Discussing the article: "Trading with the MQL5 Economic Calendar (Part 7): Preparing for Strategy Testing with Resource-Based News Event Analysis"
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Check out the new article: Trading with the MQL5 Economic Calendar (Part 7): Preparing for Strategy Testing with Resource-Based News Event Analysis.
In this article, we prepare our MQL5 trading system for strategy testing by embedding economic calendar data as a resource for non-live analysis. We implement event loading and filtering for time, currency, and impact, then validate it in the Strategy Tester. This enables effective backtesting of news-driven strategies.
tatic data integration is essential for those aiming to develop and test robust strategies, particularly in environments like MQL5, where historical economic event data isn’t retained for long periods. Unlike live trading, where the platform can pull real-time news feeds, the Strategy Tester lacks access to such dynamic updates. It doesn’t store extensive archives of past events, leaving us without a native solution for backtesting news-driven approaches. By downloading this data from external sources and organizing it ourselves—whether as files, databases, or embedded resources—we gain control over a consistent dataset that can be reused across multiple tests, ensuring our strategies face the same conditions each time.
Beyond overcoming platform limitations, static data integration will offer flexibility that live feeds cannot. The Economic calendar, as we did see already in the prior versions, often includes critical details like event dates, times, currencies, and impact levels, but these aren’t always preserved in a format suited for algorithmic analysis over long timeframes. By structuring this information manually, we can tailor it to our needs—filtering for specific currencies or high-impact events, for example—allowing for deeper insights into how news influences market behavior without relying on real-time availability.
Additionally, this approach will enhance efficiency and independence. Gathering and storing static data upfront means we’re not tethered to internet connectivity or third-party services during testing, reducing variables that could skew results. It also empowers us to simulate rare or specific scenarios—like major economic announcements—by curating datasets that span years or focus on key moments, something live systems or limited platform storage can’t easily replicate. Ultimately, static data integration bridges the gap between live trading insights and backtesting precision, laying a solid foundation for strategy development.
Data storage will be a key consideration, and MQL5 provides a wide range of variability, from Text (txt) formats, Comma Separated Values (CSV), American National Standards Institute (ANSI), Binary (bin), Unicode and also database organizations as below.
We will use not the easiest but the most convenient format, which is CSV format. That way, we will have the data with us and will not be required we to wait for hours to backtest our strategy, saving us lots of time and energy. Let's go.
Author: Allan Munene Mutiiria