People often ask me for book recommendations. But here’s a better question: What’s going to help you make money today? Reading a book probably isn’t the answer. I’m not saying books aren’t useful. They absolutely are. But you’re not preparing for a PhD defence – you’re trying to turn

There are 2 good reasons to buy put options: Because you think they are cheap Because you want downside protection. You want to use the skewed payoff profile to protect a portfolio against large downside moves without capping your upside too much. The first requires a pricing model. Or, at the

Back in May 2020, in the eye of the Covid storm, we looked at overnight vs intraday returns in US equities. Intuitively, we’d probably expect to see higher average returns overnight when the market is closed – because it’s much more difficult to hedge and manage our exposures when the cash

A few years ago, I got deep into the idea of constructing a long/short equity options portfolio based on the kind of simple factor sorts that had been so successful in quant equity. My original intention was to set up an index and license it to fund managers. Of course, there are many reasons why

Last week, we looked at simple data analysis techniques to test for persistence. But we only looked at a feature that is measured over a single day – the absolute range. Such a feature makes it easy to test persistence because you don’t have the problem of overlapping data. Each data point is

An assumption we often make in trading research is that the future will be at least a little like the past. I see a lot of beginners making this assumption implicitly without recognising that they’re making it or thinking about whether it’s reasonable to do so. That’s a mistake. If you are

Here’s a thing that I suspect will make money, but that I haven’t yet tested (for reasons that I will explain shortly): Every day, at the start of the trading day, get the SPX straddle price and convert it to an expected SPX price move. Then at the end of the trading day, take the SPX price and

Here at Robot Wealth, we trade with Interactive Brokers (IB) primarily because they offer access to global markets at a reasonable price. In recent times, IB has put some time and effort into upping its tech game, including development of an API for interacting with its desktop trading applications.

This is the final article in our recent stat arb series. The previous articles are linked below: A short take on stat arb trading in the real world A general approach for exploiting stat arb alphas Ideas for crypto stat arb features Quantifying and combining crypto alphas A simple and effective way

This article continues our recent stat arb series. The previous articles are linked below: A short take on stat arb trading in the real world A general approach for exploiting stat arb alphas Ideas for crypto stat arb features Quantifying and combining crypto alphas A simple and effective way to

Modeling features as expected returns can be a useful way to develop trading strategies, but it requires some care. The main advantage is that it directly aligns with the objective of predicting and capitalising on future returns. This can make optimisation and implementation more intuitive. It also

Every time we trade, we incur a cost. We pay a commission to the exchange or broker, we cross spreads, and we might even have market impact to contend with. A common issue in quant trading is to find an edge, only to discover that if you executed it naively, you’d get killed with costs. In this

In this article, I’ll take some crypto stat arb features from our recent brainstorming article and show you how you might quantify their strength and decay characteristics and then combine them into a trading signal. This article continues our recent articles on stat arb: A short take on stat arb

This article continues our recent articles on stat arb: A short take on stat arb trading in the real world A general approach for exploiting stat arb alphas In this article, I’ll brainstorm some ideas for predictive features that you could potentially use in a crypto stat arb model. The ideas draw

Last week, I wrote a short article about statistical arbitrage trading in the real world. Statistical arbitrage is a well-understood concept: find pairs or baskets of assets you expect to move together, wait for them to diverge, and bet on them converging again. Simple enough. But making it work,

In textbooks, one often sees pairs trading algorithms start by regressing prices of Asset A on Asset B to calculate a hedge ratio. I’ve rarely seen anyone actually do this in the real world. That’s because it is a very unstable thing – especially for a pair of volatile assets, and especially

Here in Australia, we’re right in the depths of the silly season. We indulge in long lunches, take days off work, and generally let our hair down. In that spirit, I thought I might have some fun punting shitcoins. (Maybe my definition of fun differs from yours, but let’s run with it). For the

Let’s talk about carry trades. First, what exactly is a carry trade? A carry trade is a trade that pays you to hold it. A position where, if nothing changes except the passing of time, you expect to make money. Let’s go through some examples. FX carry The classic example is the FX carry trade,

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

Exponential weighting schemes can help navigate the trade-off between responsiveness and stability of the inherently noisy estimates we make from market data. We previously saw examples of calculating the exponentially weighted moving average of a vector, and estimating the correlation between SPY

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 than past data, since it may reflect the current state

