Creating a Trading Administrator Panel in MQL5 (Part IV): Login Security Layer
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Security is paramount in any field of expertise, and we cannot afford to overlook its importance. With the persistent threat of unauthorized access, it’s crucial to safeguard our Admin Panel from potential intruders. If unauthorized individuals gain access, they could easily manipulate the panel and jeopardize our communication efforts with the broadcast community. The primary purpose of this system is to facilitate reliable communication, and while we can enhance functionality at the Expert Advisor level, the risks of intrusion remain significant.
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Neural Networks Made Easy (Part 90): Frequency Interpolation of Time Series (FITS)
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In the previous articles, we discussed the
FEDformer method that uses the frequency domain to find patterns in a time series. However, the
Transformer used in that method can hardly be referred to as a lightweight model. Instead of complex models that require large computational costs, the paper "
FITS: Modeling Time Series with 10k Parameters" proposes a method for the frequency interpolation of time series (
Frequency Interpolation Time Series - FITS). It is a compact and efficient solution for time series analysis and forecasting.
FITS uses frequency domain interpolation to expand the window of the analyzed time segment, thus enabling the efficient extraction of temporal features without significant computational overhead.
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Building A Candlestick Trend Constraint Model (Part 9): Multiple Strategies Expert Advisor (II)
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In the 20th century, Richard Donchian established a trend-following strategy through his studies of financial markets, which later evolved into the Donchian Channels. We briefly discussed his work in a previous article, but today we will focus on implementing the strategies associated with his theory. According to various sources, the channels are believed to encompass multiple strategies within their framework. The abundance of literature on Donchian Channels suggests the continued effectiveness of this theory in modern-day trading. By integrating Donchian Channel strategies, we aim to expand the opportunities for our Trend Constraint Expert, enhancing both its profitability and adaptability to diverse market conditions.
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Price Action Analysis Toolkit Development (Part 1): Chart Projector
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In this article, titled "The Chart Projector," we focus on developing a tool designed to simplify market analysis, particularly for price action traders. The Chart Projector overlays the previous day's chart onto the current one in a ghost mode representation.
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Data Science and ML (Part 32): Keeping your AI models updated, Online Learning
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Online machine learning is a machine learning method in which the model incrementally learns from a stream of data points in real time. It’s a dynamic process that adapts its predictive algorithm over time, allowing the model to change as new data arrives. This method is incredibly significant in rapidly evolving data-rich environments such as in trading data as it can provide timely and accurate predictions.
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Price Action Analysis Toolkit Development (Part 3): Analytics Master — EA
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Having developed the analytic comment script in the previous episode, I have identified its limitations regarding script properties, which I will outline below. While I acknowledge the considerable impact that the analytical comment script has had within the trading community, I have developed an Expert Advisor (EA) called Analytics Master. This EA offers the same analysis while also providing extra metrics for better trading insights. This new tool offers several advantages, including the ability to continuously update metrics every two hours, in contrast to the script, which could only write metric information once. The EA enhances analysis and keeps users updated.
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Introduction to MQL5 (Part 10): A Beginner's Guide to Working with Built-in Indicators in MQL5
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We will be developing a Relative Strength Index (RSI) EA in this article. One of the most used technical indicators in trading is the RSI. We'll build a tool that tracks market conditions and makes trades automatically by including this indicator in our EA. Although the Relative Strength Index (RSI) is the subject of this article, the ideas we'll discuss apply to most built-in indicators because they all operate on similar principles. Since this series is intended mostly for beginners, my main goal will be to keep the explanations and code as straightforward as possible. I know that for a novice, it is crucial to fully comprehend every stage of the process, including why certain code is written, what each component performs, and how the various parts work together.
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Price Action Analysis Toolkit Development Part(4): Analytics Forecaster EA
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In this article, we will focus on integrating our broadcast with Telegram to ensure wider access to analyzed information. I chose Telegram for this purpose due to its popularity and accessible interface among traders. Integrating MetaTrader 5 chart updates with Telegram offers significant advantages for active traders. This setup provides immediate access to crucial market information, enhances the user experience, and improves communication. Consequently, you can develop more effective strategies and respond swiftly in a rapidly changing market. With this integration, you can increase your chances of success and make more informed, timely decisions.
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How to build and optimize a volume-based trading system (Chaikin Money Flow - CMF)
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Welcome to a new article where we explore new indicators in terms of creating them, building trading systems based on their concept, and optimizing these systems to get better insights and results regarding profits and risks. In this article, we will introduce a new volume-based technical indicator called the Chaikin Money Flow (CMF) indicator.
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Neural Networks Made Easy (Part 97): Training Models With MSFformer
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In the previous article we built the main modules of the
MSFformer model, including
CSCM and
Skip-PAM. The
CSCM module constructs a feature tree of the analyzed time series, while
Skip-PAM extracts information from time series at multiple scales using attention mechanism based on a temporal feature tree. In this article, we will continue that work by training the model and evaluating its performance on real-world data using the MetaTrader 5 Strategy Tester.
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