# profiling

Posted on May 29, 2020 by
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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 take on a little more sloppiness, there are often cheaper approaches available... https://robotwealth.com/finding-effective-crash-protection-using-downside-regressions/ Quant Skills Data manipulation skills are crucial to efficient quant trading. In the following posts, Ajet, Kris and I explain some of the skills you need to work with modern financial datasets. It's important not to use data from the future to analyse the past. Rolling and expanding windows are essential tools to help "walk your data forward" to avoid these issues. https://robotwealth.com/rolling-and-expanding-windows-for-dummies/ When you're working with large universes of stock data then you'll come across a lot of challenges. This article explains a trick to help deal with missing stock data. https://robotwealth.com/how-to-fill-gaps-in-large-stock-data-universes-using-tidyr-and-dplyr/ The kind of stuff that makes money tends to involve looking for edge in...

Posted on May 22, 2020 by
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Here's a round-up of our new articles this week. They cover options trading, digital signal processing, data munging and Kris's luxurious moustache... Trading Insanity! Every new trader tries out a few insane trading ideas! In a new series on the blog, Kris explores three insane trading strategies that tempted him back when he didn't know any better. First, he looks at the Martingale betting scheme. Is doubling your bet size after a losing trade really a good idea? https://robotwealth.com/get-rich-quick-trading-strategies-and-why-they-dont-work/     Non-Stupid Option Trading Most approaches to options trading are stupid. Here is a non-stupid approach. Options trading is just like anything else. You've got to buy the cheap stuff and sell the expensive stuff. https://robotwealth.com/how-to-find-an-edge-trading-equity-options/ Digital Signal Processing in Quant Trading In this monster post, Kris explores techniques from the field of digital signal processing, and whether they can be useful to us as systematic traders. https://robotwealth.com/using-digital-signal-processing-in-quantitative-trading-strategies/ How to Get Historical S&P 500 Constituents for Free One of the biggest advantages you can have in equity trading is going broad... But how do you pick a broad bias-free universe for equity strategy...

Posted on May 22, 2020 by
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Recently, we wrote about calculating mean rolling pairwise correlations between the constituent stocks of an ETF. The tidyverse tools dplyr and slider solve this somewhat painful data wrangling operation about as elegantly and intuitively as possible. Why did you want to do that? We're building a statistical arbitrage strategy that relies on indexation-driven trading in the constituents. We wrote about an early foray into this trade - we're now taking the concepts a bit further. But what about the problem of scaling it up? When we performed this operation on the constituents of the XLF ETF, our largest intermediate dataframe consisted of around 3-million rows, easily within the capabilities of modern laptops. XLF currently holds 68 constituent stocks. So for any day, we have  $\frac{68*67}{2} = 2,278$ correlations to estimate (67 because we don't want the diagonal of the correlation matrix, take half as we only need its upper or lower triangle). We calculated five years of rolling correlations, so we had  $5*250*2,278 = 2,847,500$ correlations in total. Piece of cake. The problem gets a lot...