To analyze slippage impact in algorithmic trading, compare the price your algo expected to execute at with the actual execution price. This difference is the slippage. Track this over multiple trades to see how much it affects overall profit or loss. High slippage can make a profitable algorithm turn into a loss-making one. Regular analysis helps you improve trade timing and order types.

What is slippage in algorithmic trading?

Slippage happens when an order is executed at a different price than expected. In algo trading, slippage usually occurs due to high volatility, low liquidity, or delay in order execution. Since algorithms make fast decisions, even a few paise of slippage per trade can have a large impact over hundreds or thousands of trades.

How do you measure slippage?

Slippage is calculated by subtracting the expected order price from the actual executed price.

Formula: Slippage = Executed Price - Expected Price

If your algorithm expected to buy at ₹150, but the trade got executed at ₹151, the slippage is ₹1 per share. Track this value over many trades to understand its average impact on your trading strategy.

Why is slippage important in algorithmic trading?

Slippage affects profitability. If your algorithm relies on small margins, even a slippage of a few paise can turn a profitable trade into a loss. For example, if you target a profit of ₹1 per share, but slippage eats up ₹0.60, your net gain drops to ₹0.40 or worse. By analyzing slippage, you can refine your strategy for better returns.

What causes slippage in algorithmic strategies?

Common causes of slippage in algorithmic trading include:

• 📉 High market volatility
• 📊 Low liquidity in the traded stock
• ⏱️ Delay in order routing or execution
• 📦 Placing large market orders
• 🕒 Trading during news announcements or market opening/closing times

Knowing these factors can help you time your trades better and reduce slippage.

How do I reduce slippage in algo trading?

You can reduce slippage using these simple strategies:

• ✔️ Use limit orders instead of market orders
• ✔️ Break large orders into smaller parts (order slicing)
• ✔️ Avoid trading in illiquid stocks
• ✔️ Trade during high volume times for better execution
• ✔️ Use brokers with fast execution speeds

These steps will help your algorithm execute orders more efficiently and reduce unnecessary losses.

How to backtest slippage effect on your algo strategy?

To test slippage impact, include it in your backtest simulation. For example, you can assume ₹0.10 or ₹0.20 slippage per trade in your backtesting model. Then compare results with and without slippage. This will help you understand the real-world performance of your algorithm and adjust your expectations accordingly.