Algorithmic Trading Articles for Trading systems

Exploring Advanced Machine Learning Techniques on the Darvas Box Breakout Strategy

The Darvas Box Breakout Strategy, created by Nicolas Darvas, is a technical trading approach that spots potential buy signals when a stock’s price rises above a set "box" range, suggesting strong upward momentum. In this article, we will apply this strategy concept as an example to explore three advanced machine learning techniques. These include using a machine learning model to generate signals rather than to filter trades, employing continuous signals rather than discrete ones, and using models trained on different timeframes to confirm trades.

Neural Networks in Trading: A Complex Trajectory Prediction Method (Traj-LLM)

In this article, I would like to introduce you to an interesting trajectory prediction method developed to solve problems in the field of autonomous vehicle movements. The authors of the method combined the best elements of various architectural solutions.

Neural Networks in Trading: State Space Models

A large number of the models we have reviewed so far are based on the Transformer architecture. However, they may be inefficient when dealing with long sequences. And in this article, we will get acquainted with an alternative direction of time series forecasting based on state space models.

Price Action Analysis Toolkit Development (Part 17): TrendLoom EA Tool

As a price action observer and trader, I've noticed that when a trend is confirmed by multiple timeframes, it usually continues in that direction. What may vary is how long the trend lasts, and this depends on the type of trader you are, whether you hold positions for the long term or engage in scalping. The timeframes you choose for confirmation play a crucial role. Check out this article for a quick, automated system that helps you analyze the overall trend across different timeframes with just a button click or regular updates.

MQL5 Wizard Techniques you should know (Part 35): Support Vector Regression

Support Vector Regression is an idealistic way of finding a function or ‘hyper-plane’ that best describes the relationship between two sets of data. We attempt to exploit this in time series forecasting within custom classes of the MQL5 wizard.

Build Self Optimizing Expert Advisors With MQL5 And Python

In this article, we will discuss how we can build Expert Advisors capable of autonomously selecting and changing trading strategies based on prevailing market conditions. We will learn about Markov Chains and how they can be helpful to us as algorithmic traders.

Multiple Symbol Analysis With Python And MQL5 (Part 3): Triangular Exchange Rates

Traders often face drawdowns from false signals, while waiting for confirmation can lead to missed opportunities. This article introduces a triangular trading strategy using Silver’s pricing in Dollars (XAGUSD) and Euros (XAGEUR), along with the EURUSD exchange rate, to filter out noise. By leveraging cross-market relationships, traders can uncover hidden sentiment and refine their entries in real time.

William Gann methods (Part II): Creating Gann Square indicator

We will create an indicator based on the Gann's Square of 9, built by squaring time and price. We will prepare the code and test the indicator in the platform on different time intervals.

The Kalman Filter for Forex Mean-Reversion Strategies

The Kalman filter is a recursive algorithm used in algorithmic trading to estimate the true state of a financial time series by filtering out noise from price movements. It dynamically updates predictions based on new market data, making it valuable for adaptive strategies like mean reversion. This article first introduces the Kalman filter, covering its calculation and implementation. Next, we apply the filter to a classic mean-reversion forex strategy as an example. Finally, we conduct various statistical analyses by comparing the filter with a moving average across different forex pairs.

Neural Networks in Trading: Using Language Models for Time Series Forecasting

We continue to study time series forecasting models. In this article, we get acquainted with a complex algorithm built on the use of a pre-trained language model.

Trading with the MQL5 Economic Calendar (Part 6): Automating Trade Entry with News Event Analysis and Countdown Timers

In this article, we implement automated trade entry using the MQL5 Economic Calendar by applying user-defined filters and time offsets to identify qualifying news events. We compare forecast and previous values to determine whether to open a BUY or SELL trade. Dynamic countdown timers display the remaining time until news release and reset automatically after a trade.

Automating Trading Strategies in MQL5 (Part 9): Building an Expert Advisor for the Asian Breakout Strategy

In this article, we build an Expert Advisor in MQL5 for the Asian Breakout Strategy by calculating the session's high and low and applying trend filtering with a moving average. We implement dynamic object styling, user-defined time inputs, and robust risk management. Finally, we demonstrate backtesting and optimization techniques to refine the program.

