The Equal Risk Contribution (ERC) portfolio seeks to maximally diversify portfolio risk by equalising the risk contribution of each component. The intuition is as follows: Imagine we have a 3-asset portfolio Assets 1 and 2 are perfectly correlated (correlation of 1.0) Asset 3 is uncorrelated with the other two (correlation of 0.0) Let’s say we …
Using Exponentially Weighted Moving Averages to navigate trade-offs in systematic trading A big part of the job of the indie trader is data analysis. We’re always looking in the past data to validate (or more often, invalidate) a hypothesis about what might predict future returns. And one could argue that recent data is more useful …
I’ve previously discussed the Volatility Risk Premium (VRP) and how it differs from the Equity Risk Premium (ERP). Probably the most interesting difference, from the perspective of the trader, is that the VRP may be somewhat amenable to timing – more than the ERP at any rate. In this article, I’ll use some of the …
Lately, I’ve been thinking a lot about the Volatility Risk Premium (VRP). The VRP makes much more sense (to me, at least) when I have the Equity Risk Premium (ERP) for context and comparison. So, in this article, I want to discuss the ERP and the VRP, their similarities and differences, and how I seek …
I want to discuss a couple of simple trade-off considerations around quant trading signals that may not be obvious. Here’s the price of some asset: Our main job is to predict how it’s likely to move. To do this, you use information about it that you think is predictive. And at any point in time: …
In 2021, James, I, and a small team decided to set up a crypto trading venture. We faced several problems, but knowing almost nothing about crypto was the most significant. We sensed that the fractured, developing nature of the crypto market would likely be a good place to seek out inefficiencies, but beyond that, we …
Towards the end of last year, we ran a couple of free Zoom webinars on: Here are the recordings: Basics of Edge Extraction Data analysis for Traders The colab research notebook for the second session can be found here. (To make sense of it you’ll want to watch the video.)
In Australia, if you’re serious about getting the job done effectively and efficiently, you might say: “I’m not here to f*** spiders.” Many traders act like they are, indeed, here to f*** spiders. If you’re making soup, you first need a good stock. Stock isn’t exciting. Everyone has stock. Garnish is exciting, but you can’t …
Imagine you’re a relatively small, independent trader trying to turn trading from a hobby into a serious business. If that’s you, then there are a few concepts that will help you pick the right trades to get after. This is important because picking the right trades is most of the game. First, the Market Gods …
rsims is a new package for fast, quasi event-driven backtesting in R. You can find the source on GitHub, docs here, and an introductory blog post here. Our use case for rsims was accurate but fast simulation of trading strategies. I’ve had a few questions about how I made the backtester as fast as it …
