PyFolio Performance Reporting in Python

Pyfolio is a Python library that takes a return series of an asset, hedge fund, trading strategy, anything with daily returns and automatically generates some really cool statistics and charts. There is a LOT of cool stuff to explore in the library, have fun! Performance statistics Backtest annual_return 0.98 annual_volatility 0.62 sharpe_ratio 1.41 calmar_ratio 1.71 stability_of_timeseries 0.91 max_drawdown -0.57 omega_ratio 1.27 sortino_ratio 2.01 skew -0.51 kurtosis 2.12 tail_ratio 1.00 common_sense_ratio 1.99 information_ratio 0.09 alpha 0.28…

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DIY Quants

“Too much money chasing too few ideas” has been the death knell for investment fad after fad. This will never cease to be so. – Howard Marks With the recent announcement at Point72 of a $250MM investment in quantitative trading platform Quantopian and a recent FT article, there has been a surge in interest in Do-It-Yourself quant strategies. Here’s one from WSJ.   There are some significant challenges for these startups in my opinion*: Has…


Dashboard for Historical SPX Butterfly Prices

I built a dashboard to plot historical butterfly prices from an options database that I have locally. I’m using Python and Ipywidgets in a Jupyter Notebook to create a user interface to select the expiration and strikes of the fly and it will plot the closing fly prices versus the SPX. I’m sharing the code as it can serve as a good template for others looking to visualize data or create GUI Dashboards with Python.  The…


Automatic Support/Resistance using ML

I think an interesting application of ML could be generating the ‘features’ for inclusion in a trading algorithm, converting non-numerical data into numerical. For example, converting market sentiment or satellite imagery to count cars in a retailer’s parking lot. More examples here. I had written previously about Machine Learning for trading strategy design here. Thanks to code from @johnromero via @quantocracy I wrote a quick script to draw support and resistance lines automatically using machine learning on…

HF Hard Time

Hard Times at Hedge Fund High

Edit: I wrote a post concerning the future of hedge funds sometime ago and it languished in the Drafts Folder. In the meantime, Ben Carlson of A Wealth of Common Sense posted this which is a far better and more concise explanation. I guess you have to be quick these days! So I’ll only summarize some additional thoughts here. I came across this excellent link on twitter of a scanned Forbes article from 1970 entitled “Hard…


Equity Supply/Demand Indicator

I read a very interesting post from AlephBlog  which led me to another blog called Philosophical Economics. It’s a long and in depth article I had to read a few times to understand but the basic gist of it is that when investors are under allocated to equities, future returns are better than when they are over allocated. It utilizes the Fed Flow of Funds report to develop a ratio of the value of equities…

ML Knearest2

Machine Learning & SciKit Learn

I made the point to someone the other day that technology and coding is getting easier and easier to accomplish. I don’t think I would have been able to perform ‘machine learning’ five years ago but with the resources available today (Python, SciKit Learn, and pages upon pages of StackOverflow) even someone like me can fit a model and build ML algorithms. Machine Learning is also ridiculously “easy”. It’s literally 4 lines of code. It…


More about RenTech

I’ve written a little bit about RenTech in the past; Here is an article with an interview with Jim Simmons that essentially explains his biz model. And here is one on a ‘tax arbitrage’ trade utilizing basket options to avoid gains and I assume market impact. Below is an older article I just stumbled upon from 2014 with some additional insight revealed during their basket options court hearing. Of note: In one year they executed 26-39MM…


Factor Premium/Discount & Market Timing

Smart beta is the new new thing, I even wrote about it here. There has been some interesting writing on it recently that shares some concerns with the popularity of this approach which I will attempt to summarize: Factor investing can work but it can become relatively ‘cheap’ and ‘expensive’ due to performance chasing and affect future returns. Here is a chart of relative returns of the Value Factor: Great visual explanation of the time…


Speculation in a Path Dependent World

I’m happy to publish my first paper on SSRN entitled, “Speculation in a Path Dependent World”  I found many otherwise talented managers entering a multi-manager platform (assigned capital with strict risk limitations) having a difficult time transitioning. The paper is my simple attempt to suggest an alternative framework for new or potential managers dealing with drawdown risk. Any and all feedback, comments, advice, diatribes are greatly appreciated!