Slippage: Live Trading | Definition, Example, and Real-Time Scenarios

Slippage is probably the enemy number one for short-term traders. What is slippage in live trading and how much slippage can you expect to have in live trading? What is slippage in real trading?

Slippage in trading is a hidden cost that is difficult to quantify. It’s the difference between a theoretical price and the price you get in live trading. In this article, we provide you with facts about slippage in live trading in a select choice of three different ETFs. Slippage is most likely lower than you realize.

First, let’s define what slippage in trading is:

What is slippage in trading?

Slippage is the difference between fictional results when backtesting strategies and the actual results in real life adjusting for commissions and transaction costs. It’s a “hidden” cost and is based on the transaction.

For example, a backtest might simulate an entry price of 38.09, but in live trading, you get a price of 38.1. This means you get a worse price in live trading (assuming you bought the asset). This difference might not sound much, but done many times, it can amount to significant amounts of money over a year.

Another example: If your backtest shows an entry on 100, in live trading this strategy might buy those shares at 100.02 – not 100. That means your strategy will be less profitable than when testing. This is slippage!

Slippage is not the same as commissions. Commissions are a cost we know. However, costs related to buying and selling are not always easy to measure. When you backtest a strategy, the entry and close are estimated on an executed price.

Our experience tells us that the backtest results are always worse than real trading. But by how much?

It depends on the assets and strategies you are trading. SPY (S&P 500) has significantly less slippage than SIL (Silver Miners), for example, and breakout strategies are more costly to chase than mean reversion. It boils down to several factors. Thus, just using an arbitrary number like 0.1% or 0.05% doesn’t make much sense, in our opinion. Please read the link above to commissions in trading. We have established that we pay around 0.025% for a round trip in QQQ and SPY – including both commissions and slippage.

An example of slippage in trading – real trading

Let’s assume you want to buy shares in Apple. Apple might have a bid of 172.05 and an offer at 172.12. If you want to buy Apple you have to hit the offer at 172.12 or put in a lower bid. If you bid 172.07, for example, you risk not getting any shares unless someone hits your bid.

Slippage in trading is a hidden cost that a backtest can’t capture. When we backtest strategies, we always assume a negative slippage to allow for a margin of safety. We always expect live trading to have a negative slippage, but we also assume that the strategy gets worse over time.

Why is there slippage in trading?

There are mainly two factors determining the slippage in a trade:

Volume is the main determinant of slippage

The most important factor is probably volume. The more volume in an instrument, the tighter the spreads (the difference between the bid and the ask).

However, even high volume might not stop an asset from having high spread:

High volatility means more slippage

If an instrument or asset has high volatility, traders in the market offset this by bidding lower and offering higher. This means that the bid and offer diverge and thus you need to pay more if you want to buy and receive less if you are selling.

How can you measure slippage in trading?

There is only one method to measure slippage in trading: to compare executed prices in live trading to the historical data you perform backtests on.

This is a pretty boring task, but nevertheless very important for your strategy development.

The results in this article are based on daily bars: we go long or short with market orders at the close and we cover our positions n bars later at the close or open. We send our orders just a few seconds before the market closes or opens. We have described in a previous article how to enter and exit positions at the close. We use Amibroker and Tradestation to buy and sell at the close, and we go into code and details in our Amibroker course.

Thus, all the results are based on the price action in the last seconds before the close or in the first seconds of a trading day. This is official trading hours, not aftermarket or before the bell. Our broker is Interactive Brokers.

Slippage in XLP – live trading:

Trading XLP is not the most exciting thing in the world. Together with another ETF, XLU, both are probably the most boring trading vehicle there is. But in trading, boring is good. It might be more interesting day trading Tesla or other hot stocks, but we believe you are less likely to make any money compared to XLP trading.

XLP is a slow-moving ETF that consists of stocks like Procter&Gamble (PG), Coca-Cola (KO), Pepsi (PEP), Wal-Mart (WMT), Costco (COST), Mondelez (MDLZ), Philip Morris (PM), Altria (MO), Colgate (CL), and Estee-Lauder (EL). Because XLP has rarely any sudden and sharp moves, the spread between the bid and ask is always very low, normally 1 cent.

The XLP is a highly liquid ETF with more than 10 million shares traded daily. The high volume and the low volatility make the spread small at all times.

Live trading and slippage in XLP:

We looked at our executed prices in XLP and compared them to data in Norgate and free data from Yahoo!finance. We used our trading records for the last six months and we got the following results after about 50 executed trades:

Those 50 trades in XLP showed a positive slippage of about 1 cent. In other words, we get a better price than simulated in our backtests!

This was rather surprising, and not expected. A few outliers contribute to this, but overall, we got a better price in 40% of the trades. We expect this to gravitate toward zero or even slightly negative in the future.

