The key component of my forex trading system is the prediction engine. So far I have been trying to optimize the prediction engine in the overall context of simulated trading on the historical data where besides the parameter responsible for prediction, at least three other parameters were being optimized. With the large number of parameters, the CPU demands of optimization become prohibitive: as the dimensionalty of the parameter space grows, the number of parameter combination grows with it. As the pseudo-random time series are at the very heart of the problem, the randomness of changes in performance with respect to every parameter clouds the analyst's vision of any parameter in the course of the optimization. The adverse effect of that is possibly even more important than the combinatorial growth of the volume of the parameter space. It is therefore very helpful to factor the problem out into independent pieces which can then be optimized separately. The success depends, among other things, on the figures of merit used and on the degree of true independence between such pieces. Predictions for every market under analysis are obtained at every decision-making step (in this case, a day). As always, the system has no access to the future of the time series and only learns from the past. Every step during the simulation is therefore a direct test of the applicability of the past learning to the present context, just as it will be in real life. In real life however, the system chosen for operation will bear in itself the bias associated with its selection. In the Monte Carlo tests, we don't select and deal with an entire array of systems. The statements made for such an a priori array are free of selection bias. In order to conduct an unbiased test of the prediction quality and determine the best prediction parameter, I use the following procedure. At every prediction step, I record the prediction and one step later, when the future becomes reality as predicted or otherwise, I take the product of the real and predicted logarithmic returns for the day. The average of the product is the quantity plotted along the vertical axis in Fig.1 for the entire range of the prediction parameters. Fig.1. Covariance of predicted and actual day-scale logarithmic returns as a function of the forecasting parameter nicknamed Fred. Back-testing simulations with no access to the future.
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Why I think my forex forecasting engine works
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October 20 2009, 2:09pm | Comments »
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How I know my forex forecasting engine works
The key component of my forex trading system is the prediction engine. So far I have been trying to optimize the prediction engine in the overall context of simulated trading on the historical data where besides the parameter responsible for prediction, at least three other parameters were being optimized. With the large number of parameters, the CPU demands of optimization become prohibitive: as the dimensionalty of the parameter space grows, the number of parameter combination grows with it. As the pseudo-random time series are at the very heart of the problem, the randomness of changes in performance with respect to every parameter clouds the analyst's vision of any parameter in the course of the optimization. The adverse effect of that is possibly even more important than the combinatorial growth of the volume of the parameter space. It is therefore very helpful to factor the problem out into independent pieces which can then be optimized separately. The success depends, among other things, on the figures of merit used and on the degree of true independence between such pieces. Predictions for every market under analysis are obtained at every decision-making step (in this case, a day). As always, the system has no access to the future of the time series and only learns from the past. Every step during the simulation is therefore a direct test of the applicability of the past learning to the present context, just as it will be in real life. In real life however, the system chosen for operation will bear in itself the bias associated with its selection. In the Monte Carlo tests, we don't select and deal with an entire array of systems. The statements made for such an a priori array are free of selection bias. In order to conduct an unbiased test of the prediction quality and determine the best prediction parameter, I use the following procedure. At every prediction step, I record the prediction and one step later, when the future becomes reality as predicted or otherwise, I take the product of the real and predicted logarithmic returns for the day. The average of the product is the quantity plotted along the vertical axis in Fig.1 for the entire range of the prediction parameters. Fig.1. Covariance of predicted and actual day-scale logarithmic returns as a function of the forecasting parameter nicknamed Fred. Back-testing simulations with no access to the future.
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October 20 2009, 2:09pm | Comments »
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CAD and oil hour-scale correlation: it's safer to rely on CAD
In the recent forex/CFD data, USD/CAD is negatively correlated with light oil (WTI) CFD. This is the same as saying that CAD, one of the commodity currencies, is positively correlated with oil. This is old news. In this article I take a deeper look at the issue and analyze the shape of the correlation peak. Analyzed on the hour time scale, the correlation peak is broad and somewhat asymmetric, indicating that it is much safer to rely on the guidance of USD/CAD in predicting the oil price, rather than other way round. The necessary caveat is that this is a time-integrated picture, covering a period from late August 2008.
