Using OLAP Hypercubes to Analyze Trading Reports and Optimization Results
Summary
The article explains how online analytical processing can organize trading and optimization data as a multidimensional cube. Once records have been mapped into cube cells and aggregate values calculated, an analyst can inspect slices such as profit by symbol, weekday, trade direction, or Expert Advisor identifier, and combine dimensions for more detailed comparisons. “Online” refers to quickly exploring precomputed results, not internet connectivity.
The proposed object-oriented design separates records, data adapters, selectors, filters, cube structure, aggregators, displays, and an analyst controller. Selectors map record fields to valid indexes on cube axes; filters restrict which records contribute, while aggregators calculate summaries such as sums or averages. The article outlines reusable foundations and examples of fields for trade histories and optimization reports, with adapters envisioned for sources such as account history, CSV, and HTML. It describes an extensible analysis framework, not empirical findings about strategy performance; users still need to choose meaningful dimensions, aggregates, and filters for each application.
Key ideas
- OLAP represents trading data across multiple dimensions so analysts can inspect precomputed slices quickly.
- Trading reports can be grouped by attributes such as symbol, weekday, direction, and robot identifier.
- Selectors map record fields onto cube axes, while filters limit which records are included.
- Aggregators compute cell statistics such as sums or averages.
- Separate adapters and record definitions allow the framework to work with trade history, optimization output, and other data sources.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.