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Mechanical trading strategies

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mechanical trading strategies

Exploiting the edge from historical market patterns. Wednesday, February 21, Designing a Robust Mechanical Trading Strategy: A Best Practice in Strategies. This best practice post comes to us from Edward Heming, who is the author of the Lord Tedders trading blog. He discusses a few aspects of developing a reliable mechanical trading strategy and also covers the pros and cons of mechanical trading. Note that Henry Carstens has also made available a series of articles on the topic of developing trading systems. What I most like about Lord Tedders' article is the insight that researching system ideas is a great way to gain a feel for the market. For that reason, it can even benefit the discretionary trader. Those wanting to gain some of the benefits of system testing without the challenges of programming can look to the Odds Maker program developed by Trade Ideas or can follow the advice of Bonnie Lee Hill and utilize the drop-down menu testing platform available through Ensign Software. With such tools, it is easier strategies ever to truly determine if your ideas are providing you with a performance edge. Thanks to Edward for the insightful post. This can be anything from a statistical edge trending mechanical an execution edge arbitrage. Furthermore, this strategy must hold up over an extensive period of trades historically at least several hundred strategies must hold up in future trading which can be simulated. A mechanical system has several advantages that discretionary traders do not, such as the ability to perform quantitative and data mining analysis quickly and over extended historical periods. Additionally, mechanical systems can alleviate some of the emotional distress that accompanies discretionary trading — particularly among new traders. However, it is important to recognize that mechanical trading has several disadvantages as well. The first being that you must be able to quantify each strategies every trading decision that the system will make, secondly the mechanical system will have to be periodically adjusted just like a discretionary trader adjusts their methods either through inherent adaptivity, optimization, or diversification. Lastly, mechanical systems only work if one puts in the tremendous amount of time and effort required to program, test, debug, and continually adjust it. To design any mechanical strategy it is important to consider three things before anything else: Once you have determined this, it is easy to find your essential methodology because there are mechanical 4 ways to trade any market: Once you have determined your objective, market, timeframe and method you are ready to attempt to put together your first strategy. However, if you do not have extensive experience you will have to find a method that works. The very best way for the inexperienced trader to build a new system is to test ideas. This can be done in two ways — visually or programmatically. This is performed by taking an idea such as a moving average crossover and testing it with historical data on the given market and time frame by moving your charts forward from the past into the future and trading the way the system would — without future knowledge of the markets. However, I had to test nearly fifty or sixty ideas to get down to those ten strategies that work, and finally refine the process until I had found four of those ten systems that I found tradable. It sounds like a lot of work right? Well it was, but it also gave me a feel for those markets that is nearly as good as having traded those markets in real time. After doing this for some time, I felt that there had to be a more effective way to test ideas. And there is — programmatic testing. Programmatic testing again can be very easy — a simple moving average cross is a simple thing to program in nearly any programming language. However, the difficulties that can destroy the beginning programmatic trader are nearly endless. Also, ideas that I had tested extensively by hand sometimes were difficult to program. I have had so many experiences where I miscoded a critical concept even by a slight degree and this ended up giving drastically different results than my hand testing. Without the knowledge that it was the code that was incorrect, Trading might have falsely dismissed many trading ideas that were in fact valid. Additionally, at this level of programmatic trading it is very important to consider factors of minimizing inputs degrees of freedom and utilizing flexible inputs. An example of this would be to utilize a 3 ATR stop instead of a 60 pip stop so that as the prices and volatility of the market fluctuate your stop is not being taken out because of random noise. Other ways that you can improve the robustness of your strategy include utilizing realistic fills and commissions and ensuring that your limit orders would have actually been filled this is not as easy to test trading some software as it should be. Optimization is another useful tool to consider at mechanical point in your strategy testing career. This is a powerful but two edged sword. Walk forward testing is another useful tool that can help you achieve realistic results and see for yourself whether a strategy would have been successful on data that was not optimized similar to the future. Going further into programmatic trading, after having experienced many pitfalls, I feel that I ought to be able to test more than one idea at a time. In fact, ideally I would like to test many ideas, over multiple time frames and multiple markets. Right now this is the work trading I am involved in designing and I feel that strategies will help me analyze the markets with the speed and precision that will take my trading to the next level. This is the arena of the best strategy designers, where statistical data mining, market analysis, timeframe analysis, technical analysis, fundamental analysis, and money management are combined with realistic evolutionary testing into a single package. As you can see, advanced programmatic testing and trading is a complex arena. I myself am still learning and by no means consider myself an expert. The good news is that successful robust mechanical strategy creation and implementation can be done in as simple or as complex a manner as you choose. Your best ideas will come from strategies observation, but some of the best ideas are the simplest and most straightforward. I've recently posted a call for traders and programmers who would like to collaborate ; this could be one promising way of getting started! Posted by Brett Steenbarger, Ph. Newer Post Trading Post Home. About Me Brett Steenbarger, Ph. Author of The Psychology of Trading Wiley,Enhancing Trader Performance Wiley,The Daily Trading Coach Wiley,and Trading Psychology strategies. As a trading coach for portfolio managers and traders at financial organizations, I am also interested in performance enhancement among traders, drawing upon research from expert performers in various fields. I took a leave from blogging starting May, due to my role at a global macro hedge fund. Blogging resumed in February,along trading regular posting to Twitter and StockTwits steenbab. I teach brief therapy as Clinical Associate Professor at SUNY Upstate in Syracuse, with a particular emphasis of solution-focused "therapies for the mentally well". Co-editor of The Art and Science of Brief Psychotherapies American Psychiatric Press, I don't offer coaching for individual traders, but welcome questions and comments at steenbab at aol dot com. View my complete profile. Subscribe To Posts Atom. Twitter Trader follow me on Twitter. 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