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Aaron Trading designs and rigorously tests its commodity futures trading models applying out-of-sample walk forward financial engineering principles utilizing back-adjusted continuous data. These procedures are applied to avoid the fatal dangers of curve fitting and over optimization.. Usually, these two reasons are the culprit for the failure of futures trading systems when applied to actual trading. Furthermore, we also utilize additional statistical measures in an attempt to design extremely stable and robust trading models.
Aaron Trading utilizes these techniques at the core foundation of its commodity futures trading system engineering, testing and implementation because:
If given enough optimization and curve fitting, an individual can implement a variety of different trading principles to a given financial market and make it “appear” profitable. However, applying this approach, will normally produce a trading system that trades the past well, but most likely will fail overwhelmingly in the future when applied to actual commodity markets.
The out-of-sample walk forward approach represents a more “real world” trading simulation of the way a system is applied in real-time. Additionally, the out-of-sample approach provides answers to the following vital questions.
- Will a commodity futures trading model continue its profitability after optimization?
- What impact will fluctuations in trend and volatility dictate on future performance?
- Is a trading system extrapolating random events or something that has statistical value?
Answering the above questions provides the following benefits:
- It identifies a system that has potentially been curve fitted or improperly optimized. A model that displays profitably over a large number of walk-forward tests is most likely to be successful within future applications because of its greater statistical validity.
- It verifies the forward-trading ability of the model, i.e. how versatile is the system to changes in trend and volatility.
- It provides a more vigorous measure of the rate of post-optimization profit and risk. Walk forward approaches to model construction generate a statistical profile of multiple in sample and out-of-sample optimizations periods. Additionally, because it is based on a considerably larger sample, as opposed to testing a single period of data, it exhibits significantly greater statistical validity. Furthermore, it also allows a precise comparison of measurements of profitability between rates of out-of-sample versus in-sample trading.
If you have any questions about our trading model construction methods please contact us toll-free at 1-866-455-3633.
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