Monday, July 12, 2010

Recorded Future and Quantitative Trading

We have a new blog post on the conceptual ways that Recorded Future content can be integrated with Quant trading strategies. Take a look and let us know what you think: Recorded Future and Quantitative Trading

Thursday, July 1, 2010

Predicting the Future with Recorded Future

We often get asked about the quality of our predictions and our support for backtesting and I wanted to take a minute and put out something on this blog about that.

I think it helps to discuss two kinds of predictions you can do with Recorded Future. One thing that we do is aggregate and structure what others have said about the future and support the analysis of that information. Those aren't "our" predictions per se but rather our support for accessing and using the predictions of others. The second type are predictions an analyst specifically makes by using our content (perhaps in conjunction with other data). We have a couple of blog posts that highlight this approach that you can look over at
http://www.predictivesignals.com/2010/06/does-momentum-predict-higher-trading.html
http://www.predictivesignals.com/2010/06/news-sentiment-analysis.html
In these posts, we look at the relationship between content in our database and future events like volume or asset price. These involve postulating and testing specific predictive relationships.

So to consider backtesting, we record two timestamps for everything we capture, one is when the information was published and one is when the event is expected to occur (in the past, at publication time, or in the future)

Assessing prediction quality in either of these cases is currently best done using our web services analytic framework (JSON web service + Python/R etc)

For the first type of prediction, it is fairly straightforward to find content where event time is after publication time and to assess the reliability of those predictions. This is really assessing the reliability of the individual predictors we have captured in our system. In the second case where an analyst is exploring predictive relationships based on our content, it is easy to understand what was known when and to assess the quality of the predictive hypothesis. Again, the blog posts above are examples of those types of analysis.

Over time we will be adding additional support for backtesting to our product, both in the online and web service analytic frameworks.