Wednesday, December 7, Testing and Analysis of Algorithmic Trading Strategies in MATLAB Part 4 — Genetic Algorithms. This post is about how important is to use different types of optimisation methods such as genetic algorithms and parallelisation to get results faster. Genetic Algorithms Optimisation Despite the fact that the genetic evolutionary algorithm principle is very well explained in the MathWorks webinars, in the examples, however, it is used only for optimisation of the choice of a strategy group from a set. This is a good example of the use of these algorithms, however, it happens that there is a need to set many variables with significant intervals for one strategy, you don't get by with one iteration and the parallelisation of processes — calculations can take several days. Certainly, there are strategies in the final stage of optimisationwhen we almost surely know the trading strategy is successful, we can wait for several days as well or rent the whole cluster - the result might be worth it. However, if we need to "estimate" the results of a "bulky" strategy and decide if it is worth it to spend the time, then genetic algorithms may be perfectly suitable. We provide the possibility to use three methods to optimise the system in WFAToolbox: Trading method — it is a usual mode of sorting in which you will see all matlab suboptimal results. It gives maximum accuracy. Parallel method — all kernels of your CPU will be used. It does not allow to see intermediate results, but significantly speeds up the operation. It gives maximum accuracy during increase of computation speed. Genetic method — it uses the evolutionary optimisation algorithm. It allows to see suboptimal matlab, but gives the result close to the best. It's not a very accurate method, but it's precise enough for the initial "run" of the strategy. Genetic Algorithms We are often asked if WFAToolbox - Walk-Forward Analysis Toolbox for MATLAB has the ability to use the GPU in calculations. Unfortunately, GPU is not suitable for all tasks and its use is very specific. In order to use it, you need to adjust the logic and the code of each strategy for graphic cores testing. Unfortunately, due to such non-universality of the method one cannot use GPU in WFAToolbox. Posted by Igor Volkov at Share to Twitter Share to Facebook Share to Pinterest. Monday, December 5, Testing and Analysis of Algorithmic Trading Strategies in MATLAB Part 3 — Visualisation of Process. Continuing Part 2 of the discussion of problems and solutions in testing and analysis of algorithmic trading strategy in MATLAB, I invite you to read this post about problem of unavailability of visualisation of the processes in modern software solutions for testing trading systems. Visualisation of Testing Process In my work experience, I often analysed other popular platforms for trading strategy testingsuch as TradeStationMetaStockMulticharts etc. The thing is that when we don't see the results of the intermediate, sub-optimal values of optimised parameters, we often throw away gold along with the dirt. The matter is because of an overly broad sampling, the strategy adjusts the parameters the way we either see a " perfect strategy " which fails system real life or see one or two deals, which are supposedly the best because it was selected such time interval data where the best trading strategy would be "buy-and-hold", but why are then other strategies necessary for? The analysis of values in the N-dimensional spaces can definitely be an alternative, but has several limitations: What if there are more than 4 dimensions? When code see what signals and at what frequency they appear in the price range, you have almost all the necessary visual representation of your strategy: Posted by Igor Volkov at 1: Wednesday, November 30, Testing and Analysis of Algorithmic Trading Strategies in MATLAB Part 2 — Easy-to-use GUI. In this post, in continuation of Part 1I will try to describe the most common problems which occur while testing algorithmic trading strategies in MATLAB when using one's own groundwork or the code from the automated trading webinars. Someone experienced users of MATLAB, in particularmight argue that the use of ready-made functions is not any worse and actually is even sometimes better and more convenient that the static GUI. It's possible, but a GUI however has a number of advantages: For new and not only users of MATLAB it is much more convenient to use a Matlab with buttons and entry fields than to search in the code; therefore, there is a GUI even in the MathWorks Toolboxes in most cases because it is more convenient. It allows focusing only trading the code of code strategy because use of a GUI does not at all imply that it somehow limits your ability to write a strategy. Thus, in WFAToolboxwe created a possibility to write any codes for your strategy, using any of MATLAB toolboxes and working with multiple assets for the strategies such as pairs trading, basket trading or triplet arbitrage, etc. In order to easily master the patterns of code to create your strategies, not only we created detailed WFAToolbox Documentationbut also WFAToolbox Video Tutorial system, which provides an opportunity to a full-scale work with the app in a few minutes. Easy-to-use GUI of WFAToolbox. Posted by Igor Volkov at 6: Tuesday, November 29, Testing and Analysis of Algorithmic Trading Strategies in MATLAB Part 1 - Introduction. Hello, my name is Igor VolkovI have been system algorithmic trading strategies since and have worked in several hedge funds. In this article, I would like to discuss difficulties arising on the way of MATLAB trading