Algorithmic Trading Demystified
To many investors, the increasing volume of algorithmic trading (over 50% of all stock trading at last count1) is evidence of Skynet out to destroy all of human financial markets once and for all. What with the 2010 Flash Crash, the August 2011 market panic, and the 2012 Knight Capital's "technology glitch" still fresh in our mind, there may be some justification in this fear.
But not all algorithmic trading is high frequency trading, and there are certainly benefits in trading algorithmically. Psychologists tell us that we humans often make wrong decisions due to greed and fear. If nothing else, execution automation is a way to impose discipline on ourselves and not give in to these emotions. A bigger benefit of algorithmic trading is that it allows us to adopt a "scientific" approach to studying the financial markets, where assumptions can be tested, and causes and effects established, at least statistically. This scientific approach is called "backtesting".
Many people confuse algorithmic trading with technical analysis. Sure, a technical analyst can become an algorithmic trader fairly easily, but so can an investor who relies on fundamental (but quantifiable) knowledge about stocks. Discretionary traders, too, can benefit from backtesting some of their ideas, if only to confirm their intuition.
Backtesting and execution automation are the 2 main steps of algorithmic trading. Believe it or not, technology has advanced to such a point that many discretionary traders and traditional investors with little or no programming or math skills can now participate in both areas. In the coming articles, I will describe the life-cycle of building a successful algorithmic trading program, including such details as
- Where to look for trading ideas that can be backtested and automated.
- Where to find historical data to backtest.
- What software platforms can a non-programmer use for backtesting.
- What are the pitfalls involved in backtesting.
- What software platforms can a non-programmer use for automating executions.
- Why does live trading performance often differ from backtests.
Hopefully by the end of our journey, you will find it easy enough to jump on the algo trading bandwagon, or at least, gain some understanding of whether there is anything valuable in it for you!
Ernest Chan is a hedge fund manager and the author of "Quantitative Trading: How to Build Your Own Algorithmic Trading Business" (Wiley, 2009). Find out more about him at www.epchan.com.
1 Times Topics: High-Frequency Trading, The New York Times, December 20, 2012
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