Tu Lin Jiang / Profile

Forecasting financial time-series is a required element of any investing activity. The concept of investing itself - put up money now to gain profits in future - is based on the concept of predicting the future. Therefore, forecasting financial time-series underlies the activities of the whole investing industry - all organized exchanges and other securities trading systems.

The indicator shows trend lines displaying the recent events on the market. The indicator is developed considering the recommendations and the approach of Thomas Demark concerning technical analysis. The indicator displays both the last direction of the trend and the next-to-last opposite direction of the trend.

This article has been made to show you how to use neural networks, via FANN2MQL, using an easy example: teaching a simple pattern to the neuralnetwork, and testing it to see if it can recognize patterns it has never seen.

The article contains considerations regarding creation of a DLL library - wrapper that will enable the interaction of MetaTrader 4 and the MATLAB mathematical desktop package. It describes "pitfalls" and ways to overcome them. The article is intended for prepared C/C++ programmers that use the Borland C++ Builder 6 compiler.

The article details TD points and TD lines discovered by Thomas DeMark. Their practical implementation is revealed. In addition to that, a process of writing three indicators and two Expert Advisors using the concepts of Thomas DeMark is demonstrated.

This article is a continuation of previous articles on deep neural network and predictor selection. Here we will cover features of a neural network initiated by Stacked RBM, and its implementation in the "darch" package.

This article shows you how to easily use Neural Networks in your MQL4 code taking advantage of best freely available artificial neural network library (FANN) employing multiple neural networks in your code.