twiecki

8 ms·

31.
Visualizing MCMC with PyMC and Matplotlib(twiecki.github.io)
1 points by twiecki 13y ago|0 comments
32.
Using Wikipedia page view counts to predict market movements.(quantopian.com)
6 points by twiecki 13y ago|4 comments
33.
Google Search Terms predict market movements(quantopian.com)
58 points by twiecki 13y ago|15 comments
34.
Parameter Optimization with Zipline, PiCloud, StarCluster, and IPython Parallel(blog.quantopian.com)
21 points by twiecki 14y ago|1 comments
35.
by twiecki 14y ago
Yes, yahoo finance prices can be quite messy and sometimes even wrong for the reasons you mention. I wanted the code to be portable so that's why I used them. We have the same strategy on Quantopian though ( https://www.quantopian.com/posts
36.
by twiecki 14y ago
Also, this is not your standard dual moving average cross-over strategy. Rather, it rebalances its portfolio (optimally) under the assumption that the stocks will revert to their moving average. The eps parameter, which I explored in this t
37.
by twiecki 14y ago
Certainly that's always a risk. Zipline does however support simulating transaction-costs and slippage so those will be accounted for (as best as possible). As for market-paradigm shifts, I totally agree. One way to deal with that is to con
38.
by twiecki 14y ago
That's currently not implemented, but definitely something we would like include. What I'd really like to see is a client for a bitcoin exchange. See our contribution request page for more details: https://github.com/quantopian/zipline/wik
39.
by twiecki 14y ago
Yeah, the code is cut-off unfortunately. It's the new IPython nbconverter that still has some kinks. The idea of the algo is expressed on the next slide. You can, however, view the full IPython NB here: http://nbviewer.ipython.org/urls/raw
40.
Show HN: Optimized trading algorithms using IPython parallel and ec2(twiecki.github.com)
102 points by twiecki 14y ago|28 comments