The Importance of Out-of-Sample Tests and Lags in Forecasts and Trading Algorithms

I recently had the opportunity to listen to some great minds in the area of high-frequency data and trading. While I won’t go into the details about what has been said, I wanted to illustrate the importance of proper out-of-sample testing and proper variable lags in potential trade algorithms or arbitrage models that has been brought up. This topic can also be generalized to a certain degree to all forecasts.

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Speeding “Bayesian Power Analysis t-test” up with Snowfall

This is a direct (though minor) answer to Daniel’s blogpost Power Analysis for default Bayesian t-tests, which I found very interesting, as I have been trying to get my head around Bayesian statistics for quite a while now. However, one thing that bugs me, is the time needed for the simulation. On my machine it took around 22 minutes. Depending on the task, 22 minutes for a signle test can be way too long (especially if the tests are done in a business environment where many tests are needed – yesterday) and a simple t-test might be more appealing only because it takes a shorter computing time. Here is my solution to speed-up the code using snowfall‘s load-balancing parallel structures to reduce the time to 8.5 minutes.

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Getting started with PostgreSQL in R

When dealing with large datasets that potentially exceed the memory of your machine it is nice to have another possibility such as your own server with an SQL/PostgreSQL database on it, where you can query the data in smaller digestible chunks. For example, recently I was facing a financial dataset of 5 GB. Although 5 GB fit into my RAM the data uses a lot of resources. One solution is to use an SQL-based database, where I can query data in smaller chunks, leaving resources for the computation.

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