Automating Trading Strategies in MQL5 (Part 8): Building an Expert Advisor with Butterfly Harmonic Patterns

In this article, we build an MQL5 Expert Advisor to detect Butterfly harmonic patterns. We identify pivot points and validate Fibonacci levels to confirm the pattern. We then visualize the pattern on the chart and automatically execute trades when confirmed.

Neural Networks in Trading: Injection of Global Information into Independent Channels (InjectTST)

Most modern multimodal time series forecasting methods use the independent channels approach. This ignores the natural dependence of different channels of the same time series. Smart use of two approaches (independent and mixed channels) is the key to improving the performance of the models.

Neural Networks in Trading: Practical Results of the TEMPO Method

We continue our acquaintance with the TEMPO method. In this article we will evaluate the actual effectiveness of the proposed approaches on real historical data.

William Gann methods (Part I): Creating Gann Angles indicator

What is the essence of Gann Theory? How are Gann angles constructed? We will create Gann Angles indicator for MetaTrader 5.

Automating Trading Strategies in MQL5 (Part 7): Building a Grid Trading EA with Dynamic Lot Scaling

In this article, we build a grid trading expert advisor in MQL5 that uses dynamic lot scaling. We cover the strategy design, code implementation, and backtesting process. Finally, we share key insights and best practices for optimizing the automated trading system.

Price Action Analysis Toolkit Development (Part 13): RSI Sentinel Tool

Price action can be effectively analyzed by identifying divergences, with technical indicators such as the RSI providing crucial confirmation signals. In the article below, we explain how automated RSI divergence analysis can identify trend continuations and reversals, thereby offering valuable insights into market sentiment.

Developing a Replay System (Part 59): A New Future

Having a proper understanding of different ideas allows us to do more with less effort. In this article, we'll look at why it's necessary to configure a template before the service can interact with the chart. Also, what if we improve the mouse pointer so we can do more things with it?

Custom Indicator: Plotting Partial Entry, Exit and Reversal Deals for Netting Accounts

In this article, we will look at a non-standard way of creating an indicator in MQL5. Instead of focusing on a trend or chart pattern, our goal will be to manage our own positions, including partial entries and exits. We will make extensive use of dynamic matrices and some trading functions related to trade history and open positions to indicate on the chart where these trades were made.

Building a Keltner Channel Indicator with Custom Canvas Graphics in MQL5

In this article, we build a Keltner Channel indicator with custom canvas graphics in MQL5. We detail the integration of moving averages, ATR calculations, and enhanced chart visualization. We also cover backtesting to evaluate the indicator’s performance for practical trading insights.

Developing a Replay System (Part 57): Understanding a Test Service

One point to note: although the service code is not included in this article and will only be provided in the next one, I'll explain it since we'll be using that same code as a springboard for what we're actually developing. So, be attentive and patient. Wait for the next article, because every day everything becomes more interesting.

Cascade Order Trading Strategy Based on EMA Crossovers for MetaTrader 5

The article guides in demonstrating an automated algorithm based on EMA Crossovers for MetaTrader 5. Detailed information on all aspects of demonstrating an Expert Advisor in MQL5 and testing it in MetaTrader 5 - from analyzing price range behaviors to risk management.

Neural Networks in Trading: Lightweight Models for Time Series Forecasting

Lightweight time series forecasting models achieve high performance using a minimum number of parameters. This, in turn, reduces the consumption of computing resources and speeds up decision-making. Despite being lightweight, such models achieve forecast quality comparable to more complex ones.

Automating Trading Strategies in MQL5 (Part 6): Mastering Order Block Detection for Smart Money Trading

In this article, we automate order block detection in MQL5 using pure price action analysis. We define order blocks, implement their detection, and integrate automated trade execution. Finally, we backtest the strategy to evaluate its performance.

How to Integrate Smart Money Concepts (BOS) Coupled with the RSI Indicator into an EA

Smart Money Concept (Break Of Structure) coupled with the RSI Indicator to make informed automated trading decisions based on the market structure.

Developing a Replay System (Part 58): Returning to Work on the Service

After a break in development and improvement of the service used for replay/simulator, we are resuming work on it. Now that we've abandoned the use of resources like terminal globals, we'll have to completely restructure some parts of it. Don't worry, this process will be explained in detail so that everyone can follow the development of our service.

