Articles on trading system automation in MQL5

Price trends form price channels that can be observed on financial symbol charts. The breakout of the current channel is one of the strong trend reversal signals. In this article, I suggest a way to automate the process of finding such signals and see if the channel breakout pattern can be used for creating a trading strategy.

Trading in financial markets is associated with a whole range of risks that should be taken into account in the algorithms of trading systems. Reducing such risks is the most important task to make a profit when trading.

The article deals with the concept of night trading, as well as trading strategies and their implementation in MQL5. We perform tests and make appropriate conclusions.

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.

In this article we are going to analyze the NRTR indicator and create a trading system based on this indicator. We are going to develop a module of trading signals that can be used in creating strategies based on a combination of NRTR with additional trend confirmation indicators.

We continue testing the patterns and trying the methods described in the articles about trading currency pair baskets. Let's consider in practice, whether it is possible to use the patterns of the combined WPR graph crossing the moving average. If the answer is yes, we should consider the appropriate usage methods.

The article considers one of the variants for Expert Advisor practical realization to trade DiNapoli levels using MQL5 standard tools. Its performance is tested and conclusions are made.

The article suggests a technology helping everyone to create custom trading strategies by assembling an individual indicator set, as well as to develop custom market entry signals.

For successful trading, we almost always need indicators that can separate the main price movement from noise fluctuations. In this article, we consider one of the most promising digital filters, the Kalman filter. The article provides the description of how to draw and use the filter.

The article deals with the popular trading method - triangular arbitrage. Here we analyze the topic in as much detail as possible, consider the positive and negative aspects of the strategy and develop the ready-made Expert Advisor code.

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.

The article considers the classic method for divergence construction and provides an additional divergence interpretation method. A trading strategy was developed based on this new interpretation method. This strategy is also described in the article.

The main difference of the trading system proposed in the article is the use of mathematical tools for analyzing stock quotes. The system applies digital filtering and spectral estimation of discrete time series. The theoretical aspects of the strategy are described and a test Expert Advisor is created.

The article deals with a simple approach to creating an automated trading system based on the chart linear markup and offers a ready-made Expert Advisor using the standard properties of the MetaTrader 4 and 5 objects and supporting the main trading operations.

The article deals with evaluating the performance of Signals Providers. We offer several additional parameters highlighting signal trading results from a slightly different angle than in traditional approaches. The concepts of the proper management and perfect deal are described. We also dwell on the optimal selection using the obtained results and compiling the portfolio of multiple signal sources.

We begin testing the patterns and trying the methods described in the articles about trading currency pair baskets. Let's see how oversold/overbought level breakthrough patterns are applied in practice.

In this article, we discuss the method of trading analysis by measuring angles in the MetaTrader 4 terminal. The article provides a general plan of using angles for trend movement analysis, as well as non-standard ways to the practical application of angle analysis in trading. The article also provides conclusions that can be useful for trading.

The article considers the ideology and methodology of building a recommendatory system for time-efficient trading by combining the capabilities of forecasting with the singular spectrum analysis (SSA) and important machine learning method on the basis of Bayes' Theorem.

The article describes the Fibo levels-based trading system developed by Joe DiNapoli. The idea behind the system and the main concepts are explained, as well as a simple indicator is provided as an example for more clarity.

In this article, I will tell you how to successfully trade by merging a very well-known strategy and a neural network. It will be about the Thomas DeMark's Sequential strategy with the use of an artificial intelligence system. Only the first part of the strategy will be applied, using the Setup and Intersection signals.

In this article, we develop and tests several strategies based on the Donchian channel using various indicator filters. We also perform a comparative analysis of their operation.

The article provides a brief overview of ten trend following strategies, as well as their testing results and comparative analysis. Based on the obtained results, we draw a general conclusion about the appropriateness, advantages and disadvantages of trend following trading.

We continue our discussion of the patterns traders can come across while trading currency baskets. In this part, we will consider the patterns formed when using combined trend indicators. Indicators based on a currency index are to be used as the analytical tool.