Live trading and slippage in QQQ

Unfortunately, our results are not so positive for QQQ:

In QQQ, we have a negative slippage of on average 2 cents per trade, ie. 4 cents on a roundtrip. This is still a minuscule slippage of 0.01%, far away from ruining any strategy.

Live trading and slippage in SPY

Our third ETF for the day is SPY, the ETF that tracks the S&P 500, the oldest ETF around. Just like in QQQ we have a negative slippage:

In SPY, we have a negative slippage of 1.5 cents on average, 3 cents for a roundtrip. This is less than 0.01% per trade and has minimal impact on the results.

Ending remarks about slippage in live trading:

Picking the right assets to trade is important. Our trade records show that slippage is minuscule in highly liquid instruments like QQQ and SPY. For XLP, which is both liquid and slow, we had a positive slippage, although this was skewed by a few outliers.

Epilogue: How Bid-Ask Spreads/slippage Influenced My Trading Profits Negatively

In 2016 the SEC implemented a 5 cents spread rule in certain stocks. How did this influence my trading strategies?

Why bid-ask spreads increased

On October 3 2016, SEC implemented the Tick Size Pilot Program.

Put short, there will be a minimum of 5 cents spread in specified stocks.  The entire idea behind the program is for the SEC to determine if increasing the spreads for thinly-traded stocks will improve liquidity in the market. The SEC is hypothesizing that larger spreads will provide brokers with more incentive to make markets in these stocks.

A lot of “experts” argue that narrow spreads are bad for the markets. Here is a random explanation for that:

In my opinion, decimalization is a negative because it narrowed the spreads. On the surface you would think it would be better for the markets, but narrower spreads mean less profit for market makers. Less profit leads to less capital and less capital leads to less liquidity” (Forbes).  A market maker’s job is to sit at the ready for a trader who wants to come to buy or sell shares.  That means the market maker must have shares on both the bid and the ask.  If a trader decides to suddenly sell 10K shares, it’s the job of the market maker to absorb that sale.  In order to generate profits, market makers profit from the spread. Prior to decimalization, a market maker could buy shares at 10 1/8 and offer to sell at 10 1/4, which is a 6.25-cent profit in the spread.  With decimalization, their profits diminished greatly.  While this may seem better for investors, market makers have become less inclined to provide a market for stocks that are thinly traded.  That means a small-cap investor who wants to sell 10K shares may not always have a market maker there to absorb the sale.  As a result, prices could suddenly drop, leading the investor to receive far less from his or her sale than if market makers were there creating the spread.  These quick drops and spikes in prices have resulted in more volatility, which further increases risk for investors of small-cap stocks.

This is a typical phrase I have been hearing about why bigger spreads are good.

But bigger bid-ask spreads are detrimental to trading profits

I have detailed statistics going back many years for a lot of the stocks affected by the pilot program. So I decided to look at my own data and how a bigger bid-ask spreads have affected my P/L. I enter trades purely mechanical and exit 90% mechanically. I have not changed anything as regards my trading due to the pilot program.  Most of these stocks are small-cap and low-volume stocks. All my stocks in the pilot group are from 200k daily on average to about 750k.

In total, I have 65 stocks that I regularly trade which now have a minimum of 5 cents spread. I have divided my results into two time series: the 7 months before the implementation, and the 7 months after the implementation. In total it is hundreds of trades. Here are the results:

  • From 1st of March 2016 until the 3rd of October my average accumulated profits (per ticker) in affected stocks were 135% of my average for all stocks (per ticker) in my trading universe.
  • From the 3rd of October 2016 until the 1st of May 2017 my average accumulated profits (per ticker) in affected stocks have fallen to just 50% of the average of all stocks (per ticker).

Is this just the freak nature of randomness? It is anybody’s guess, but I don’t think so.

My simple conclusion is that I’m giving away money to market makers that previously ended up in my account.

I am not gonna go into any politics on this blog, but it is quite obvious that the industry is making it favorable for the members and the bigger institutions.

