This is a summary of links featured on Quantocracy on Wednesday, 03/08/2017. To see our most recent links, visit the Quant Mashup. Read on readers!
-
PSA: Your NCAA March Madness Rules are Garbage. Do This Instead. [Invest Resolve]On the heels of last years fun and successful March Madness Bracket Challenge (WHERE SKILL PREVAILS!), we are happy to invite any and all to 2017s version. Feel free to read the post for this years rules, but bear in mind this years pool is limited to 250 entrants, so dont wait: Register here. As with most investing topics, our thinking on March Madness bracket rules continues
-
Interactive brokers native python API [Investment Idiocy]Until quite recently interactive brokers didn't offer a python API for their automated trading software. Instead you had to put up with various 3rd party solutions, one of which swigibpy I use myself. Swigibpy wrapped around the C++ implementation. I wrote a series of posts on how to use it, starting here. Although swigiby has been very good to me its always better to use official solutions
-
What is Deep Learning? [Quant Start]Almost a year ago QuantStart discussed deep learning and introduced the Theano library via a logistic regression example. Given the recent results of the QuantStart 2017 Content Survey it was decided that an up to date beginner-friendly article was needed to introduce deep learning from first principles. These days it is almost impossible to work in any technology-heavy field without hearing about
-
66 DTE Iron Condor Results Summary [DTR Trading]This article reviews the backtest results of iron condors (IC) entered at 66 days to expiration (DTE). These tests covered 9 IC variations, with short strike deltas at four locations (8, 12, 16, 20), utilizing 12 exits. In all, there were 432 test runs (9 variations x 4 deltas x 12 exits). Each test run executed slightly less than 200 SPX IC trades between the January 2007 expiration and the
-
Machine Learning in Python for Finance: 2-Day Workshop in Warsaw, Poland [Quant at Risk]After wonderful and rewarding 2-day workshop devoted to Python for Algo-Trading on March 4-5, it is my pleasure to announce a new, upcoming, on demand 2-Day Workshop on Machine Learning in Python for Finance (May 20-21, 2017). Since Machine Learning is the latest hottest topic covering different fields we will understand its aspects in a wide range of possible applications. Click here to learn
-
Historic data from native IB python API [Investment Idiocy]This is the second in a series of posts on how to use the native python API for interactive brokers. This post is an update of the post I wrote here, which used the 3rd party API swigibpy. Okay so you have managed to run the time telling code in my last post. Now we will do something a bit more interesting, get some market prices. Arguably it is still not that interesting, and this stuff will
-
Firm-Specific Information and Momentum Investing [Alpha Architect]When it comes to momentum investing, everyone is always looking for a better way to implement a momentum-based stock selection strategy (the same goes for a value strategy). We highlight a few methods in our book, Quantitative Momentum, as well as on our blog. We recently came across a paper from 2006 that has an improvement on a baseline momentum investing strategy, titled Firm-specific