The MetaTrader 5 platform allows developing and testing trading robots that simultaneously trade multiple financial instruments. The built-in Strategy Tester automatically downloads required tick history from the broker's server taking into account contract specifications, so the developer does not need to do anything manually. This makes it possible to easily and reliably reproduce trading environment conditions, including even millisecond intervals between the arrival of ticks on different symbols. In this article we will demonstrate the development and testing of a spread strategy on two Moscow Exchange futures.

Following up our previous article on the currency baskets trading principles, here we are going to analyze the patterns traders can detect. We will also consider the advantages and the drawbacks of each pattern and provide some recommendations on their use. The indicators based on Williams' oscillator will be used as analysis tools.

The article describes the development of tools (indicator and Expert Advisor) for analyzing the '80-20' trading strategy. The trading strategy rules are taken from the work "Street Smarts. High Probability Short-Term Trading Strategies" by Linda Raschke and Laurence Connors. We are going to formalize the strategy rules using the MQL5 language and test the strategy-based indicator and EA on the recent market history.

The article features formalized rules of two trading strategies 'Turtle Soup' and 'Turtle Soup Plus One' from Street Smarts: High Probability Short-Term Trading Strategies by Linda Bradford Raschke and Laurence A. Connors. The strategies described in the book are quite popular. But it is important to understand that the authors have developed them based on the 15...20 year old market behavior.

Is it possible to develop an Expert Advisor able to optimize position open and close conditions at regular intervals according to the code commands? What happens if we implement a neural network (multilayer perceptron) in the form of a module to analyze history and provide strategy? We can make the EA optimize a neural network monthly (weekly, daily or hourly) and continue its work afterwards. Thus, we can develop a self-optimizing EA.

Subscribers often search for an appropriate signal by analyzing the total growth on the signal provider's account, which is not a bad idea. However, it is also important to analyze potential risks of a particular trading strategy. In this article we will show a simple and efficient way to evaluate a Trading Signal based on its performance values.

The article reveals the portfolio trading principles and their application to Forex market. A few simple mathematical portfolio arrangement models are considered. The article contains examples of practical implementation of the portfolio trading in MetaTrader 4: portfolio indicator and Expert Advisor for semi-automated trading. The elements of trading strategies, as well as their advantages and pitfalls are described.

Analysis of the trade history and plotting distribution charts of trading results in HTML depending on position entry time. The charts are displayed in three sections - by hours, by days of the week and by months.

Scalping automatic systems are rightfully regarded the pinnacle of algorithmic trading, but at the same time their code is the most difficult to write. In this article we will show how to build strategies based on analysis of incoming ticks using the built-in debugging tools and visual testing. Developing rules for entry and exit often require years of manual trading. But with the help of MetaTrader 5, you can quickly test any such strategy on real history.

Profitability of trading systems is defined not only by logic and precision of analyzing the financial instrument dynamics, but also by the quality of the performance algorithm of this logic. False trigger is typical for low quality performance of the main logic of a trading robot. Ways of solving the specified problem are considered in this article.

The article provides the results of testing a simple trading strategy in three modes: "1 minute OHLC", "Every tick" and "Every tick based on real ticks" using actual historical data.

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.

In the last part of the series of articles about the CStrategy trading engine, we will consider simultaneous operation of multiple trading algorithms, will learn to load strategies from XML files, and will present a simple panel for selecting Expert Advisors from a single executable module, and managing their trading modes.

This article explores the effectiveness of complex trading systems by analyzing the efficiency of its individual components. Any analysis, whether it is graphic, based on indicators, or any other, is one of the key components of successful trading in financial markets. This article is to some extent a research of few simple and independent trading systems for analyzing their effectiveness and usefulness of the joint application.

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.

In this article, we will continue analyzing the algorithms of the CStrategy trading engine. The third part of the series contains the detailed analysis of examples of how to develop specific trading strategies using this approach. Special attention is paid to auxiliary algorithms — Expert Advisor logging system and data access using a conventional indexer (Close[1], Open[0] etc.)

In order to expand possibilities of retail Forex traders, we have added the second accounting system — hedging. Now, it is possible to have multiple positions per symbol, including oppositely directed ones. This paves the way to implementing trading strategies based on the so-called "locking" — if the price moves against a trader, they can open a position in the opposite direction.