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,

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

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:

Traders typically have many ideas for trading strategies – more than they can ever implement in practice! Therefore it’s useful to be able to move quickly in the early research phase. You want to disprove things as quickly as possible so that you can move onto the next thing. Obviously there is

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,

In this blog post, I will demonstrate how to work with stock price data using the DuckDB database management system in R. DuckDB is a fast and lightweight analytical database engine that is designed to work with various programming languages, including R. You can use Duck DB from the command line or

Towards the end of last year, we ran a couple of free Zoom webinars on: The Basics of Edge Extraction – the “trader smarts” of getting an edge Data Analysis for Traders – an interactive research session. Here are the recordings: Basics of Edge Extraction Data analysis for Traders The colab

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:

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

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,

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

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

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

Many beginner traders don’t realize how variable the p&l of a high-performing trading strategy really is. Here’s an example… I simulated ten different 5 year GBM processes with expected annual returns of 20% and annualized volatility of 10%. (If you speak Sharpe Ratios, I’m simulating a

Broadly, there are three types of systematic trading strategy that can “work.” In order of increasing turnover they are: Risk premia harvesting Economically-sensible, statistically-quantifiable slow-converging inefficiencies Trading fast-converging supply/demand imbalances This post provides an

Earlier versions of Zorro used to ship with a script for converting market data in Zorro binary format to CSV. That script seems to have disappeared with the recent versions of Zorro, so I thought I’d post it here. When you run this script by selecting it and pressing [Test] on the Zorro

Several years ago, I wrote about some experimentation I’d done with data mining for predictive features from financial data. The article has had several tens of thousands of views and nearly 100 comments. I think the popularity of the article lay in its demonstration of various tools and modeling

I have been sharing examples of simple real-time trading research on my Twitter account. I do this kind of thing a lot in the training program of our trading group – and I’m sharing in the hope that it might also help a wider audience. Here’s a piece of analysis I did recently on a really

I’m a big fan of Ernie Chan’s quant trading books: Quantitative Trading, Algorithmic Trading, and Machine Trading. There are some great insights in there, but the thing I like most is the simple but thorough treatment of various edges and the quant tools you might use to research and trade them.

The S&P index committee recently announced that Tesla, already one of the biggest stocks listed in the country, would be included in the S&P 500. Here’s the press release: Due to TSLA’s size, it was widely expected to have entered the S&P 500 index much earlier – but S&P has

This post is a BONUS LESSON taken directly from Zero to Robot Master Bootcamp. In this Bootcamp, we teach traders how to research, build and trade a portfolio of 3 strategies including an Intraday FX Strategy, a Risk Premia Strategy and a Volatility Basis Strategy. If you’re interested in adding

I recently listened to a podcast about one of the earliest human civilizations – the ancient Sumerians. Apparently, our system of minutes, hours, and days has been with us since the time of these ancient people, who developed it based on a simple base-12 counting system: There are three joints in

I was very sad to learn that Quantopian is shutting down its community services. Quantopian’s efforts to bring quant finance outside of institutions was a genuine game-changer. The educational content was solid, the tech was excellent, and the QuantCon conferences were professional, well-run, and

You’ve probably noticed that there’s a US election on the horizon. This is an event of known uncertainty: a “known unknown” in the now immortal language of Donald Rumsfeld. In trading, we sometimes observe marginal pricing inefficiencies around these “known unknowns”. For example, ahead

Trading books set a low bar for the reviewer. 99% are full of facile feel-good advice (don’t fight the trend, always use a protective stop). The 1% that are useful tend to either be dry technical treatments (quants who don’t trade), or sporadically helpful insights from traders who make money

This post presents an analysis of the SPY returns process using the QuantConnect research platform. QuantConnect is a strategy development platform that lets you research ideas, import data, create algorithms, and trade in the cloud, all in one place. For this research, I’ve used their online

On Zero to Robot Master Bootcamp, we teach how to build a portfolio of three automated systematic trading strategies. One of them is a long term Risk Premia Harvesting strategy which trades asset class ETFs. ETFs are useful instruments for analysing long term (tradeable) performance of various asset

Why aren’t calls more expensive than puts for an asset which is more likely to go up than down? We have an asset trading at $100 for which the distribution of future returns is a known fact. It has annual returns described by a normal distribution with mean 5% and standard deviation 10%. This is,