Neural Networks in Trading: Reducing Memory Consumption with Adam-mini Optimization

One of the directions for increasing the efficiency of the model training and convergence process is the improvement of optimization methods. Adam-mini is an adaptive optimization method designed to improve on the basic Adam algorithm.

Neural Networks in Trading: Spatio-Temporal Neural Network (STNN)

In this article we will talk about using space-time transformations to effectively predict upcoming price movement. To improve the numerical prediction accuracy in STNN, a continuous attention mechanism is proposed that allows the model to better consider important aspects of the data.

We provide a special installer for the MetaTrader 5 trading platform on macOS. It is a full-fledged wizard that allows you to install the application natively. The installer performs all the required steps: it identifies your system, downloads and installs the latest Wine version, configures it, and then installs MetaTrader within it. All steps are completed in the automated mode, and you can start using the platform immediately after installation.

Chaos theory in trading (Part 2): Diving deeper

We continue our dive into chaos theory in financial markets. This time I will consider its applicability to the analysis of currencies and other assets.

Automating Trading Strategies in MQL5 (Part 5): Developing the Adaptive Crossover RSI Trading Suite Strategy

In this article, we develop the Adaptive Crossover RSI Trading Suite System, which uses 14- and 50-period moving average crossovers for signals, confirmed by a 14-period RSI filter. The system includes a trading day filter, signal arrows with annotations, and a real-time dashboard for monitoring. This approach ensures precision and adaptability in automated trading.

Neural Networks Made Easy (Part 97): Training Models With MSFformer

When exploring various model architecture designs, we often devote insufficient attention to the process of model training. In this article, I aim to address this gap.

Developing a multi-currency Expert Advisor (Part 16): Impact of different quote histories on test results

The EA under development is expected to show good results when trading with different brokers. But for now we have been using quotes from a MetaQuotes demo account to perform tests. Let's see if our EA is ready to work on a trading account with different quotes compared to those used during testing and optimization.

Automating Trading Strategies in MQL5 (Part 4): Building a Multi-Level Zone Recovery System

In this article, we develop a Multi-Level Zone Recovery System in MQL5 that utilizes RSI to generate trading signals. Each signal instance is dynamically added to an array structure, allowing the system to manage multiple signals simultaneously within the Zone Recovery logic. Through this approach, we demonstrate how to handle complex trade management scenarios effectively while maintaining a scalable and robust code design.

The Inverse Fair Value Gap Trading Strategy

An inverse fair value gap(IFVG) occurs when price returns to a previously identified fair value gap and, instead of showing the expected supportive or resistive reaction, fails to respect it. This failure can signal a potential shift in market direction and offer a contrarian trading edge. In this article, I'm going to introduce my self-developed approach to quantifying and utilizing inverse fair value gap as a strategy for MetaTrader 5 expert advisors.

Data Science and Machine Learning — Neural Network (Part 01): Feed Forward Neural Network demystified

Many people love them but a few understand the whole operations behind Neural Networks. In this article I will try to explain everything that goes behind closed doors of a feed-forward multi-layer perception in plain English.

Price Action Analysis Toolkit Development (Part 9): External Flow

This article explores a new dimension of analysis using external libraries specifically designed for advanced analytics. These libraries, like pandas, provide powerful tools for processing and interpreting complex data, enabling traders to gain more profound insights into market dynamics. By integrating such technologies, we can bridge the gap between raw data and actionable strategies. Join us as we lay the foundation for this innovative approach and unlock the potential of combining technology with trading expertise.

MQL5 Trading Toolkit (Part 7): Expanding the History Management EX5 Library with the Last Canceled Pending Order Functions

Learn how to complete the creation of the final module in the History Manager EX5 library, focusing on the functions responsible for handling the most recently canceled pending order. This will provide you with the tools to efficiently retrieve and store key details related to canceled pending orders with MQL5.

Developing a Calendar-Based News Event Breakout Expert Advisor in MQL5

Volatility tends to peak around high-impact news events, creating significant breakout opportunities. In this article, we will outline the implementation process of a calendar-based breakout strategy. We'll cover everything from creating a class to interpret and store calendar data, developing realistic backtests using this data, and finally, implementing execution code for live trading.