SUMMARY
Quantitative trading strategies are a combination of technical and statistical analysis which, when applied, generate buy and sell signals.
Once these trading strategies are formed, their performance is tested historically to validate the trading ideas.
After the performance is tested, we select the markets to be traded.
By trading a widely diversified portfolio, we are able to minimize our risk while maintaining expected reward.
Developing the idea, testing historical performance, and picking markets to trade are a few of the many techniques required for efficient and profitable trading strategies.
While a few books have touched on various areas of the development process, I believe this book is the first to fully capture all the nuances of the trading process.
Studying these fundamental factors is the most common method of analyzing markets.
Technical analysis does not attempt to predict market movements based on fundamentals.
As a result, technical analysts believe that price action is the best source of information.
Catchy names such as "Head and shoulders top," "Symmetrical triangle," and "Trendline" are a large part of the technical analyst's toolbox.
Typically, the technical analyst relies on a good bit of discretion for his or her trading ideas.
While a pattern may look like a buy signal to one technical analyst, another may see a different pattern emerging and actually be preparing to sell the market.
Whenever we make statements about the market, we can perform mathematical and statistical tests to determine if we are correct in our beliefs.
Do changes in interest rates affect returns on the stock market? If corn prices have been rising, is it likely that they will continue to do so in the near future? Considering that historical data for market prices is available back to the turn of the twentieth century in many cases, we can study historical market prices and usually find answers to these questions once we quantify each of these questions.
The remainder of this book will attempt to answer questions aimed at understanding exactly how markets behave and how investors and traders can profit from this information.
Most of our study involves creating, testing, and applying trading strategies.
A trading strategy is a set of rules that signal the trader when to buy, when to sell, and when to sell short a market.
The signals can also be very complex and include statistical regression and relationships between many related markets.
The most vital part of trading system development is performance testing.
When we test historical performance, we first want to see if our strategies have been profitable in the past.
Because many strategies will be profitable historically, we need a methodology to compare the profitability among trading strategies.
Very often the most profitable system is not the best system for our trading.
I believe most traders use outdated and inconsistent performance measures to evaluate historical performance.
Finally, we need to develop a money management plan for trading our strategies.
Once we have designed and tested our trading strategy, the next choice is to decide which markets to trade.
Shares of companies trade on three major markets in the United States: the New York Stock Exchange , the American Stock Exchange, and the NASDAQ.
While the NYSE and AMEX are physical trading floors where buyers and sellers meet to trade shares, the NASDAQ is a linkage of market makers negotiating prices with customers and with each other.
Due to the high leverage and low transaction costs associated with the futures market, these markets have long been a popular trading vehicle for quantitative traders.
Most of these markets are actually combinations of other markets where one asset is bought and the other asset is sold short.
Once we design and test our quantitative trading strategies, we can implement them on these new markets to gain access to products outside the typical stock and futures markets.
Three forms of the EMH (Efficient Markets Hypothesis) exist: strong form EMH, semi-strong form EMH, and weak form EMH.
The strong form of EMH suggests that all information, both public and private, is always incorporated into current prices.
The semi-strong form of the EMH states that current prices reflect all information in the public domain, including annual company reports, USDA crop estimates, Wall Street research reports, and quality of corporate management.
The weak form EMH suggests that prices already reflect all information that can be derived from analyzing historical market data, such as closing prices, volume, and short interest.
All three forms of the EMH suggest that our attempts to make money by buying and selling based on prior price patterns are hopeless.
These cracks in the EMH hint that markets may not be as efficient as was once thought.
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