This is a summary of links featured on Quantocracy on Thursday, 06/22/2017. To see our most recent links, visit the Quant Mashup. Read on readers!
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Rough Path Theory and Signatures Applied To Quantitative Finance – Part 4 [Quant Start]This is the fourth in a new advanced series of posts written by Imanol Prez, a PhD researcher in Mathematics at Oxford University and an expert guest contributor to QuantStart. In this post Imanol applies the Theory of Rough Paths to the task of predicting which country a company belongs to based on the evolution of its stock price and traded volume. – Mike. As we saw in the last article, the
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Taming Mean Reversion s Left Tail Don t use Stop Losses! [Sutherland Research]Mean reversion strategies rely on the premise that extremes in price eventually revert to the mean price over time. They are effective during established markets bull, bear or sideways but unfortunately do not perform well during market regime changes or tail events. Tail events are outcomes that have a low probability of occurring, but may inflict significant damage to a portfolio when
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Some more trading rules [Investment Idiocy]It is a common misconception that the most important thing to have when you're trading, or investing, systematically is good trading rules. In fact it is much, much, much more important to have a good position management framework (as discussed in my first book) and to trade a diversified set of instruments. Combine those with a couple of simple trading rules, and you'll have a pretty
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Factor Investing: Evidence Based Insights [Alpha Architect]I will be talking on the Factor Investing panel at the upcoming Evidence-Based Investing Conference in Dana Point, CA next Sunday Tuesday. I am excited for the opportunity to chat, and figured I would highlight a few thoughts we have on the topic going into the event. First, what is evidence based investing? What does that even mean? If we read a index factsheet, review a long-term live
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Iron Condor Results Summary – Part 2 – Loss Levels [DTR Trading]In the last article we looked at the backtest results from 600,912 iron condor trades entered between January 2007 and September 2016. The focus in that article was on win rate and normalized P&L per day for each of the 3024 variations tested. Recall that we looked at combinations of: Trade entry dates based on days to expiration (DTE) Iron condor wing widths Iron condor short strike position
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Matrix Algebra – Linear Algebra for Deep Learning (Part 2) [Quant Start]Last week I posted an article, which formed the first part in a series on Linear Algebra For Deep Learning. The response to the article was extremely positive, both in terms of feedback, article views and also more broadly on social media. Many of you commented that there was "an appetite" for introductory mathematical content and this only confirms the results of the QuantStart 2017