Explore the research behind our trading, plus some just-for-fun stuff....

Posted on Jun 03, 2020 by Ajet Luka

In the world of Big Data, there are lots of tools and technologies to choose from. Choosing the "right" one depends on the things that you are building and the problems you are trying to solve. Trading firms have skilled teams that monitor and deploy data pipelines for their organisation and the technical overhead that comes with that. Firms invest...

Posted on Jun 02, 2020 by Kris Longmore
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Holding data in a tidy format works wonders for one's productivity. Here we will explore the tidyr package, which is all about creating tidy data. In particular, let's develop an understanding of the tidyr::pivot_longer and tidyr::pivot_wider functions for switching between different formats of tidy data. In this video, you'll learn: What tidy data looks like Why it's a sensible approach...

Posted on Jun 01, 2020 by Kris Longmore
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Anyone that's been around the markets knows that the monthly release of the United States Department of Labor's Non-Farm Payrolls (NFP) data can have a tremendous impact, especially in the short term. NFP is a snapshot of the state of the employment situation in the US, representing the total number of paid workers, excluding farm employees and public servants. We...

Posted on May 29, 2020 by Robot James
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Here's a round-up of our new articles this week. They cover crash protection, sloppy, noisy regressions, and data-munging skills. Finding Options for Effective Crash Protection Large capital losses can be devastating to your trading account. A couple of weeks ago, we explained how you can use SPY put options to protect your portfolio against severe market downside. If you're prepared to...

Posted on May 28, 2020 by Kris Longmore
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When data is too big to fit into memory, one approach is to break it into smaller pieces, operate on each piece, and then join the results back together. Here's how to do that to calculate rolling mean pairwise correlations of a large stock universe. Background We've been using the problem of calculating mean rolling correlations of ETF constituents as...

Posted on May 27, 2020 by Robot James
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When you're working with large universes of stock data you'll come across a lot of challenges: Stocks pay dividends and other distributions that have to be accounted for. Stocks are subject to splits and other corporate actions which also have to be accounted for. New stocks are listed all the time - you won't have as much history for these...

Posted on May 26, 2020 by Robot James

One way we can quantify a stock's movement relative to the market index is by calculating its "beta" to the market. To calculate the beta of MSFT to SPY (for example) we: calculate daily MSFT returns and daily SPY returns align the returns with one another regress MSFT returns against SPY returns. This shows the procedure, graphically: library(tidyverse) library(ggpmisc) msftspyreturns...

Posted on May 25, 2020 by Ajet Luka

In today's article, we are going to take a look at rolling and expanding windows. By the end of the post,  you will be able to answer these questions: What is a rolling window? What is an expanding window? Why are they useful? What is a Rolling or Expanding window? Here is a normal window. We use normal windows because...