Learning to Trade - Learning to Trade: The Psychology of...

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Learning to Trade: The Psychology of Expertise Brett N. Steenbarger, Ph.D. When people hear that I am an active trader and a professional psychologist, they naturally want to hear about techniques for mastering emotions in trading. That is an important topic to be sure, and later in this article I will even have a few things to say about it. But there is much more to psychology and trading than “trading psychology”, and that is the ground I hope to cover here. Specifically, I would like to address a surprisingly neglected issue: How does one gain expertise as a trader? It turns out that there are two broad answers to this question, focusing upon quantitative and qualitative insights into the markets. We can dub these research expertise and pattern-recognition expertise , respectively. These perspectives are much more than academic, theoretical issues. How we view knowledge and learning in the markets will shape the strategies we employ and—quite likely—the results we will obtain. In this article, I will summarize these two positions and then offer a third, unique perspective that draws upon recent research in the psychology of learning. I believe this third perspective, based on implicit learning , has important, practical implications for our development as traders. Developing Expertise Through Research The research answer to our question says that we gain trading expertise by performing superior research. We collect a database of market behavior and then we research variables (or combinations of variables) that are significantly associated with future price trends. This is the way of mechanical trading systems , as in the trading strategies developed with TradeStation and the systems featured on the FuturesTruth.com site. We become expert, the mechanical system trader would argue, by building a better mousetrap —finding the system with the lowest drawdown, least risk, greatest profit, etc. A variation of the research answer can be seen in traders who rely on data-mining strategies . The data-miner questions whether there can be a single system appropriate for all markets or appropriate for all time frames. To use a phrase popularized by Victor Niederhoffer, the market embodies “ever-changing cycles”. The combination of predictors that worked in the bull market of 2000 may be disastrous a year later. The data-miner, therefore, engages in continuous research: modeling and remodeling the markets to capture the changing cycles. Tools for data mining can be found at kdnuggets.com. There are hybrid strategies of research, in which an array of prefabricated mechanical systems are defined and then applied, data-mining style, to individual stocks to see which ones have predictive value at present . This is the approach of “scanning” software, such as Nirvana Systems’ OmniTrader. By scanning a universe of stocks and indices across an array of systems, it is possible to determine which systems are working best for which instruments.
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As most traders are aware, the risk of research-based strategies is that of overfitting. If
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Learning to Trade - Learning to Trade: The Psychology of...

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