Stocks & Commodities V. 23:2 (32-35): Building A Workable Trading Model by Seth Weinstein
Product Description
Stocks & Commodities V. 23:2 (32-35): Building A Workable Trading Model by Seth Weinstein
Having trouble designing a successful trading system? Here’s how you can develop a robust, non–curve fitted system for trading.
Statistical models allow you to reduce, if not remove, emotion from the trading process. Knowing the model’s past performance gives you some comfort when you enter a trade, something a discretionary trader does not have. However, there are drawbacks to using quantitative models if they are not designed correctly. If a model is not properly designed and tested, it can be a dangerous tool. The key to using a quantitative model is to develop a robust, non–curve fitted system for trading.
DEVELOPING A ROBUST STRATEGY
When you create a quantitative strategy, you need to find a statistical pattern from historical data that will continue to work going forward. A robust model should perform in actual use in a similar manner as it did in historical tests. So what can you do to increase the odds that a model will be a longlasting one?
OUT-OF-SAMPLE TESTING
One precaution you can take to ensure that a model is robust is to use out-of-sample testing during the optimization process. Optimization is the process of testing for the most effective values for the inputs of the system being designed. Do not use the full range of historical price data to perform out-of-sample testing when optimizing. By leaving out a section or sections of historical data from the optimizing, you can see how the system would perform on data outside of the optimization. This process helps you avoid curve-fitting the system.
Having trouble designing a successful trading system? Here’s how you can develop a robust, non–curve fitted system for trading.
Statistical models allow you to reduce, if not remove, emotion from the trading process. Knowing the model’s past performance gives you some comfort when you enter a trade, something a discretionary trader does not have. However, there are drawbacks to using quantitative models if they are not designed correctly. If a model is not properly designed and tested, it can be a dangerous tool. The key to using a quantitative model is to develop a robust, non–curve fitted system for trading.
DEVELOPING A ROBUST STRATEGY
When you create a quantitative strategy, you need to find a statistical pattern from historical data that will continue to work going forward. A robust model should perform in actual use in a similar manner as it did in historical tests. So what can you do to increase the odds that a model will be a longlasting one?
OUT-OF-SAMPLE TESTING
One precaution you can take to ensure that a model is robust is to use out-of-sample testing during the optimization process. Optimization is the process of testing for the most effective values for the inputs of the system being designed. Do not use the full range of historical price data to perform out-of-sample testing when optimizing. By leaving out a section or sections of historical data from the optimizing, you can see how the system would perform on data outside of the optimization. This process helps you avoid curve-fitting the system.
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