eb1368bfac938fd9909eaa481de867cd864b1f6852281da8af7c2b44951",
+          "ml4t": "c8a5eb818a64a128dce41bc9827adea0a46fdab87a11f418abb19d5df8c7bff9",
+          "vectorbt_oss": "c1817727bbf49d1551a072717db324c3e70484eefbdc27aa84af2a093e3e4d52",
+          "vectorbt_pro": "c1817727bbf49d1551a072717db324c3e70484eefbdc27aa84af2a093e3e4d52",
+          "zipline": "c23b547767fad7a60f8bd6b0abd1d8f1c1acf99ef0faab3da8fd34621b0ec482"
+        }
+      },
+      {
+        "certified_at": "2026-10-08T20:00:56.038473+00:00",
+        "correctness_evidence_generated_at": "2026-10-08T20:00:49.510846+00:00",
+        "correctness_evidence_sha256": "3f26dc66ec0874de379a4fe97fa149ccf262688901a7b6aa2c3a973e4376a861",
+        "correctness_pairs_passed": 17,
+        "engine_source_sha256": "2a0d9b9b0570f22772090e2204b97fb7aa970b99ca6814b63f443b47539bf41f",
+        "ml4t_commit": "e76e95d5b3ca5ef1daf833c82b9548e1db726fed",
+        "reason": "The engine source differs from the measured one only by the new module src/ml4t/backtest/prediction_market_fees.py. Nothing in src/ml4t/backtest or the real-strategy runners imports it, so it is never loaded in the timed region (result = engine.run() in validation/real_strategy_runner.py) and cannot move an engine timing. Real-strategy correctness was rebuilt under this source at 5b7a27e: 17/17 pass.",
+        "sources": {
+          "backtrader": "a456cc3bf9ed6708d239f21ed2c2495a3c19a9a8306634fee24619c2c0484459",
+          "benchmark": "0ccc3c90786dc6bc7b8d5992097cd977e2568c39c20f1490d83531b20e6a5188",
+          "comparison_input": "a00bf9383c3e5561333ef0fa734745296cf9cda6fe7155ce950713c1ba2da4f9",
+          "lean_crypto": "82f1fbb6054fba25d74a4eabbc9ad4eae808caf89c00f560edefa63304da0dc4",
+          "lean_equity": "f566341d0f4103eda45280684be1b8a91f9da1a46a3a06477cba3b49674d89e8",
+          "lean_fx": "85c18eb1368bfac938fd9909eaa481de867cd864b1f6852281da8af7c2b44951",
+          "ml4t": "c8a5eb818a64a128dce41bc9827adea0a46fdab87a11f418abb19d5df8c7bff9",
+          "vectorbt_oss": "c1817727bbf49d1551a072717db324c3e70484eefbdc27aa84af2a093e3e4d52",
+          "vectorbt_pro": "c1817727bbf49d1551a072717db324c3e70484eefbdc27aa84af2a093e3e4d52",
+          "zipline": "c23b547767fad7a60f8bd6b0abd1d8f1c1acf99ef0faab3da8fd34621b0ec482"
+        }
+      }
+    ],
     "ml4t_engine_source_sha256": "553e3e351f2de59bc17fc4f42b67e4e4f923d059b44eecd6a69e114df4b4a8fe",
     "sources": {
       "backtrader": "a456cc3bf9ed6708d239f21ed2c2495a3c19a9a8306634fee24619c2c0484459",
diff --git a/validation/REAL_STRATEGY_RESULTS.json b/validation/REAL_STRATEGY_RESULTS.json
index 6ea17fa4..592faaf0 100644
--- a/validation/REAL_STRATEGY_RESULTS.json
+++ b/validation/REAL_STRATEGY_RESULTS.json
@@ -20,7 +20,7 @@
       "us_equities_panel": "session date"
     }
   },
-  "generated_at": "2026-09-24T01:50:22.858146+00:00",
+  "generated_at": "2026-10-08T20:00:49.510846+00:00",
   "provenance": {
     "adapters": {
       "backtrader": "a456cc3bf9ed6708d239f21ed2c2495a3c19a9a8306634fee24619c2c0484459",
@@ -131,24 +131,24 @@
     },
     "machine": "x86_64",
     "ml4t": {
-      "commit": "0524f8ecef19a83d8950b4cfc6fb6a384ec459cf",
+      "commit": "e76e95d5b3ca5ef1daf833c82b9548e1db726fed",
       "dirty": false,
-      "engine_source_sha256": "553e3e351f2de59bc17fc4f42b67e4e4f923d059b44eecd6a69e114df4b4a8fe"
+      "engine_source_sha256": "2a0d9b9b0570f22772090e2204b97fb7aa970b99ca6814b63f443b47539bf41f"
     },
-    "platform": "Linux-6.8.0-139-generic-x86_64-with-glibc2.39",
+    "platform": "Linux-7.0.0-38-generic-x86_64-with-glibc2.43",
     "python": "3.12.11"
   },
   "records": [
     {
       "case_study": "etfs",
       "engine_seconds": {
-        "framework": 1.1340462770313025,
-        "ml4t": 1.2943288600072265,
+        "framework": 0.3412680019973777,
+        "ml4t": 0.419192252040375,
         "single_run_diagnostic_only": true
       },
       "evidence": {
-        "framework_manifest_sha256": "44f1ffaf162456369fa006ef5aa075a20423127a3f827dd4211adec1fa442cd6",
