What is the best slippage percentage for crypto backtesting?

There is no single "best" percentage, as it depends on the asset and volatility. For major pairs like BTC/USDT, a static slippage of 0.05% to 0.1% might be reasonable during calm periods. However, for altcoins or during high volatility, slippage can easily exceed 1%. It is better to use a dynamic model that adjusts based on market conditions rather than a fixed number.

How do I prevent lookahead bias in my slippage model?

Lookahead bias occurs when your model uses future data to calculate slippage. To prevent this, ensure your custom slippage logic only uses data available at the time of the trade signal. For example, do not use the high of the current bar to calculate slippage for a trade that triggers at the open. Use the previous bar's high or the current bar's open, depending on your execution logic.

Can Backtrader simulate partial fills?

Backtrader's default broker does not natively support partial fills in all scenarios. However, you can simulate partial fills by modifying the notify_order method in your strategy or by creating a custom broker class. This is important for large orders that cannot be filled instantly in a single price level.

Why does my strategy perform worse with slippage enabled?

This is expected behavior. Slippage represents the cost of trading in a real market. If your strategy's profit margin is thin, even a small amount of slippage can turn a winning trade into a losing one. This indicates that the strategy may not be robust enough for live trading or that the slippage model needs to be calibrated to more realistic levels.

How do I test my custom slippage model?

Run your backtest with different slippage parameters and compare the results. You can also use a "sanity check" by running the backtest with zero slippage and then with a very high slippage (e.g., 5%). The difference in performance should be significant. If it is not, your strategy might be too insensitive to execution costs.

Backtrader: Customizing Slippage Models

Your backtest shows a 40% return, but your live account is bleeding capital. Why? Because your backtrader slippage model assumes perfect fills that neve...

By Trader Algorítmico (Equipo Editorial) | Published: December 7, 2025

Your backtest shows a 40% return, but your live account is bleeding capital. Why? Because your backtrader slippage model assumes perfect fills that never happen in real crypto markets. Most traders run simulations on clean OHLCV data, ignoring the friction of spreads, latency, and order book depth. The result is a strategy that looks profitable on paper but fails the moment it touches a live exchange. In crypto, where volatility spikes can move prices 2% in seconds, a static slippage assumption is often a dangerous lie. This guide explores how to build dynamic execution logic in Backtrader that respects the chaotic nature of crypto tick data. We will move beyond simple percentage adjustments to models that react to market conditions. Slippage is the difference between the expected price of a trade and the price at which the trade is actually executed. In crypto markets, this gap widens significantly during high volatility or low liquidity periods. According to Interactive Brokers, slippage is a fraction of the stock price you must assume as a deviation from the price you are willing to pay.

Related Products

ata | orderflowdataextractor | alvanor

Back to Blog | Indicators | Strategies | About