How Algorithmic Trading Makes Money On Energy

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How Algorithmic Trading Makes Money On Energy by Michael McDonald,

It’s getting harder to be a human – at least a human making a good living in the financial markets.

High frequency trading, algorithmic trading, dark pools, and a variety of other technical finance concepts are upending the once cushy job of Wall Street traders everywhere and making computers more and more essential to the trading that powers Wall Street forward each day. More than 80 percent of trades today are made by computers and almost half are high frequency trades held for a split-second according to the SEC. This is increasingly becoming true in the energy and commodities space – areas of the market that have been a little less efficient than traditional equities.

The new energy trader is not a Wall Street type guy in a suit and suspenders with a sixth sense for where markets are going. Instead it’s an engineer, a mathematician, or an economist who understands how to use data and numbers to identify opportunities in the markets. These opportunities might be present for weeks at a time or just a few minutes, but they are based on the science of economics in all cases rather than market psychology. Computers don’t have any psychology, and these days, nearly everyone trading is using an automated computer program. The last vestiges of human trading are mostly retail investors who are eschewing the trend of ETFs.

None of this means that investing or investors are dead. It does mean that investors need to evolve though. They need to understand data and algorithmic trading and how these tools can be used to make a profit. The rewards for those who embrace new tools like this are huge. (As a financial economist, I help funds develop trading strategies using algorithmic approaches). Top quant hedge funds – those using algorithmic trading tools – overall last year had a great year and posted enormous returns of as much as 47 percent.

Algorithmic or quant trading involves a number of different tools and software packages, some of which are very specialized. At its core though, algorithmic trading does not have to be complex or difficult. Instead, investors simply need to understand a few basic concepts.

First, investors need to understand that when dealing in quant trading, the idea is to reduce all information to a series of mathematical values. Rather than talking about a firm with a great management team and a strong economic moat, the firm should be described based on data – earnings growth, potential earnings in the future, mathematical ranking of management capabilities, etc. This approach takes the uncertainty and human error out of trading.

Second, quant trading relies on correlations between different variables. For instance, oil prices have a very obvious and powerful correlation to the earnings and stock prices of oil companies. Similarly, rig counts have a correlation to oil prices, and in turn, to stock prices of oil companies. These correlations are not always going to be stable or perfect – a $1 increase in the price of oil per barrel might translate into a $0.50 or a $2 increase in the stock price of XLE.

Third, economic and stock data often display what is called momentum. Momentum is a concept similar to “trends” but a little more scientific. Trends are simply directions, momentum relates to the speed of the market in adjusting to new information.

One opportunity here is for investors to take advantage of quant trading opportunities by looking for correlations between individual equities. For example, investors might consider opportunities to buy stocks in economically linked firms based on the earnings of their customers or suppliers.

An even easier approach is to look for intraday price differences in oil stocks and buy the laggards that have previously done well. For instance, if stock XYZ is usually a leading gainer compared to peers when oil prices rise, but today it is not gaining as quickly as peers as oil prices rise, then the stock is probably a good target to buy. These trades don’t work out all of the time of course, but on average, over time they can earn an investor an enormous profit. Proper back-testing of the strategy is key to making sure it will work correctly though.

Quant trading is not easy and most quant trading firms have deep pockets which individuals lack. Yet individual investors can still get started in the space potentially opening up new avenues of investing.

How Algorithmic Trading Makes Money On Energy by Michael McDonald of

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