strategies developer during testing and analysis, as well as to offer possible solutions. I have been using MATLAB for testing of algorithm strategies since and I have come to conclusion that this is not only the most convenient research tool, but also the most powerful one because it makes possible using of complex statistical and econometric models, neural networks, machine learning, digital filters, fuzzy logic, etc by adding toolbox. The MATLAB language is quite simple and well documented, so even a non-programmer like me can master it. How Code All Started It was if I am not mistaken when the first webinar on algorithmic trading in MATLAB with Ali Kazaam was released, covering the topic of optimising simple strategies based on technical indicators, etc. They served as a starting point for research and enhancement of a testing and analysis model which would allow to use all the power of toolboxes and freedom of MATLAB actions during creation of one's own trade strategies, at the same time it would allow to control the process of testing and the obtained data and their subsequent analysis would choose effective portfolio of robust trading systems. Subsequently, Mathworks webinars have been updated every year and gradually introduced more and more interesting elements. Thus, the first webinar on pairs trading statistical arbitrage using the Econometric Toolbox was held inalthough the Toolbox of testing and analysis remained the same. In code, Trading Toolbox from Mathworks appeared which allowed to connect MATLAB to different brokers for execution of their applications. Although there were automatic solutions matlab execution of the transactions, from that point MATLAB could be considered a system for developing trading strategies with a full cycle: Why Should Every Algotrader Reinvent the Wheel? However, Mathworks has not offered a complete solution for testing and analysis of the strategies — those codes that you could get out of webinars were the only "elements" of a full system test, and it was necessary to modify them, customise them, and add them to the GUI for ease of use. It was very time consuming, thus posing a question: So the decision was made to create a product that would allow to perform the whole process associated with the testing and analysis of algorithmic trading strategies using a simple and user-friendly interface. Monday, November 7, Whoa?! What happened with the blog? Hi, My name is Igor Volkov and I'm the founder and developer of WFAToolbox - Walk-Forward Analysis Toolbox for MATLAB. WFAToolbox is an add-on that is aimed to ease the life of MATLAB users, who develop their own algorithmic trading strategies. This add-on has lots of benefits that are too many to enumerate here, but I will suggest you try it now by downloading its demo version from our official website: WFAToolbox - Walk-Forward Analysis Toolbox for MATLAB First of all, I would like to answer the following questions: Jev Kuznetsov is not the owner anymore The blog was trading from our friend, Jev Kuznetsov, who has moved to his other blog http: He concluded that Python is better than MATLAB for trading, which I considered to be false. MATLAB remains one of the best software in the world for algorithmic trading purposes IMHO I have some facts about this though for future discussion. From this moment the blog will be called MatlabTrading, which is much more understandable regarding the topics it will include. Furthermore, the domain name has been changed to www. I will be happy if we can all relate through comments in posts. Subscribe to our news to get alerted about the newest posts and events. Later on, we have plans to make Google Hangouts webinars. Don't miss it, click on "Follow" button at the upper right corner to join our community. What would you like to read in our blog posts? What topics can you suggest? Please write here in comments. Posted by Igor Volkov at 5: Tuesday, Trading 1, Intraday mean reversion. In my previous post I came to a conclusion that close-to-close pairs trading is not as profitable today as it used to be before A reader pointed out that it could be that mean-reverting nature of spreads just shifted towards shorter timescales. I happen to share the same idea, so I decided to test this hypothesis. This time only one pair is tested: Backtest is performed on second bar data from The rules are simple and similar to strategy I tested in the last post: The result looks very pretty: Posted by sjev at 8: Sunday, December 30, Is pairs trading dead? Bad news everybody, according to my calculations, which I sincerely hope are incorrect the classical pairs trading is dead. Some people would strongly disagree, but here is what I found: Let's take a hypothetical strategy that works on a basket of etfs: Each pair is constructed as a market-neutral spread. On each day, for each pair, calculate z-score based on day standard deviation. Transaction costs are not taken into account either. Here are the results simulated for several thresholds: Posted by sjev at 4: WFAToolbox - Walk-Forward Analysis Toolbox MATLAB Add-on for Developing Algorithmic Trading Strategies in MATLAB the easy way. Labels algorithmic trading 1 automated trading 1 backtesting 2 database 1 gaps 5 gui 1 intraday 1 MATLAB 4 mean reversion 2 news 1 opening quotes 1 pca 2 spread 4 strategy 2 trading strategies 2 webinars 3 wfatoolbox 5. Testing and Analysis of Algorithmic Trading Strate Theme images by gaffera. Visualisation of trading strategy testing process in MATLAB proposed in webinar. WFAToolbox - Walk-Forward Analysis Toolbox for MATLAB.
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