-        "ml4t_manifest_sha256": "6471618270dbf4dfa8fed409e074fb3ff96647d8e12a856b089de3cf982a82f9"
+        "framework_manifest_sha256": "1b7f4eca0fd6eafcc14c5bb97a60150810c4517972671ab93d963f507bff1b3a",
+        "ml4t_manifest_sha256": "abab1d59ba06a1216da8c0be69ad19670c277eec2bc467c42aebcc790d5d6eb3"
       },
       "excluded_surfaces": {
         "accepted_unfilled_orders": "not exposed by every native runner",
@@ -209,13 +209,13 @@
     {
       "case_study": "etfs",
       "engine_seconds": {
-        "framework": 1.0902336100116372,
-        "ml4t": 1.2939870660193264,
+        "framework": 0.18416030501248315,
+        "ml4t": 0.4282481879927218,
         "single_run_diagnostic_only": true
       },
       "evidence": {
-        "framework_manifest_sha256": "4bda15d8c20b681d468a1f54c81a967f7fb9a12d6a0fe65bcf122b433c0e4025",
-        "ml4t_manifest_sha256": "ce8e5c941b9682e822e6b7653a81ea4b635228ffe6f9fd9868d625d46a7b68e6"
+        "framework_manifest_sha256": "48aa6691ec1a296142101db639e8ba5966ad83490de0b58e646620ff022cfd2c",
+        "ml4t_manifest_sha256": "064b493228789906039cbd462d3297059d48f56ceed0a4c1c5e691475ed7134c"
       },
       "excluded_surfaces": {
         "accepted_unfilled_orders": "not exposed by every native runner",
@@ -276,13 +276,13 @@
     {
       "case_study": "etfs",
       "engine_seconds": {
-        "framework": 23.003436908009462,
-        "ml4t": 1.1710805310867727,
+        "framework": 9.737538582005072,
+        "ml4t": 0.4487659599981271,
         "single_run_diagnostic_only": true
       },
       "evidence": {
-        "framework_manifest_sha256": "2c321c02039c5d16ae30a4bc677b4779dbc8719896fa16262a10f9be9aedae95",
-        "ml4t_manifest_sha256": "4c34a465e1ed166eabe4b33c1905409e1b440b81fdebb918473b06b1d47458ef"
+        "framework_manifest_sha256": "7f1e9cecc4feeced81af2ee7d99123196536bce1e5fb292631d7d250d19834d7",
+        "ml4t_manifest_sha256": "d83bebda06df2b217689c872082582b19a7b09d459578458a08de55273b37a7e"
       },
       "excluded_surfaces": {
         "accepted_unfilled_orders": "not exposed by every native runner",
@@ -353,13 +353,13 @@
     {
       "case_study": "etfs",
       "engine_seconds": {
-        "framework": 15.617935788934119,
-        "ml4t": 1.7292126989923418,
+        "framework": 4.807452238979749,
+        "ml4t": 0.6355875970330089,
         "single_run_diagnostic_only": true
       },
       "evidence": {
-        "framework_manifest_sha256": "9259f755de4a5deefaf89e6a6d13647061193db0e49d89e7631314b1c3e65c0d",
-        "ml4t_manifest_sha256": "d9a6f6210dd5d378d9a244c73772aefdaffbed945a6add5e611c2b2a0d2716e8"
+        "framework_manifest_sha256": "26775814326860feebb8fc359492b58f601240a811916b2797219907b443ae11",
+        "ml4t_manifest_sha256": "a4aaa05edad9e296818a7687a24b7f502d0eecdd796bfde3215fab866f9ecd21"
       },
       "excluded_surfaces": {
         "accepted_unfilled_orders": "not exposed by every native runner",
@@ -420,13 +420,13 @@
     {
       "case_study": "etfs",
       "engine_seconds": {
-        "framework": 7.2253005559323356,
-        "ml4t": 2.232580687967129,
+        "framework": 2.9700952940038405,
+        "ml4t": 0.7646393330069259,
         "single_run_diagnostic_only": true
       },
       "evidence": {
-        "framework_manifest_sha256": "8dfa63a34ca4f69adfe5a5ebb667ab74114c5545dd05097870b37f2ce3dd2161",
-        "ml4t_manifest_sha256": "7b8426a090590cd9e9a96977c308de245349e8f5d94d95721a621d74885483ef"
+        "framework_manifest_sha256": "bccc2fa584afc9759bca5b77f912a2d9df9b7bb22f446d458c32978932f48064",
+        "ml4t_manifest_sha256": "89ee685f46b21e64bac1fc900cc473735ddfb9a23e02cda8714bf73358f5d657"
       },
       "excluded_surfaces": {
         "accepted_unfilled_orders": "not exposed by every native runner",
@@ -515,13 +515,13 @@
     {
       "case_study": "cme_futures",
       "engine_seconds": {
-        "framework": 1.0428278229665011,
-        "ml4t": 0.9311261599650607,
