Random Forest (RF) with the use of bagging is one of the most powerful machine learning methods, which is slightly inferior to gradient boosting. This article attempts to develop a self-learning trading system that makes decisions based on the experience gained from interaction with the market.
The article considers an example of applying the fuzzy logic to build a simple trading system, using the Fuzzy library. Variants for improving the system by combining fuzzy logic, genetic algorithms and neural networks are proposed.
Currently, the IT sphere is experiencing a boom in neural networks and machine learning. Machine learning is widely used in various fields and is intended to replace the human brain for solving complex problems of classification and prediction.
TensorBot is a self-learning trading system with built-in adaptive strategy and a neural network that independently studies market patterns, remembers them and trades. This principle allows you to "train" a robot for trading on any financial instrument...