In an ideal trading universe, we’d all have a big golden “causation magnifying glass”. Through the lens of this fictional tool, you’d zoom in and understand the fleeting, enigmatic nature of the financial markets, stripping bare all its causes and effects. Knowing exactly what causes exploitable inefficiencies would make predicting market behaviour and building profitable trading strategies a fairly cushy gig, right? If you’re an engineer or scientist reading this, you are probably nodding along, hoping I’ll say the financial markets show some kind of domino effect for capitalists. That you can model them with the kinds of analytical methods you’d throw at a construction project or the petri dish. But unfortunately, trying to shoehorn the markets into formulas is a futile exercise... like stuffing Robot Wealth’s frontman Kris into a suit. Since the markets aren’t strictly deterministic, this makes testing your new and exciting strategy ideas a bit tricky. We’d all love to know for sure whether our ideas will be profitable before we throw real money at them. But, since you can’t realistically apply mathematical equations to your...

In our inaugural Algo Bootcamp, we teamed up with our super-active community of traders and developed a long-only, always-in-the-market strategy for harvesting risk premia. It holds a number of different ETFs, varying their relative weighting on a monthly basis. We're happy with it. However, the perennial question remains: can we do better? As you might expect, we found evidence suggesting that risk premia are time-varying. If we could somehow predict this variation, we could use that prediction to adjust the weightings of our portfolio and quite probably improve the strategy's performance. This might sound simple enough, but we actually found compelling evidence both for and against our ability to time risk premia returns. We're always telling our Bootcamp participants that developing trading and investment strategies requires the considered balancing of evidence in the face of uncertainty. In this case, we decided that there was enough evidence to suggest that we could weakly predict time-varying risk premia returns, at least to the extent that slight weight adjustments in accordance with these predictions might provide value. The strategy was already decent enough, so...

This article is part of a series derived from our most recent Algo Boot Camp, in which we developed a strategy for harvesting risk premia. We have allocated proprietary capital to the strategy, and many of our members are trading it too. In our Boot Camps we develop trading strategies in collaboration with the Robot Wealth community over an 8 week period. The Boot Camp format is proving incredibly useful for teaching our members how to research, develop, think about the markets and execute real trading strategies. They get to watch us do it every step of the way, and watch every decision we make. In our next Boot Camp, we'll be developing a portfolio of active FX strategies. Find out more about Robot Wealth's Algo Boot Camps, including how you can be a part of the next one, here. Investing: the easiest game in town? Trading and investing doesn't have to be complicated. Check out this chart: [caption id="attachment_5425" align="alignnone" width="680"] source: Dimson, Marsh and Staunton, Triumph of the Optimists[/caption]   The blue line shows returns from US Stocks from...

This is Part 2 in our Practical Statistics for Algo Traders blog series—don't forget to check out Part 1 if you haven't already.   Even if you've never heard of it, the Law of Large Numbers is something that you understand intuitively, and probably employ in one form or another on an almost daily basis. But human nature is such that we sometimes apply it poorly, often to great detriment. Interestingly, psychologists found strong evidence that, despite the intuitiveness and simplicity of the law, humans make systematic errors in its application. It turns out that we all tend to make the same mistakes - even trained statisticians who not only should know better, but do! In 1971, two Israeli psychologists, Amos Tversky and Daniel Kahneman,[footnote]Readers of the Robot Wealth blog will know that I'm a big fan of the work of Tversky and Kahneman. In fact, I'd go as far to call it the most important body of work related to understanding errors made by the human mind - something that is of obvious interest to traders. Check out Kahneman's "Thinking...

How do you feel when you see the word "statistics"?  Maybe you sense that it's something you should be really good at, but aren't.  Maybe the word gives you a sense of dread, since you've started exploring its murky depths, but thrown your hands up in despair and given up - perhaps more than once. If you read lots of intelligent-sounding quant blogs, you might even feel like your lack of statistical sophistication is what's standing between you and algo trading success. Well, you're not alone. The reality is that classical statistics is difficult, time-consuming and downright confusing. Fundamentally, we use statistics to answer a question - but when we use classical methods to answer it, half the time we forget what question we were seeking an answer to in the first place. But guess what? There's another way to get our questions answered without resorting to classical statistics. And it's one that will generally appeal to the practical, hands-on problem solvers that tend to be attracted to algo trading in the long run. Specifically, algo traders can leverage their...

One of the ongoing research projects inside the Robot Wealth community involves an FX strategy with some multi-week hold periods. Such a strategy can be significantly impacted by the swap, or the cost of financing the position. These costs change over time, and we decided that for the sake of more accurate simulations, we would incorporate these changes into our backtests. This post shows you how to simulate variable FX swaps in both Python and the Zorro trading automation software platform. What is Swap? The swap (also called the roll) is the cost of financing an FX position. It is typically derived from the central bank interest rate differential of the two currencies in the exchange rate being traded, plus some additional fee for your broker. Most brokers apply it on a daily basis, and typically apply three times the regular amount on a Wednesday to account for the weekend. Swap can be both credited to and debited from a trader's account, depending on the actual position taken. Why is it Important? Swap can have a big impact on strategies...