+        "framework": 0.37743046099785715,
+        "ml4t": 0.4161097949836403,
         "single_run_diagnostic_only": true
       },
       "evidence": {
-        "framework_manifest_sha256": "65264b17b19a01dfa389029873603b9fe9a5d04db757d9070167364c94a936da",
-        "ml4t_manifest_sha256": "0a0960be223d66a24b88568160bdca9e3f5adb2b17bc58954ce24bfd223192ba"
+        "framework_manifest_sha256": "95f831f02c6990a73c4e08b41587dfee3103c8587f2121d5ea2878d841d70c0a",
+        "ml4t_manifest_sha256": "82e4d2ded6a373d2b5b7cf3f0ec696c54c4405ceecd4fc49c0c067d8f4368370"
       },
       "excluded_surfaces": {
         "accepted_unfilled_orders": "not exposed by every native runner",
@@ -589,13 +589,13 @@
     {
       "case_study": "cme_futures",
       "engine_seconds": {
-        "framework": 5.8616829010425135,
-        "ml4t": 1.1937822529580444,
+        "framework": 2.6640014040167443,
+        "ml4t": 0.4422138170339167,
         "single_run_diagnostic_only": true
       },
       "evidence": {
-        "framework_manifest_sha256": "ec546c132593e7167baf8ccb5069ce7fb46ecc6af57d32dfdf7cde8cef87fd41",
-        "ml4t_manifest_sha256": "5258c7bd6e49a847afd875cf7346eb2f4b4fcd462bce579535a80d81de891fa8"
+        "framework_manifest_sha256": "4fe816c44263c0729af1af1d26ab47f6237abdab9c10f02dff3b506a3869d700",
+        "ml4t_manifest_sha256": "33cac6b620a39bf3ed2a5845ab86d7bbed89dbf8d16606812634fea1deda4716"
       },
       "excluded_surfaces": {
         "accepted_unfilled_orders": "not exposed by every native runner",
@@ -708,13 +708,13 @@
     {
       "case_study": "crypto_perps_funding",
       "engine_seconds": {
-        "framework": 8.64055325195659,
-        "ml4t": 1.861964081064798,
+        "framework": 2.87804518698249,
+        "ml4t": 0.7094007299747318,
         "single_run_diagnostic_only": true
       },
       "evidence": {
-        "framework_manifest_sha256": "fc1ddaaf85d1bce9f43e6f7afd2fb9c7398bf4b2cdbb703e5fc670fe5f7e98ad",
-        "ml4t_manifest_sha256": "7b84318804e49728015771eae24b58a82c63963fd5e77167bb7765987f3d5707"
+        "framework_manifest_sha256": "5b5873188d1f8731a57f302831809599bcdf6ec72dd225ffc1e6ca0b40b72745",
+        "ml4t_manifest_sha256": "5badb0761f0665e00873f7490c775bcaf0bcdf2e0c5943c09d968da3632dcd89"
       },
       "excluded_surfaces": {
         "accepted_unfilled_orders": "not exposed by every native runner",
@@ -785,13 +785,13 @@
     {
       "case_study": "fx_pairs",
       "engine_seconds": {
-        "framework": 1.0617584569845349,
-        "ml4t": 0.2712107199477032,
+        "framework": 0.39610078401165083,
+        "ml4t": 0.15986847499152645,
         "single_run_diagnostic_only": true
       },
       "evidence": {
-        "framework_manifest_sha256": "63b30da67c7b8315b1bfa355479d78d083046543cabb8f49e5fa0083a6338697",
-        "ml4t_manifest_sha256": "84ed03c40397d27e5024027ee66d0c0647774d4a26548960ca0fa062dc14036c"
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         "accepted_unfilled_orders": "not exposed by every native runner",
@@ -852,13 +852,13 @@
     {
       "case_study": "fx_pairs",
       "engine_seconds": {
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-        "ml4t": 0.367529945098795,
+        "framework": 0.2652946129674092,
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         "single_run_diagnostic_only": true
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       "evidence": {
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-        "ml4t_manifest_sha256": "5a17717ceebcf9ac1cedd534815b60da584f6c3fb654671fd98420ea3e1b37d7"
+        "framework_manifest_sha256": "9bc9c3020fda3549d772d6de2464138cc0ff710c845c29e2ca363e1a54e13b4f",
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       },
       "excluded_surfaces": {
         "accepted_unfilled_orders": "not exposed by every native runner",
@@ -919,13 +919,13 @@
     {
       "case_study": "fx_pairs",
       "engine_seconds": {
-        "framework": 1.0406507139559835,
-        "ml4t": 0.17209112795535475,
