It’s time for the 5th and final part of the Build Better Strategies series. In part 3 we’ve discussed the development process of a model-based system, and consequently we’ll conclude the series with developing a data-mining system. The principles of data mining and machine learning have been the topic of part 4. For our short-term trading example we’ll use a deep learning algorithm, a stacked autoencoder, but it will work in the same way with many other machine learning algorithms. With today’s software tools, only about 20 lines of code are needed for a machine learning strategy. I’ll try to explain all steps in detail. Continue reading “Better Strategies 5: A Short-Term Machine Learning System”
Deep Blue was the first computer that won a chess world championship. That was 1996, and it took 20 years until another program, AlphaGo, could defeat the best human Go player. Deep Blue was a model based system with hardwired chess rules. AlphaGo is a data-mining system, a deep neural network trained with thousands of Go games. Not improved hardware, but a breakthrough in software was essential for the step from beating top Chess players to beating top Go players.
In this 4th part of the mini-series we’ll look into the data mining approach for developing trading strategies. This method does not care about market mechanisms. It just scans price curves or other data sources for predictive patterns. Machine learning or “Artificial Intelligence” is not always involved in data-mining strategies. In fact the most popular – and surprisingly profitable – data mining method works without any fancy neural networks or support vector machines. Continue reading “Build Better Strategies! Part 4: Machine Learning”
The more data you use for testing or training your strategy, the less bias will affect the test result and the more accurate will be the training. The problem: price data is always in short supply. Even shorter when you must put aside some part for out-of-sample tests. Extending the test or training period far into the past is not always a solution. The markets of the 1990s or 1980s were very different from today, so their price data can cause misleading results.
In this article I’ll describe a simple method to produce more trades for testing, training, and optimizing from the same amount of price data. The method is tested with a price action system based on data mining price patterns. Continue reading “Better Tests with Oversampling”