Access to this page has been denied because we believe you are using automation tools to browse the website. Deep Blue was the first gamma scalping forex market that won a chess world championship. Deep Blue was a model based system with hardwired chess rules.
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 principles A learning algorithm is fed with data samples, normally derived in some way from historical prices. Each sample consists of n variables x1 .
The predictors, features, or whatever you call them, must carry information sufficient to predict the target y with some accuracy. They must also often fulfill two formal requirements. First, all predictor values should be in the same range, like -1 . Second, the samples should be balanced, i. So there should be about as many winning as losing samples.
Regression algorithms predict a numeric value, like the magnitude and sign of the next price move. Classification algorithms predict a qualitative sample class, for instance whether it’s preceding a win or a loss. Some algorithms, such as neural networks, decision trees, or support vector machines, can be run in both modes. A few algorithms learn to divide samples into classes without needing any target y.
That’s unsupervised learning, as opposed to supervised learning using a target. Somewhere inbetween is reinforcement learning, where the system trains itself by running simulations with the given features, and using the outcome as training target. Whatever signals we’re using for predictors in finance, they will most likely contain much noise and little information, and will be nonstationary on top of it. Therefore financial prediction is one of the hardest tasks in machine learning.
More complex algorithms do not necessarily achieve better results. The selection of the predictors is critical to the success. Here’s a list of the most popular data mining methods used in finance. Indicator soup Most trading systems we’re programming for clients are not based on a financial model. The client just wanted trade signals from certain technical indicators, filtered with other technical indicators in combination with more technical indicators. I’m trading it manually, and it works.
And those were also often profitable in real trading. The client had systematically experimented with technical indicators until he found a combination that worked in live trading with certain assets. Candle patterns Not to be confused with those Japanese Candle Patterns that had their best-before date long, long ago. The modern equivalent is price action trading.