+        "framework": 0.4294113659998402,
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         "single_run_diagnostic_only": true
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       "evidence": {
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-        "ml4t_manifest_sha256": "32edb292c16b71cb2fb06225d6cef7e34409cbdbd26b62cfe1dc1718412703ea"
+        "framework_manifest_sha256": "79035767c67ffb9b102972fd70dcfd0562e021680186b37e4cb9db606591d019",
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       },
       "excluded_surfaces": {
         "accepted_unfilled_orders": "not exposed by every native runner",
@@ -1003,13 +1003,13 @@
     {
       "case_study": "fx_pairs",
       "engine_seconds": {
-        "framework": 0.9791555179981515,
-        "ml4t": 0.17271842202171683,
+        "framework": 1.0083705990109593,
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         "single_run_diagnostic_only": true
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       "evidence": {
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-        "ml4t_manifest_sha256": "ebb575c8a173bb875d61ae646d6f96a978305ffe733ac4820935f83591139b0e"
+        "framework_manifest_sha256": "6c63ce8dee1eec909a0d06ae7629d93aa3a71edd04ba657edb110bb9212b32f3",
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       },
       "excluded_surfaces": {
         "accepted_unfilled_orders": "not exposed by every native runner",
@@ -1080,13 +1080,13 @@
     {
       "case_study": "us_equities_panel",
       "engine_seconds": {
-        "framework": 1.2900436379713938,
-        "ml4t": 38.869148430996574,
+        "framework": 1.1930540719768032,
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         "single_run_diagnostic_only": true
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       "evidence": {
-        "framework_manifest_sha256": "acdc68f862f8ba07917fd16a59a19ec5ac6825b8a26f5ec8c16bc96c9bf4a1e7",
-        "ml4t_manifest_sha256": "88ae2e84f8d3187a4f5d84e78fe8441f5bc652cb08ce4d5591018edbc50d5587"
+        "framework_manifest_sha256": "f9883ea4e6e1f8662b829ddcdb1253490d75539610560497265080ffd691c7b0",
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       },
       "excluded_surfaces": {
         "accepted_unfilled_orders": "not exposed by every native runner",
@@ -1147,13 +1147,13 @@
     {
       "case_study": "us_equities_panel",
       "engine_seconds": {
-        "framework": 37.01605061593,
-        "ml4t": 26.629379056044854,
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       "evidence": {
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       },
       "excluded_surfaces": {
         "accepted_unfilled_orders": "not exposed by every native runner",
@@ -1214,13 +1214,13 @@
     {
       "case_study": "us_equities_panel",
       "engine_seconds": {
-        "framework": 616.757336596027,
-        "ml4t": 89.94735079002567,
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       "evidence": {
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-        "ml4t_manifest_sha256": "15b5f87860d12c3f895c827d10bc867361a1e63347db0b96d69472775e161691"
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       },
       "excluded_surfaces": {
         "accepted_unfilled_orders": "not exposed by every native runner",
@@ -1291,13 +1291,13 @@
     {
       "case_study": "us_equities_panel",
       "engine_seconds": {
-        "framework": 344.35249897500034,
-        "ml4t": 80.43836854794063,
+        "framework": 106.2881790450192,
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       "evidence": {
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       },
       "excluded_surfaces": {
         "accepted_unfilled_orders": "not exposed by every native runner",
@@ -1358,13 +1358,13 @@
     {
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       "engine_seconds": {
-        "framework": 50.57741306605749,
-        "ml4t": 28.115149467950687,
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