Tu Lin Jiang / Profile

Object-oriented programming enables creation of a more compact code that is easy to read and modify. Here we will have a look at the example for three ONNX models.

It has been over a year since MQL5 started providing native support for OpenCL. However not many users have seen the true value of using parallel computing in their Expert Advisors, indicators or scripts. This article serves to help you install and set up OpenCL on your computer so that you can try to use this technology in the MetaTrader 5 trading terminal.

The article describes a method of automated creation of neural network EAs using MQL5 Wizard and Hlaiman EA Generator. It shows you how you can easily start working with neural networks, without having to learn the entire body of theoretical information and writing your own code.

This article provides programmatic definition of one of the movement continuation models. The main idea is defining two waves — the main and the correction one. For extreme points, I apply fractals as well as "potential" fractals - extreme points that have not yet formed as fractals.

This article covers the main principles set fourth in evolutionary algorithms, their variety and features. We will conduct an experiment with a simple Expert Advisor used as an example to show how our trading system benefits from optimization. We will consider software programs that implement genetic, evolutionary and other types of optimization, and provide examples of application when optimizing a predictor set and parameters of the trading system.

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 is a follow-up to the previous one called "Reversal patterns: Testing the Double top/bottom pattern". Now we will have a look at another well-known reversal pattern called Head and Shoulders, compare the trading efficiency of the two patterns and make an attempt to combine them into a single trading system.

This article is dedicated to a new and perspective direction in machine learning - deep learning or, to be precise, deep neural networks. This is a brief review of second generation neural networks, the architecture of their connections and main types, methods and rules of learning and their main disadvantages followed by the history of the third generation neural network development, their main types, peculiarities and training methods. Conducted are practical experiments on building and training a deep neural network initiated by the weights of a stacked autoencoder with real data. All the stages from selecting input data to metric derivation are discussed in detail. The last part of the article contains a software implementation of a deep neural network in an Expert Advisor with a built-in indicator based on MQL4/R.

It is essential to detect whether a market is flat or not for many strategies. Using the well known ADX we demonstrate how we can use the Strategy Tester not only to optimize this indicator for our specific purpose, but as well we can decide whether this indicator will meet our needs and get to know the average range of the flat and trend markets which might be quite important to determine stops and targets of the markets.

Using virtual trading, you can create an adaptive Expert Advisor, which will turn on and off trades at the real market. Combine several strategies in a single Expert Advisor! Your multisystem Expert Advisor will automatically choose a trade strategy, which is the best to trade with at the real market, on the basis of profitability of virtual trades. This kind of approach allows decreasing drawdown and increasing profitability of your work at the market. Experiment and share your results with others! I think many people will be interested to know about your portfolio of strategies.

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

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.