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January 23, 2026

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Constructing an Exterior Information Filter: Find out how to Cease Your EA Earlier than CPI Hits

There’s a recurring nightmare for each Algo-Dealer. You backtest a trend-following technique. The fairness curve appears like a straight line to the moon. You launch it on a Dwell Account.

For 3 weeks, it prints cash. Then, on a random Friday at 8:30 AM New York time (NFP launch), the market whipsaws 100 pips in 3 seconds.

Your EA, oblivious to the information, tries to purchase the breakout. The unfold widens to twenty pips. You get stuffed on the high. The value crashes. Account blown.

MetaTrader 5 is a beast for execution, however it is not nice at parsing complicated internet knowledge like Financial Calendars. Python, nonetheless, is ideal for it.

On this tutorial, I’ll present you the structure of a “Hybrid Information Filter”: A Python script that scrapes calendar knowledge and “talks” to your MQL5 EA to pause buying and selling throughout high-impact occasions.

The Structure: The “Satellite tv for pc” Strategy

We do not need to burden the MT5 terminal with heavy HTML parsing. As a substitute, we use a Python script working within the background as a “Satellite tv for pc.”

Python Script: Scrapes Foreign exchange Manufacturing unit (or makes use of an API) to seek out Excessive Affect Information (Purple Folders).
The Bridge: Python writes a easy textual content file ( information.txt ) into the MT5 “Frequent” folder.
MQL5 EA: Reads this file each minute. If a information occasion is imminent, it units a world variable TradingAllowed = false .

Step 1: The Python Scraper (The “Eyes”)

We’d like a script that checks the calendar and calculates the “Minutes Till Affect.” Here’s a simplified model of the logic utilizing pandas .

# PYTHON LOGIC import pandas as pd from datetime import datetime, timedelta “` def check_news():
# 1. Fetch Calendar Knowledge (Pseudo-code)
df = get_economic_calendar()

# 2. Filter for Excessive Affect (Purple Folder) & USD/EUR pairs
high_impact = df[(df[‘impact’] == ‘Excessive’) & (df[‘currency’].isin([‘USD’,’EUR’]))]

    # 3. Examine time distinction
    now = datetime.utcnow()
    for index, row in high_impact.iterrows():
        time_diff = row[‘date’] – now
        minutes = time_diff.total_seconds() / 60

        # 4. Write “STOP” if information is inside 30 minutes
        if 0 < minutes < 30:
            with open(MT5_COMMON_PATH + “news_signal.txt”, “w”) as f:
                f.write(“STOP”)
            return

    # Else, all clear
    with open(MT5_COMMON_PATH + “news_signal.txt”, “w”) as f:
        f.write(“GO”)
“`