Sergio & Michael,
Thank you for the feedback.
The main purpose of this first post was not so much analysis, but rather to illustrate the application of the EntityStore concept for financial instruments. While the Dataset is a great concept for smaller datasets, it can become unwieldy for larger, non-rectangular datasets that may include both fundamental and technical data series.
The steps ahead include the following:
- Creating a survivor-bias free equities EntityStore
- Demonstrating how the EntityStore can be used in portfolio construction - I am in the middle of that piece of analysis that I will probably publish as a paper.
- Incorporation of historical fundamental information into the equities EntityStore. This can be used for factor modelling, stock selection and portfolio construction.
At this point the equities EntityStore will contain historical fundamental, technical (returns, price & volume) and performance-related information (alpha, beta, drift, correlation etc) for around 1000 active stocks and a further 1,000-2,000 delisted securities.
Also included will be a library of routines for portfolio construction and strategy backtesting.
This is potentially a valuable research tool, structured within the very powerful Wolfam framework.
From there my plan is to develop
- similar EntityStores for commodities, equity options and currencies.
- an EntityStore for equity pairs, that will focus on metrics such as correlation and cointegration