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October 14 2009, 8:42pm | Comments »
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CAD and oil hour-scale correlation: it's safer to rely on CAD
In the recent forex/CFD data, USD/CAD is negatively correlated with light oil (WTI) CFD. This is the same as saying that CAD, one of the commodity currencies, is positively correlated with oil. This is old news. In this article I take a deeper look at the issue and analyze the shape of the correlation peak. Analyzed on the hour time scale, the correlation peak is broad and somewhat asymmetric, indicating that it is much safer to rely on the guidance of USD/CAD in predicting the oil price, rather than other way round. The necessary caveat is that this is a time-integrated picture, covering a period from late August 2008.
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October 14 2009, 8:42pm | Comments »
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Volatility-neutral trading system
After inspecting the simulated track of the best selected algorithmic traders (see EUR/USD, USD/JPY, GBP/USD, USD/CHF, USD/CAD, AUD/USD, it becomes clear that a volatility-neutral approach is needed. The optimized robots trade only during the peak of financial panic so there is a risk that if such a system is launched and the volatility returns to normal, no trades will be placed.
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October 12 2009, 3:47pm | Comments »
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Volatility-neutral trading system
After inspecting the simulated track of the best selected algorithmic traders (see EUR/USD, USD/JPY, GBP/USD, USD/CHF, USD/CAD, AUD/USD, it becomes clear that a volatility-neutral approach is needed. The optimized robots trade only during the peak of financial panic so there is a risk that if such a system is launched and the volatility returns to normal, no trades will be placed.
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October 12 2009, 3:47pm | Comments »
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Graphical analysis of trading system's simulated track record. Step Two algorithm, AUD/USD.
This AUD/USD back-testing analysis concludes the series which began with EUR/USD. Simulated track records of six best Step Two algorithmic traders are studied graphically. For a more numbers-oriented approach to performance, see the article explaining the trading system optimization process which led to the selection of these six robots. This is the sixth, final report in the series and by now, the main area needing improvement is already clear: just as the system undertrades the less volatile currencies, it overtrades Aussie.
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October 5 2009, 1:55pm | Comments »
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Graphical analysis of trading system's simulated track record. Step Two algorithm, AUD/USD.
This AUD/USD back-testing analysis concludes the series which began with EUR/USD. Simulated track records of six best Step Two algorithmic traders are studied graphically. For a more numbers-oriented approach to performance, see the article explaining the trading system optimization process which led to the selection of these six robots. This is the sixth, final report in the series and by now, the main area needing improvement is already clear: just as the system undertrades the less volatile currencies, it overtrades Aussie.
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October 5 2009, 1:55pm | Comments »
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Graphical analysis of trading system's simulated track record. Step Two algorithm, USD/CAD.
This USD/CAD visual back-testing analysis continues the series which began with EUR/USD. Simulated track records of six best Step Two algorithmic traders are studied graphically. For a more numbers-oriented approach to performance, see the article explaining the trading system optimization process which led to the selection of these six robots.
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October 1 2009, 3:38pm | Comments »
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Graphical analysis of trading system's simulated track record. Step Two algorithm, USD/CAD.
This USD/CAD visual back-testing analysis continues the series which began with EUR/USD. Simulated track records of six best Step Two algorithmic traders are studied graphically. For a more numbers-oriented approach to performance, see the article explaining the trading system optimization process which led to the selection of these six robots.
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October 1 2009, 3:38pm | Comments »
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Graphical analysis of trading system's simulated track record. Step Two algorithm, USD/CHF.
This USD/CHF back-testing analysis continues the series which began with EUR/USD. Simulated track records of six best Step Two algorithmic traders are studied graphically. For a more numbers-oriented approach to performance, see the article explaining the trading system optimization process which led to the selection of these six robots.
