To analyze slippage in algorithmic trading, compare the price your algorithm intended to trade at with the price the trade was actually executed at. The difference is called slippage. Track this difference across multiple trades to understand its average impact. It helps you measure the hidden cost of trading and refine your strategy for better profits.

What is Slippage in Algorithmic Trading?

Slippage happens when your trade is executed at a different price than what your algorithm expected. It usually occurs due to fast market movements, low liquidity, or delays in execution. In algo trading, where speed and accuracy matter a lot, even small slippage can impact your overall returns.

How to Calculate Slippage in Algo Trading?

Slippage = Executed Price – Expected Price

If your algorithm planned to buy a stock at ₹100, but it got filled at ₹101, your slippage is ₹1 per share. If you bought 100 shares, that’s ₹100 lost due to slippage. You should calculate average slippage across multiple trades to see how much it's eating into your profits.

Why is Slippage a Big Deal in Algo Trading?

In algorithmic trading, where trades happen in milliseconds and profits are sometimes in paise, slippage can make or break your strategy. If your strategy depends on small profits per trade (scalping), even ₹0.20 slippage per order can turn profits into losses. Analyzing and reducing slippage is a must for consistent performance.

Top Causes of Slippage in Automated Trading

Here are the main reasons why slippage happens in algo trades:

• 📈 High volatility in the stock or market
• 📉 Low liquidity (not enough buyers/sellers)
• ⏳ Delay in order routing or broker response time
• 🧾 Market orders used instead of limit orders
• 📦 Executing large trades that move the price

Understanding the cause can help you control slippage more effectively.

How to Reduce Slippage in Algo Trading?

Here are simple ways to minimize slippage:

• ✅ Use limit orders instead of market orders
• ✅ Trade in highly liquid stocks
• ✅ Split big orders into smaller chunks
• ✅ Avoid trading during news or volatile events
• ✅ Choose a broker with fast execution speed

These changes can help your algorithm stick to planned prices and improve actual returns.

How to Backtest Slippage in Your Algo?

To analyze the real impact of slippage, you should include it in your backtest. Add a small cost (like ₹0.10 or ₹0.25) to every trade in your test results. This shows how your strategy would perform in live conditions. Many times, a strategy looks good without slippage, but fails in the real market because it wasn't tested with execution costs.