Here’s a lesson that I’m consistently forced to re-learn: volatility and range matter. A lot. This is something which keeps coming up in my trading, and perhaps it represents a mental defect on my part that I can’t internalize it. Hopefully you can do better. Continue reading
Before continuing, you need to have a solid grasp of alpha and beta – otherwise the rest of this article will make no sense.
Back in part three, we developed a method for trading the S&P 500 for use in the long term speculative account. In other words, we’ve figured out how to trade beta. That’s good, but one of the goals of this speculation method is to limit beta exposure to +- 20% of the account value. The purpose of this limit is to mitigate account damage in unexpected crash situations (when long) or boom situations (when short). Since I’ve allocated 20% of the portfolio to beta trend following (which could thus produce betas between +-20%), ideally everything else in the portfolio should have a beta of zero. That’s a difficult requirement for stocks, however, because they essentially all have a positive beta, and in most cases that beta constitutes at least half of the stock’s movement. What we need is something that acts like a stock, but with no beta component. Continue reading
An arbitrage is a trade that produces near risk-free and near guaranteed profits. In general, independent and retail traders are not successful as arbitrageurs. We’ll get into the reasons why in a bit. But it’s still important to understand how arbitrages work, because they’re a fundamental part of the structure of the market. Each arbitrage defines an equation, for lack of a better word, of how the prices of various instruments should be related to each other and to interest rates. Some of these relationships are trivial to understand, but others are far from obvious. Continue reading
Before we continue with the speculative alternatives to investment series, I need to introduce some mathematical concepts. Yeah, yeah. You’ve got a hangover and barely eked out a C- in calculus. But stick with me.
Two key tools of quantitative finance are:
- combining financial instruments together
- slicing a single financial instrument up into smaller pieces to understand how it behaves
It’s pretty easy to understand how you combine instruments – the easiest way is to make an index. You take a bunch of instruments (say, stocks) that are somehow similar and average their price. Typically you weight the average by a size metric like market cap. Voila – an index. This is a big data approach to understanding the movement of the market.
It’s a little less clear how you take one instrument and slice it up. Turns out there’s slightly beefier math involved – Pearson correlation, linear models and linear regression in this particular case. It’s OK if you skipped that class – there are plenty of tools to help us get through the math. Continue reading
“I am not superstitious, but I avoid situations in which I continually lose.” -Barry Greenstein, Ace on the River
I want to get a few terms defined that I’m going to use in subsequent posts. Specifically, I want to talk about the way predictive methods and markets interact. My experience is that there are three basic classes of interactions:
I’ve been seeing the buzzword “big data” cropping up a lot lately. As best I can tell, in business-droid speak it refers to any very large collection of non-uniform or hard to work with data. For example, all the videos on YouTube. The large part should be obvious. The hard to work with part stems with the fact that you can’t really extract any value from the videos without doing something difficult (or at least time consuming) – watching them. There’s lots of big data out there – census data, social media, credit card data, military intelligence, etc.
While I’m always loath to jump on trendy business bandwagons, I think this one is bringing to the public an idea long overdue, and which I strongly believe in:
Given a choice between having better analysis or more data, 99% of the time you’d be way better off having more data.
Here’s another little short piece of advice that can save you some pain: Don’t use market orders. Instead use a limit order across the bid-ask gap. Under normal conditions you will get exactly the same result, but in abnormal circumstances you can avoid big trouble. Continue reading
I’ve been on a bit of a economics kick lately, but it’s time to get back to the real purpose of this blog, which is teaching profitable trading. A big part of that is passing on “lessons learned” – mistakes which cost me money and which you hopefully can avoid thanks to me highlighting them. One of those mistakes resulted from what I’ve termed “phasing”. Continue reading
I’ve previous written about auction markets and the order book and order types. Now I want to dig in at a very fine grained level and look at how the exchange matcher decides which orders to match together. This may seem like a painfully detailed topic, but it turns out to have a fairly major impact on the profitability of short term traders. Continue reading
“I possess a quality unquantifiable by its very nature.”
“And what exactly is that, Peter?”
Peter Riviera & Molly, Neuromancer
Prediction is a funny thing. You’ll encounter lots of traders who swear up and down that what they do is not predicting the market. This, of course, is nonsense. When you take a position in the market you’re predicting that price will move in the direction of your position and thus you’ll be able to exit your position at a profit. It’s as simple as that. Prediction is the heart of trading So why do many traders, even some successful ones, deny they engage in prediction at all? Continue reading