{"id":193407,"date":"2023-07-14T09:58:42","date_gmt":"2023-07-14T13:58:42","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=193407"},"modified":"2023-07-14T09:58:27","modified_gmt":"2023-07-14T13:58:27","slug":"slippage-in-model-backtesting","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/slippage-in-model-backtesting\/","title":{"rendered":"Slippage in Model Backtesting"},"content":{"rendered":"\n<p>A precious lesson I learned during my venture over programming an independent backtesting engine for new trading model was&nbsp;<strong>slippage<\/strong>. A feature that cannot be easily neglected and can blur the ultimate profit profile.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-what-is-slippage\">What is Slippage?<\/h4>\n\n\n\n<p>Simply speaking, slippage is a fraction of&nbsp;<a href=\"https:\/\/en.wikipedia.org\/wiki\/Stock\" target=\"_blank\" rel=\"noreferrer noopener\">stock<\/a>&nbsp;price which you need to assume as a deviation from the price you are willing to pay. In model backtesting the slippage is extremely important. Why? Let\u2019s imagine your model generates a signal to buy or sell a stock on a day&nbsp;<em><strong>t<sub>i<\/sub><\/strong><\/em>, i.e. after when the market has been closed and your stock trading history has been updated with a stock close price.<\/p>\n\n\n\n<p>Since you can\u2019t buy\/sell this stock on day&nbsp;<em><strong>t<sub>i<\/sub><\/strong><\/em>, your algo-trading system in connection to your model rules places a new order to be executed on day&nbsp;<em><strong>t<sub>i+1<\/sub><\/strong><\/em>. Regardless of the position the stock holds, you don\u2019t know the price on the following day at the opening of the market. Well, in real-time trading \u2013 yes. However, in the backtesting of your model this information is available, e.g. you have historical stock prices of IBM in Aug 2008, so you&nbsp;<em>know<\/em>&nbsp;the future.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-slippage-in-backtesing\">Slippage in Backtesing<\/h4>\n\n\n\n<p>Now, you may wish to program your backtesting engine to buy\/sell this stock for you on&nbsp;<em><strong>t<sub>i+1<\/sub><\/strong><\/em>&nbsp;day at the open, mid-day, intra-day, or even close price. The choice is yours. There are different strategies. Close price is good option for consideration as long as you have also a track of intra-day trading on&nbsp;<em><strong>t<sub>i+1<\/sub><\/strong><\/em>, therefore you&nbsp;<em>have time<\/em>&nbsp;to analyze the intra-day variability, take extra correction for extreme volatility or black-swans, and proceed with your order with extra caution. But if you program a simple approach in your order execution (e.g. buy at open price) you assume some risk of the price not to be in your favour.<\/p>\n\n\n\n<p>Quite conservative approach to compensate for systematic unexpected slippages in the stock price when your order has been sent to the broker is to assume in simulations (backtesting) a fixed slippage working against your profits every time. Namely, you don\u2019t buy\/sell your stock at the price as given on&nbsp;<em><strong>t<sub>i+1<\/sub><\/strong><\/em>&nbsp;day in your historical price table. You assume the&nbsp;<strong>slippage<\/strong>&nbsp;of&nbsp;<strong>\u0394<em>S<\/em><\/strong>. If the price of the stock is&nbsp;<em><strong>P<\/strong><\/em>&nbsp;your slippage affects the price:<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"693\" height=\"37\" data-src=\"\/campus\/wp-content\/uploads\/sites\/2\/2023\/07\/quant-at-risk-slippage-model-backtesting-1.png\" alt=\"\" class=\"wp-image-193408 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2023\/07\/quant-at-risk-slippage-model-backtesting-1.png 693w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2023\/07\/quant-at-risk-slippage-model-backtesting-1-300x16.png 300w\" data-sizes=\"(max-width: 693px) 100vw, 693px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 693px; aspect-ratio: 693\/37;\" \/><\/figure>\n\n\n\n<p>where&nbsp;<strong><em>P\u2032<\/em><\/strong>&nbsp;is the executed price for your simulated order. The sign&nbsp;\u00b1&nbsp;has double meaning. To allow you to understand it, let me draw two basic rules of the slippage in backtesting:<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"692\" height=\"199\" data-src=\"\/campus\/wp-content\/uploads\/sites\/2\/2023\/07\/quant-at-risk-slippage-model-backtesting-2.png\" alt=\"\" class=\"wp-image-193410 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2023\/07\/quant-at-risk-slippage-model-backtesting-2.png 692w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2023\/07\/quant-at-risk-slippage-model-backtesting-2-300x86.png 300w\" data-sizes=\"(max-width: 692px) 100vw, 692px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 692px; aspect-ratio: 692\/199;\" \/><\/figure>\n\n\n\n<p>If your trading decision is to go&nbsp;<strong>long<\/strong>&nbsp;you always buy at the price higher by&nbsp;<strong><em>P \u00d7 \u0394S<\/em><\/strong>&nbsp;than P and you sell the stock at the price lower than&nbsp;<strong><em>P<\/em><\/strong>, again by&nbsp;<strong><em>P \u00d7 \u0394S<\/em><\/strong>. Reversely, if you open a&nbsp;<strong>short<\/strong>&nbsp;position, you buy lower and sell higher when closing the same position.<\/p>\n\n\n\n<p>The amount of slippage you should assume varies depending on the different conditions. If you are involved in lots of algorithmic trading operations, you probably are able to estimate your slippage. In general, the simulated slippage shouldn\u2019t be more than 2%. One allows for higher values but this becomes useful for less liquid assets in trading.<\/p>\n\n\n\n<p>If you forget to include the slippage in your backtesting black-box, it may occur that your model is extremely profitable and you risk a lot in practice. On the other hand, adding it to your test may make your day less bright as it has started. But don\u2019t worry. Keep smiling as a new day is a new opportunity, and life is not about avoiding the risks but managing them right.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><em>Originally posted on<a href=\"https:\/\/quantatrisk.com\/2013\/01\/26\/slippage-in-model-backtesting\/\"> Quant at Risk<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A precious lesson I learned during my venture over programming an independent backtesting engine for new trading model was slippage<\/p>\n","protected":false},"author":710,"featured_media":181344,"comment_status":"open","ping_status":"closed","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,338,341],"tags":[851,4922,15573],"contributors-categories":[13732],"class_list":{"0":"post-193407","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-ibkr-quant-news","9":"category-quant-development","10":"tag-algo-trading","11":"tag-econometrics","12":"tag-slippage-in-model-backtesting","13":"contributors-categories-quant-at-risk"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.9 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Slippage in Model Backtesting | IBKR Quant<\/title>\n<meta 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