HourlyDD Logger by SQXHK

MetaTrader 5 Expert Advisor Script: Hourly Equity & Drawdown Data Logger

Developer: SQX.hk

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This MQL5 script functions as an automated data-logging Expert Advisor (EA) designed to capture intraday equity metrics directly from MetaTrader 5 and export them into structured CSV files . It records hourly snapshots of account performance, high-water marks, and equity drawdowns in real-time .

Script Architecture & Functionality

• Initialization ( OnInit ): Captures the current account equity and login ID, setting the baseline high-water mark . Generates a unique CSV filename embedded with the account number and precise deployment timestamp .

• Real-Time Data Capture ( OnTick ): Evaluates tick data and triggers exact top-of-the-hour recording ( minute == 0 ) . Automatically updates peak equity (high-water mark) and tracks both current drawdown and maximum peak-to-trough drawdown .

• Thread-Safe File Handling: Utilizes non-blocking file access ( FILE_SHARE_READ | FILE_SHARE_WRITE ) to open, append, and close logs dynamically . Allows external Python scripts, SQL pipelines, or data visualization engines to read the dataset live without disrupting trade execution .

The Power of Data Science in the MT5 EA Market (SQX.hk Methodology)

Most retail traders evaluate Expert Advisors using surface-level metrics (e.g., total profit or terminal drawdowns). At SQX.hk, students learn to move beyond static backtests and harness the true power of Data Science for Quantitative Trading:

• Bridging MT5 to Python/R Pipelines: By automatically converting live broker telemetry into clean CSV files, this script bridges MQL5 execution with external data science toolkits .

• Continuous Intraday Risk Analytics: Static equity charts mask hidden intraday volatility. Continuous hourly drawdown logging equips students to calculate advanced quantitative metrics—such as Hourly Ulcer Index, Time-in-Drawdown Duration, and Intraday Tail-Risk Distributions.

• Robustness & Machine Learning Integration: SQX.hk trains students to leverage raw market logs for data-driven modeling—enabling automated strategy clustering, Monte Carlo stress testing, and real-time risk control models that adapt to changing market regimes.