Trading System Development 101 (Part 7)

Today I’m going to start discussing a data-mining approach to trading system development.

With the walk-forward approach, I have to find strategies and program them. Strategies are available in many places: books on technical analysis and trading strategies, articles, blog posts, vendors, webinars, etc.

Coming up with the strategies can take some work, though. In my experience to date, I started with a general familiarity of basic indicators and some e-books. I tested many of those on 2-3 markets. I now need to do some digging in order to continue along this path.

Another approach to trading system development involves data mining. According to microstrategy.com:

     > Data mining is the exploration and analysis of large data to
     > discover meaningful patterns and rules. It’s considered a
     > discipline under the data science field of study… [that]
     > describes historical data… data mining techniques are used
     > to build machine learning models that power modern AI apps
     > such as search engine algorithms…

I started by purchasing point-and-click software that creates trading strategies without any required programming by me.

The software is a genetic algorithm that will search many possible entry signal combinations, exit signals, and other exit criteria to form the best strategies based on selected test criteria and fitness functions (e.g. Sharpe Ratio, net profit, profit factor, etc.).

The software will then create tens to hundreds of strategies that meet my criteria. I can view fitness functions, equity curves, different kinds of Monte Carlo analyses, etc.

The software compares trading signals/strategies against random signals/strategies. This allows me to assess the probability a strategy has edge with predictive value that could not have occurred randomly. While a genetic algorithm curve fits, I don’t want an overfit strategy. A randomly-mined baseline (along with buy-and-hold) can serve as a minimum threshold to beat.

Aside from comparing against random, the software comes pre-packaged with a number of other stress tests that also help to assess whether strategies are honing in on bona fide signal or overfitting to noise. The array of stress tests is impressive. The question is how well they do to forecast profitable strategies. I won’t know that until I find some.

Depending on what particular application is purchased, these packages can do even more. The one I have can build strategy portfolios, track correlations among strategies, and generate full strategy code for different brokerage platforms.

I will continue next time.