This is a summary of links featured on Quantocracy on Wednesday, 02/03/2016. To see our most recent links, visit the Quant Mashup. Read on readers!
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Fama French Multifactor Model in Python [Largecap Trader]Factor modelling is everywhere these days. I wrote about smart beta here. It is good to quantify performance drivers but the usual caveats apply to quantitative studies utilizing backward looking data, past performance does not guarantee future results. I wanted to share a little exercise I did in Python comparing a fund, stock, or anything with a ticker available on Yahoo Finance with the
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Dream team: Combining classifiers [Quant Dare]Can a set of weak systems turn into a single strong system? When you are in front of a complex classification problem, often the case with financial markets, different approaches may appear while searching for a solution. These systems can estimate the classification and sometimes none of them is better than the rest. In this case, a reasonable choice is to keep them all and then create a final
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Trend Following: Good Start to 2016 [Wisdom Trading]Similarly to last year, trend following starts the year on strong footing. January returned over 5% for our trend following index after flirting with the double-digit territory to establish new all-time highs. Below is the full State of Trend Following report as of last month. Performance is hypothetical. Chart for January:
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SPX Straddle – Normalized Return Charts [DTR Trading]The last article on RUT straddles (here) was very popular, so I thought I'd write a similar post on SPX straddles. Recall that from September, 2015 through November, 2015 I reviewed the backtest results form 28,840 short options straddles on the S&P 500 Index (SPX). You can read the summary articles from that SPX series here and here, and the introductory article for the straddle series