Neural Networks in Trading: Piecewise Linear Representation of Time Series
Developing a Calendar-Based News Event Breakout Expert Advisor in MQL5
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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 in MQL5. 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.
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Introduction to MQL5 (Part 12): A Beginner's Guide to Building Custom Indicators
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Using a project-based approach, we will divide the process into two main parts. First, without utilizing the iMA function, we will build a Moving Average indicator entirely from scratch. Next, we'll go one step further and transform the Moving Average from the conventional line shape into a candle-style indication. In addition, this practical method will open up new avenues for developing trading tools that are specifically suited to your requirements.
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From Basic to Intermediate: Arrays and Strings (I)
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In the previous article, "
From Basic to Intermediate: Operator Precedence", we talked a little about the precautions we should take when using factorization in our codes. It's not uncommon to come across code that appears correct at first glance but ends up producing completely unexpected results in certain situations. This type of issue is often directly related to how the factorizations are implemented. Getting a piece of code to deliver consistent results may seem like a trivial task. However, without the proper knowledge (as demonstrated in that article), the likelihood of catastrophic failures increases as more and more poorly implemented factorizations are introduced into the code.
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Neural Networks in Trading: Controlled Segmentation
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The proposed Object Cluster Module plays a crucial role in enabling the model to achieve a deeper, more holistic understanding of both linguistic and visual information.
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Price Action Analysis Toolkit Development (Part 27): Liquidity Sweep With MA Filter Tool
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In this article, we will develop an MQL5 Expert Advisor designed to identify liquidity sweeps as they unfold. The EA begins by analyzing candles that break below or above previous swing points, then close back within the range, indicators of potential liquidity absorption. It incorporates optional filters, such as candlestick color changes or moving average confirmations, to ensure the signals align with your market bias. When a valid pattern is detected, the EA visually marks it on the chart with arrows or labels and generates an alert.
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Installing MetaTrader 5 and Other MetaQuotes Apps on HarmonyOS NEXT
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Huawei users running HarmonyOS NEXT can now easily install and use MetaTrader 5, MetaTrader 4, and other MetaQuotes applications. This is possible thanks to DroiTong, a compatible tool available in the Huawei AppGallery that enables you to run Android apps on HarmonyOS NEXT. This guide will walk you through the straightforward process of getting these essential applications up and running on your device.
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MQL5 Wizard Techniques you should know (Part 78): Using Gator Oscillator and the Accumulation/Distribution Oscillator
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In our last article we introduced 5 signal patterns from the pairing of the Gator and the Accumulation/Distribution oscillators, in this article we consider the final 5 of this set of 10. We have been looking at 10 signal patterns for each indicator pairing, and we will be maintaining this format. From the testing in the last article which was done on the pair GBP JPY on the 30-minute timeframe, our results indicated that the patterns 0, 3, and 4 struggled to forward walk, however before we can choose what to improve with supervised learning, let's complete examining and testing of patterns 5 to 9.
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Statistical Arbitrage Through Cointegrated Stocks (Part 3): Database Setup
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In
the previous article of this series (Part 2), we backtested a statistical arbitrage strategy composed of a basket of cointegrated stocks from the microprocessor sector (Nasdaq stocks). We started filtering among hundreds of stock symbols for those most correlated with Nvidia. Then we tested the filtered group for cointegration using the Johansen test, the stationarity of the spread using ADF and KPSS tests, and finally, we obtained the relative portfolio weights by extracting the Johansen eigenvector for the first rank. The backtest results were promising.
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