Slippage Glossary

  1. Slippage: The difference between the expected price of a trade and the actual execution price.
  2. Market Order: A type of order that is executed immediately at the current market price, often subject to slippage.
  3. Limit Order: An order to buy or sell at a specific price or better, which may result in partial or no execution if the price doesn’t reach the limit.
  4. Stop Order: An order to buy or sell a security once it reaches a specific price, which can be subject to slippage if triggered.
  5. Volatility: A measure of the price fluctuations of an asset, which can impact slippage.
  6. Liquidity: The ease with which an asset can be bought or sold without causing significant price movements, affecting slippage.
  7. Order Book: A list of buy and sell orders for a specific asset, showing potential trading opportunities and slippage risk.
  8. Market Depth: The quantity of orders at different price levels in an order book.
  9. Spread: The difference between the bid (buy) and ask (sell) prices, impacting slippage when entering or exiting trades.
  10. Fill or Kill (FOK): An order that must be executed immediately in its entirety or canceled, avoiding partial execution and potential slippage.
  11. Immediate or Cancel (IOC): An order that is partially executed immediately, with the remaining portion canceled, minimizing slippage risk.
  12. Gapping: Rapid price movement between two trading periods, causing slippage on open or close orders.
  13. Slippage Risk: The possibility of experiencing slippage due to market conditions.
  14. Trade Execution: The process of placing and completing a trade, potentially leading to slippage.
  15. Order Execution Algorithm: Automated strategies designed to minimize slippage during trade execution.
  16. Dark Pools: Private exchanges where large orders are executed anonymously, potentially reducing slippage.
  17. High-Frequency Trading (HFT): Trading strategies that aim to profit from small price movements and may exacerbate slippage.
  18. Trading Desk: A team or department responsible for executing trades, managing slippage, and risk.
  19. Market Impact: The effect of a large order on an asset’s price, leading to slippage.
  20. Requote: When a broker offers a different price for an order than initially quoted, causing slippage.
  21. Price Improvement: When a trade is executed at a better price than initially expected, reducing slippage.
  22. Slippage Tolerance: The maximum acceptable difference between the expected and actual execution price.
  23. Slippage Control: Strategies and tools used to manage and minimize slippage risk.
  24. Sip and Ping: HFT strategy involving placing small orders (sips) to gauge market interest and then executing a larger order (ping).
  25. Trading Venue: The platform or exchange where trading occurs, affecting slippage.
  26. Retail Order Flow: Orders from individual traders that may contribute to slippage in smaller-cap stocks.
  27. Block Trade: A large order that is often executed off-exchange to minimize slippage.
  28. VWAP (Volume-Weighted Average Price): A trading benchmark used to minimize slippage by executing trades over time.
  29. TWAP (Time-Weighted Average Price): A trading strategy that minimizes slippage by executing trades evenly over a specific time period.
  30. Market Impact Cost: A measure of the cost associated with slippage due to a trade’s size and speed of execution.
  31. Stop-Loss Order: An order designed to limit potential losses, but it can experience slippage if triggered in volatile markets.
  32. Slippage Slippage: The phenomenon where slippage itself can lead to additional slippage in subsequent orders.
  33. Overnight Slippage: Slippage that occurs due to market movements during extended trading hours.
  34. Halt or Suspension: When trading in a security is temporarily stopped, potentially causing slippage upon resumption.
  35. Frontrunning: Illegally trading ahead of a client’s order to take advantage of anticipated slippage.
  36. Price Volatility Bands: Pre-defined price levels used to manage slippage in automated trading systems.
  37. Black Swan Event: An extremely rare and unpredictable event that can cause significant slippage.
  38. Slippage Table: A record of historical slippage experiences for a trading strategy or asset.
  39. Brokerage Fee: Commissions and fees charged by brokers, impacting overall trading costs, including slippage.
  40. Market-Making: A strategy employed by traders or firms to provide liquidity and minimize slippage for other market participants.
  41. Tracking Error: The discrepancy between a portfolio’s performance and its benchmark index, partly due to slippage.
  42. TCA (Transaction Cost Analysis): The assessment of all costs related to trading, including slippage.
  43. Latency: The time delay between sending an order and its execution, impacting slippage.
  44. Arb (Arbitrage): A trading strategy that exploits price differences between related assets, minimizing slippage risk.
  45. Market Order Impact: The immediate effect of a market order on an asset’s price and potential slippage.
  46. Fill Rate: The percentage of an order executed successfully without slippage.
  47. Spoofing: Illegally manipulating order book data to create artificial slippage.
  48. Trade Reversal: A strategy used to profit from slippage by quickly reversing a trade.
  49. Execution Venue Analysis: Evaluating different exchanges or platforms to choose the one with minimal slippage.
  50. Slippage Slalom: A trading strategy that attempts to navigate volatile markets and minimize slippage through skillful order execution.

All in all, slippage in live trading is pretty low if you stick to the most traded instruments and stick to your rules.


– Why is slippage considered a hidden cost?

Slippage is often regarded as a hidden cost because it’s not as easily quantifiable as explicit costs like commissions. It represents the divergence between the expected execution price from testing and the real-world execution price in live trading.

– What causes slippage in trading?

Slippage can occur due to various factors, including differences in bid and ask prices, market volatility, order size, and the timing of order execution. When placing an order, the execution price might not be exactly as expected due to these factors.

– Is slippage consistent across all trading instruments?

Slippage in trading can vary depending on the specific instruments being traded. Different markets and assets may experience varying levels of slippage. It’s important to consider the potential impact of slippage when trading different instruments.

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