Casing the Nasdaq for profits

摘要

Discussing a nice trading theory is one thing. Executing that theory in the markets is quite another. In this third of three parts, we'll attempt to make this leap in the context of case-based reasoning. First, we introduced the concept. Second, we demonstrated the dry but vital step of pre-processing market data for use in cased-based forecasting applications. In this article, we will finish our Excel-based application for case-based analysis and use it to show how this methodology can be used in market forecasting and trading applications. Our model uses brute force Euclidian distance measures to find similar cases and uses the average of the similar cases to create a forecast for the case in question. This process is very slow and will not work effectively in a real world application because it takes about two to three minutes per year, per market to iterate for a single bar on a high-end PC. It is also too slow to use more advanced case-based methodologies such as adjusting the weighting on each feature. That doesn't mean we have to abandon this approach. We will show how our simple tool works using real market data. Finally, we will discuss several advanced topics that will be necessary to incorporate to take our technology to the next level.