Trade Execution Modeling

Overview

Provide more realistic execution assumptions for backtests, including slippage models, market-impact estimation, and execution-algorithm principles. This skill is for backtest simulation only and does not involve live order execution.

Slippage Models

Why Slippage Models Are Needed

Idealized backtest: filled at the close, zero slippage
Real world:
1. The order book has a bid-ask spread
2. Large orders push prices (market impact)
3. Execution is delayed (there is latency from signal to fill)

No slippage model -> overly optimistic backtest -> losses in live trading

Do not retype these models. All four are implemented and tested in src/quantlib/impact.py; import them. The tested versions validate their inputs, a zero ADV raises instead of dividing by zero, and a negative delay_bars raises instead of silently introducing look-ahead bias.

from src.quantlib.impact import fixed_slippage, linear_impact, sqrt_impact, delayed_execution

1. Fixed Slippage Model

fixed_slippage(price=100.0, direction=1, bps=5.0)   # 100.05  (buy pays up)
fixed_slippage(price=100.0, direction=-1, bps=5.0)  #  99.95  (sell receives less)

direction is 1 to buy or -1 to sell, and must be exactly one of those, it multiplies the impact, so an unchecked 2 would silently double the modelled cost. bps defaults to DEFAULT_SLIPPAGE_BPS (5.0).

Reference fixed-slippage assumptions by market: