Gang Wu / Profile

In this article, we will create the basic functionality of a scalping Market Depth tool. Also, we will develop a tick chart based on the CGraphic library and integrate it with the order book. Using the described Market Depth, it will be possible to create a powerful assistant tool for short-term trading.

This series of articles continues exploring deep neural networks (DNN), which are used in many application areas including trading. Here new dimensions of this theme will be explored along with testing of new methods and ideas using practical experiments. The first article of the series is dedicated to preparing data for DNN.

The second article of the series about deep neural networks will consider the transformation and choice of predictors during the process of preparing data for training a model.

The article discusses the methods for building and training ensembles of neural networks with bagging structure. It also determines the peculiarities of hyperparameter optimization for individual neural network classifiers that make up the ensemble. The quality of the optimized neural network obtained in the previous article of the series is compared with the quality of the created ensemble of neural networks. Possibilities of further improving the quality of the ensemble's classification are considered.

The article considers the possibility to apply Bayesian optimization to hyperparameters of deep neural networks, obtained by various training variants. The classification quality of a DNN with the optimal hyperparameters in different training variants is compared. Depth of effectiveness of the DNN optimal hyperparameters has been checked in forward tests. The possible directions for improving the classification quality have been determined.

In this article, we continue to consider writing the code to trading systems described in a book by Linda B. Raschke and Laurence A. Connors “Street Smarts: High Probability Short-Term Trading Strategies”. This time we study Momentum Pinball system: there is described creation of two indicators, trade robot and signal block on it.

Nowadays, every trader must have heard of neural networks and knows how cool it is to use them. The majority believes that those who can deal with neural networks are some kind of superhuman. In this article, I will try to explain to you the neural network architecture, describe its applications and show examples of practical use.

This article provides an answer to the question: "Is it possible to formulate an automated trading strategy based on history data with neural networks?".

The article is intended for beginners in baking "multi-layered" cakes.