In the first post in this series, we examined the process of science and its relevance for trading. In this post, we'll look more specifically at how we frame our trading ideas as scientists.
If I am trading like a scientist, I am carefully observing the market and watching for patterns. I already have observed many markets in many conditions and have some theoretical understanding of what makes markets move across different time frames--from interest rates and liquidity at longer periods to the aggressiveness of large traders at short ones.
Perhaps I notice that, as selling hits the market, volume is declining and fewer individual stocks are making fresh price lows. I also notice that one sector of stocks, the semiconductors, are actually moving higher and gaining money flow. Bonds, which had been falling with stocks, are now catching a bid. I hypothesize that the market is running out of sellers, that we are in the process of bottoming, and that we will likely see short covering as a result. That should propel the market higher.
Having formed this hypothesis, I make note of a recent short-term high price in the semiconductors and the low price. I say to myself, in essence, "I think we will hit this price (prior high) before we touch that price (recent low)." In other words, I am willing to risk a possible move back to the low in order to participate in the hypothesized move to the high.
This is only a hypothesis, however; it is not truth. For that reason, as a scientist, I must remain open to data that tell me my hypothesis is not supported. A fresh influx of sellers hitting bids; a fresh drop in bonds--many factors could alert me to a potential problem with my hypothesis. I also must keep my bet on this hypothesis modest: to risk much of my capital on the idea is to treat the tentative formulation as absolute truth.
It is in this context that every good trade tests a hypothesis. When we observe a pattern, frame an idea, test the idea with a trade, and actually profit, our idea--our theory--is supported. That may lead us to another trade that extends this idea. Conversely, if we do not profit from our theory, we may need to go back to observation mode and revise our explanations.
Thus it is that a scientific trader will gain confidence and become a bit more aggressive when his or her ideas are confirmed; a bit more cautious when ideas do not pan out. When you trade like a scientist, every good trade provides you with information, because every good trade is a solid test of your market understanding. For this reason, the scientific trader values losing trades. They, no less than the winners, are data to be assimilated and can push you to further market insight.
In the third and final post in the series, I'll look at three common mistakes traders make from the scientific vantage point.