This is a discussion on Metatrader 5 / Metatrader 4 for MQL5 / MQL4 articles preview within the General Discussion forums, part of the Trading Forum category; In the third part, we have created a basic system for searching signals, which however was based on a small ...
more...In the third part, we have created a basic system for searching signals, which however was based on a small set of indicators and a simple set of search rules. Also, I received suggestions for usability improvements which could be made in the visual part of the trade monitor. This is what we are going to implement in this part.
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more...This article is a kind of postscript for a series of articles devoted to grid Expert Advisors:
- Developing a cross-platform grid EA
- Developing a cross-platform grid EA (part II): Range-based grid in trend direction
- Developing a cross-platform grid EA (part III): Correction-based grid with martingale
- Developing a cross-platform grid EA (Last part): Diversification as a way to increase profitability
Tweeter provides free platform for anyone to post anything on their site. It can be as valuable as financial tips or as valueless as any prominent person can be in expressing her/his thoughts. Since this article primary focus on the media instead of its contents, let's get started.
For those who can code in other programming languages might find these Twitter Libraries are useful for reference. They are great ressources that provide great insight into implementation details which are sometime not obvious from simply reading API documentation only.
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more...This article considers the application of neural networks when creating trading robots. This is the narrow sense of this problem. More broadly, we will try to answer some questions and to address several problems:
- Can a profitable system be created using machine learning?
- What can a neural network give us?
- The rationale for training neural networks for decision making.
- Neural Network: is it difficult or simple?
- How to integrate a neural network into a trading terminal?
- How to test a neural network? Testing stages.
- About training samples.
more...In this article, we continue to study various mathematical expression parsing methods and their implementation in the MQL language. In the first part we considered recursive descent parsers. The main advantage of such parsers is their user-friendly structure, which is directly related to specific grammar of expressions. But when it comes to efficiency and technical features, there are other types of parsers that are worth paying attention to.
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more...We have previously created the basic functionality, which is designed to assist the traders who prefer manual trading. We mainly focused on convenient work related to order placing, and thus most of the functions were related to market entries. However, any trading strategy, whether it be manual or automatic, should have three main stages when working with the markets. These include market entry rules, open position management and closing conditions. As for now, the toolkit only covers the first stage. Therefore, as further development, we can add more opportunities for working with open positions or pending order, and to expand conditions for closing deals. All calculation should be performed by the toolkit, while the decision should be made by the trader.
more...Table of Contents
- Introduction
- General
- What Are Overbought/Oversold Zones in terms of the Process Dynamics
- On Accuracy in Detecting the Overbought/Oversold Zones
- Consequences of Entering the Market When in the Overbought/Oversold Zone
- Evaluating Losses Related to the Risk of Opening a Position in Overbought/Oversold Zones
- Consequences of Exiting the Market When in the Overbought/Oversold Zone
- Pros and Contras of Using Conventional Indicators to Detect Overbought/Oversold Zones
- Setting Up a Group of Indicators Using the Values of Fibonacci Series
- That "Restless" Stochastic: Analyzing the Indicator Formula
- Testing a Group of Indicators Detecting the Overbought/Oversold Zones on Different Trend Types
- What's Wrong with RSI? Analyzing the Indicator Formula
- Results of Evaluating the Accuracy of Detecting the Overbought/Oversold Zones Depending on Settings and Trend Type
- Conclusions on the Efficiency of Conventional Indicators Detecting the Overbought/Oversold Zones
- New Approaches to Enhance the Efficiency of Detecting the Overbought/Oversold Zones
- General Requirements for the Algorithm to Detect the Overbought/Oversold Zones When Using Fractal and Candlestick Analysis
- Method Related to Identifying the Signs of the Trend "Slowdown"
- Algorithm of Anchoring to the Trend Onset
- Method Related to Distancing from the Trend Onset
- Conclusions
more...Gradient boosting is a powerful machine learning algorithm. The method produces an ensemble of weak models (for example, decision trees), in which (in contrast to bagging) models are built sequentially, rather than independently (in parallel). This means that the next tree learns from the mistakes of the previous one, then this process is repeated, increasing the number of weak models. This builds a strong model which can generalize using heterogeneous data. In this experiment, I used the CatBoost library developed by Yandex. It is one of the most popular libraries, along with XGboost and LightGBM.
The purpose of the article is to demonstrate the creation of a model based on machine learning. The creation process consists of the following steps:
- receive and preprocess data
- train the model using the prepared data
- test the model in a custom strategy tester
- port the model to MetaTrader 5
The Python language and the MetaTrader 5 library are used for preparing the data and for training the model.
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