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September 30 2009, 1:32pm | Comments »
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Graphical analysis of trading system's simulated track record. Step Two algorithm, USD/CHF.
This USD/CHF back-testing analysis continues the series which began with EUR/USD. Simulated track records of six best Step Two algorithmic traders are studied graphically. For a more numbers-oriented approach to performance, see the article explaining the trading system optimization process which led to the selection of these six robots.
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September 30 2009, 1:32pm | Comments »
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Graphical analysis of trading system's simulated track record. Step Two algorithm, GBP/USD.
This GBP/USD back-testing analysis continues the series which began with EUR/USD. Simulated track records of six best Step Two algorithmic traders are studied graphically. For a more numbers-oriented approach to performance, see the article explaining the trading system optimization process which led to the selection of these six robots.
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September 29 2009, 11:21am | Comments »
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Graphical analysis of trading system's simulated track record. Step Two algorithm, GBP/USD.
This GBP/USD back-testing analysis continues the series which began with EUR/USD. Simulated track records of six best Step Two algorithmic traders are studied graphically. For a more numbers-oriented approach to performance, see the article explaining the trading system optimization process which led to the selection of these six robots.
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September 29 2009, 11:21am | Comments »
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Graphical analysis of trading system's simulated track record. Step Two algorithm, USD/JPY.
This USD/JPY back-testing analysis continues the series which began with EUR/USD. Simulated track records of six best Step Two algorithmic traders are studied graphically. For a more numbers-oriented approach to performance, see the article explaining the trading system optimization process which led to the selection of these six robots.
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September 28 2009, 11:00am | Comments »
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Graphical analysis of trading system's simulated track record. Step Two algorithm, USD/JPY.
This USD/JPY back-testing analysis continues the series which began with EUR/USD. Simulated track records of six best Step Two algorithmic traders are studied graphically. For a more numbers-oriented approach to performance, see the article explaining the trading system optimization process which led to the selection of these six robots.
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September 28 2009, 11:00am | Comments »
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Graphical analysis of simulated track record of Step Two robots with EUR/USD.
Simulated track records of six best Step Two algorithmic traders are studied graphically. For a more numbers-oriented approach to performance, see the article explaining the trading system oprimization process whereby the six robots were selected. This post focuses on performance in the EUR/USD market and will be followed by similar posts dealing with other markets.
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September 25 2009, 12:25pm | Comments »
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Graphical analysis of simulated track record of Step Two robots with EUR/USD.
Simulated track records of six best Step Two algorithmic traders are studied graphically. For a more numbers-oriented approach to performance, see the article explaining the trading system optimization process whereby the six robots were selected. This post focuses on performance in the EUR/USD market and will be followed by similar posts dealing with other markets.
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September 25 2009, 12:25pm | Comments »
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Six best Step Two robots are selected for closer inspection
Having identified the range of parameters where a Step Two trading system has the greatest advantage over Step One (in the course of a Step One vs Step Two performance comparison), I proceed by narrowing down the range of adjustable system parameters even further. I select six "best" algorithmic traders for a more thorough examination, by forming a set of tighter system parameter selections, complemented with cuts on the figures of merit such as skewness of return distribution, the annualized return and the fraction of months with a negative return.
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September 23 2009, 12:05pm | Comments »
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Six best Step Two robots are selected for closer inspection
Having identified the range of parameters where a Step Two trading system has the greatest advantage over Step One (in the course of a Step One vs Step Two performance comparison), I proceed by narrowing down the range of adjustable system parameters even further. I select six "best" algorithmic traders for a more thorough examination, by forming a set of tighter system parameter selections, complemented with cuts on the figures of merit such as skewness of return distribution, the annualized return and the fraction of months with a negative return.
- Tags:
- frontpage
September 23 2009, 12:05pm | Comments »