Weekly Roundup 29 May – Crash Protection, Sloppy Regressions and Data Munging Skillz

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 …

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Find Cheap Options for Effective Crash Protection Using Crash Regressions

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 …

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Weekly Roundup 22 May – Doubling Down in Losing Trades Like a Drunken Hedge Fund Manager

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 …

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How To Get Historical S&P 500 Constituents Data For Free

spx constituents historical mean return

In this post, we are going to construct snapshots of historic S&P 500 index constituents, from freely available data on the internet. Why? Well, one of the biggest challenges in looking for opportunities amongst a broad universe of stocks is choosing what stock “universe” to look at. One approach to dealing with this is to …

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How to Hedge a Portfolio with Put Options

There are 2 good reasons to buy put options: because you think they are cheap because you want downside protection. In the latter case, you are looking to use the skewed payoff profile of the put option to protect a portfolio against large downside moves without capping your upside too much. The first requires a …

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Efficiently Simulating Geometric Brownian Motion in R

For simulating stock prices, Geometric Brownian Motion (GBM) is the de-facto go-to model. It has some nice properties which are generally consistent with stock prices, such as being log-normally distributed (and hence bounded to the downside by zero), and that expected returns don’t depend on the magnitude of price. Of course, GBM is just a …

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