| """Fee, slippage and funding models for institutional-grade crypto backtests. |
| |
| Conventions (Binance USDT-M VIP0, verified 2026-10): |
| - taker fee 5 bps, maker fee 2 bps |
| - funding: charged at 8h ticks, cost = funding_rate * notional, sign |
| follows the position side (longs pay when rate > 0) |
| - slippage: square-root impact model k * sqrt(notional / ADV), floor 2 bps |
| |
| Everything here is a COST function: positive output = money lost. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import numpy as np |
| import pandas as pd |
|
|
| TAKER_FEE = 0.0005 |
| MAKER_FEE = 0.0002 |
| SLIPPAGE_FLOOR_BPS = 2.0 |
| IMPACT_K = 0.5 |
| DAILY_VOL_BPS_FALLBACK = 200.0 |
|
|
|
|
| def fee(notional: float, side: str = "taker") -> float: |
| return abs(notional) * (TAKER_FEE if side == "taker" else MAKER_FEE) |
|
|
|
|
| def slippage_cost(notional: float, adv_notional: float, |
| daily_vol_bps: float = DAILY_VOL_BPS_FALLBACK) -> float: |
| """Square-root impact cost in quote currency. |
| |
| impact_bps = k * sigma_daily_bps * sqrt(Q / ADV) |
| sigma_daily_bps is REALIZED daily volatility (the engine passes the |
| value computed from the data); the floor is 2 bps. |
| """ |
| if adv_notional <= 0: |
| raise ValueError("adv_notional must be positive") |
| bps = max( |
| SLIPPAGE_FLOOR_BPS, |
| IMPACT_K * daily_vol_bps * np.sqrt(abs(notional) / adv_notional), |
| ) |
| return abs(notional) * bps / 1e4 |
|
|
|
|
| def funding_cost( |
| positions: pd.Series, |
| funding: pd.Series, |
| prices: pd.Series, |
| ) -> pd.Series: |
| """Per-bar funding cost series, accrued only at funding timestamps. |
| |
| positions: position in units of base asset, indexed by bar start time. |
| funding: funding_rate at each 8h tick. |
| prices: mark/close price at each bar, for notional valuation. |
| |
| A bar starting at t covers [t, t+1h); a funding tick at t charges that |
| tick to the position held entering the tick, i.e. the position at t. |
| """ |
| tick_times = funding.index |
| pos_at_tick = positions.reindex(tick_times, method="ffill") |
| price_at_tick = prices.reindex(tick_times, method="ffill") |
| cost = pos_at_tick * price_at_tick * funding |
| return cost.reindex(positions.index, fill_value=0.0).fillna(0.0) |
|
|
