Walk-Forward Strategy Performance

The previous post introduced forecasting using multiple series and also suggested another form of improvement – namely filtering out low probability forecasts. Can we improve the forecasts by any of these approaches, and if yes, by how much?
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Better Model Selection for Evolving Models

For quite some time now I have been using R’s caret package to choose the model for forecasting time series data. The approach is satisfactory as long as the model is not an evolving model (i.e. is not re-trained), or if it evolves rarely. If the model is re-trained often – the approach has significant computational overhead. Interestingly enough, an alternative, more efficient approach allows also for more flexibility in the area of model selection.

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Forecasting Opportunities

The previous post in this series, showed a way to identify trading opportunities. The approach I implemented used time series daily data to identify good entry points in terms of risk-reward. The natural next step is to try to make use of these opportunities using machine learning.

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Thoughts on Trading

Is trading worth doing for living? Once in a while I am asked this question and, to me, going through this mental exercise makes sense. A lot of sense. So let me share my thoughts.

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