Backtest Cost Model

Once a strategy passes the feasibility screen, the backtest engine still needs explicit cost settings. If commission, slippage, and impact are left at optimistic defaults, the simulation is still fiction.

The Problem

Most mistakes at this stage are implementation mistakes: missing volume in the feed, flat slippage for every asset, or no participation cap on large orders. The result is a backtest that claims to include costs while still materially understating them.

The Pattern

WRONG

import numpy as np

# Zero-cost backtest - fiction
positions = compute_positions(signals)
gross_returns = positions * asset_returns
sharpe = gross_returns.mean() / gross_returns.std() * np.sqrt(252)  # overstated