Alpha isn’t just about finding people willing to trade at bad prices. Although that’s a necessary condition, it’s not enough. We also need to know why people are willing to buy too expensive or sell too cheap. The reasons are pretty simple: The first reason requires the existence of other uninformed traders, or what some …
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.)
dplyr 1.1.0 was a significant release that makes several common data operations more syntactically intuitive. The most significant changes relate to joins and grouping/aggregating operations. In this post we’ll look at the changes to joins. First, install and load the latest version of dplyr: install.packages(“dplyr”) library(dplyr) A new approach to joins The best way to …
The changing face of market data providers Over the last few years, a number of new market data providers have come online. They tend to have modern websites, broad coverage, and well-documented RESTful APIs. Their services are often priced very competitively – especially for personal use – and usually have generous free tiers. One such …
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 …
rsims is a new package for fast, realistic (quasi event-driven) backtesting of trading strategies in R. Really?? Does the world really need another backtesting platform…?? It’s hard to argue with that sentiment. Zipline, QuantConnect, Quantstrat, Backtrader, Zorro… there are certainly plenty of good options out there. But allow me to offer a justification for why …
It’s easy to lose money trading if you do certain things: Trade too much (paying fees and market impact on each transaction) Size positions too big (high volatility hurts compounding ability, and in the extreme can cause you to blow up) Short positive drift/risk premia Perhaps surprisingly, it’s actually quite hard to lose money consistently …
If you have some factor that you think predicts future stock returns (or similar) and you are making charts like below, then here are some tips… We’ll go through an example of trying to “time” SPX with the level of VIX. You get daily SPX index prices and daily VIX close data You align them …
