Like most forays into the market, this author’s started unexpectedly. It began with a company called Promise Land Technologies and a general purpose neural network plug-in for Microsoft Excel called Braincel. The patented technology was successful, and there were many clients, from manufacturing to the U.S. Trotting Association, but the most common by far were trading related. About 40%-50% of customers bought Braincel to predict the markets.
This venture followed on the heels of working for a jet engine components company performing fail mode and effect analysis: a study of component failure, how to prevent failure and what could result to the whole in the case of single part failure.
This experience, and an undergraduate degree in physics, provided a decent head start in a trading career, with these lessons fully set in place:
• Classic signal processing methods do not work without domain expertise.
• Many classic trading rules published in most books do not work. Period.
• It is vital to understand what each element of a trading system does and what happens if it fails.
After spending 1992 through 1994 studying and backtesting the markets, and using Excel for the heavy lifting — a process that inspired a seven-part series in Futures in 1999 on developing trading systems in spreadsheets — efforts were directed toward integrating neural networks into a complete trading system design that also involved genetic algorithms, classic rule-based systems and domain expertise. This complete approach was further bolstered by the failed efforts of many major investment banks at the time that were relying almost exclusively on neural networks to develop trading strategies, treating neural nets like the magic wands they could never be.
Nevertheless, even through the smoke from the billions of dollars that investment banks burned through to compute their way to the Holy Grail, the promise of advanced technologies was clear. Used carefully, these tools could make traders’ lives easier and more profitable. In the last 15 years, more than 120 articles in this space have been dedicated to that goal.
Some of these articles have been more useful than others. Here we review the best of them and see how they have evolved over the years.
SYSTEM TESTING & EVALUATION
If there’s one topic that hasn’t grown stale over the last 15 years, it’s system testing and evaluation. A recurring theme has been how to create kind of a system DNA — how to view a system’s statistical properties as if it were the system’s genetic makeup.
For example, if your system has 40% winning trades over its history and a 3:1 win/loss ratio and these patterns change drastically either for the good or bad, then it’s a warning sign. Specifically, if a trend-following system wins 10 trades in a row, it might be time to stop trading it for a while, because it does not match the system’s normal pattern. Monitoring and comparing the statistical distribution of live results to historical tests is vital. These concepts were discussed as early as March and April 1995.
Another lesson we’ve learned is that optimization is an important step. First, we must optimize across a broad range of parameters and average key statistics, such as average trade, net profit and drawdown. These must be analyzed with respect to volatility measures. The standard of requiring net profit to be positive after subtracting two times the standard deviation remains valid today.
In October 1995, we discussed using statistical analysis to develop better exits. We analyzed maximum adverse excursion for a given bar after entry and final trade profit using a scatter chart. We can use this to find an area where a trade loses more than the adverse excursion.