Learning or experimenting with Tidytext has been on my radar for at least a few years. Only recently did i have a need to pick it up. As with most learnings, they lead you down a path of more knowledge (read: rabbit holes) than you foresaw. This post is a hat-tip to the resources i used, knitting

My experience in financial markets to date has mostly been related to trading and investment banking. From executing index arbitrage and various other strategies, to product managing a team building execution algorithms for automating strategies which minimize market impact and make relative and

In their paper “The Probability of Backtest Overfitting” [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2326253] Bailey et al. introduce a method for detecting overfitting. They refer to the method as CSCV or Combinatorially Symmetric Cross Validation. Bailey et al. proceed to show that

I was fortunate enough to talk about my latest open source work with Brian Peterson at the R/Finance conference in Chicago just less than 1 month ago. It was my first time to the conference, and I will be back again for sure. The topics and their presentations are available on the website. With this

‘Once you have a system, the biggest obstacle is trusting it.’ Tom Wills ‘What is the last thing you do before you climb on a ladder? You shake it. And that is Monte Carlo simulation.’ Sam Savage, Stanford University Introduction In my early days of looking at trading strategies, getting to

In this post i take an R-view of “Backtesting – Harvey & Liu (2015).” The authors propose an alternative to the commonly practiced 50% discount that is applied to reported Sharpe ratios when evaluating backtests of trading strategies. The reason for the discount is due to the inevitable

I have had the pleasure of getting to know and work with Brian Peterson of late building out the blotter::mcsim function in the blotter package. I will be writing about this function soon and where it is headed, but in this post i wanted to share a presentation Brian gave the CapeR User Group last

A few weeks back i wrote a post including the source code for a Monte Carlo simulation function in R. The idea was to randomly sample daily returns produced by a backtest and build a confidence interval distribution of the middle 50% and 90% of returns. Since then Brian Peterson got in touch with me

Comparing a strategy’s performance to a Buy-and-Hold strategy is quick and easy. But there is a good chance it might not be appropriate and adds as much value as the time it took to research…zero. So when i see an article comparing a particular strategy to its Buy-and-Hold equivalent dating back

In this post on bettersystemtrader.com, Andrew Swanscott interviews Kevin Davey from KJ Trading Systems who discusses why looking at your back-test historical equity curve alone might not give you a true sense of a strategy’s risk profile. Kevin Davey also writes on the topic here for