jaredbroad

Hello Everyone,

I just wanted to leave a note that we deployed an integration to the Schwab API. It lets you do quantitative research in python, harnessing lots of fun data, and then deploy the algorithm to live trading. You can see more information on it here: https://www.quantconnect.com/announcements/18559/introducing-the-charles-schwab-integration-on-quantconnect/p1

Thanks! Happy to answer any questions,

Jared
(Founder @ QC).

What are your thoughts on the best approach for leading quantitative research? Picking and testing a hypothesis to avoid overfitting, or throwing it all to the wind and actively data mining to try and find patterns?

At QuantConnect, we've been advocates for intentionally picking and testing a hypothesis to limit the hyper-parameter tuning of backtests. We incorporated this approach into the Alpha Stream recommendations to design solid alphas for licensing at institutions. Our theory was that this philosophy-driven approach gives you tangible explanations for parameter values.

The recent book The Man Who Solved the Market by Gregory Zuckerman focuses on techniques used by Renaissance Techn