Working with Excel (tools, indicators, conversion ...) - page 30

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@MAXON #: Welcome I need someone to help me to modify the script to be an indicator that reads MetaTrader 4 indicators instantly in an Excel file.

... you also have the option to hire a programmer in the Freelance section — (text from my previous post in answering yours)

Trading applications for MetaTrader 5 to order
Trading applications for MetaTrader 5 to order
  • 2023.11.13
  • www.mql5.com
The largest freelance service with MQL5 application developers
 

Market Simulation (Part 06): Transferring Information from MetaTrader 5 to Excel

Market Simulation (Part 06): Transferring Information from MetaTrader 5 to Excel

Many people, especially non=programmers, find it very difficult to transfer information between MetaTrader 5 and other programs. One such program is Excel. Many use Excel as a way to manage and maintain their risk control. It is an excellent program and easy to learn, even for those who are not VBA programmers. However, transferring information between MetaTrader 5 and Excel is not one of the simplest tasks, especially if you have no programming knowledge.
Market Simulation (Part 06): Transferring Information from MetaTrader 5 to Excel
Market Simulation (Part 06): Transferring Information from MetaTrader 5 to Excel
  • 2025.11.18
  • www.mql5.com
Many people, especially non=programmers, find it very difficult to transfer information between MetaTrader 5 and other programs. One such program is Excel. Many use Excel as a way to manage and maintain their risk control. It is an excellent program and easy to learn, even for those who are not VBA programmers. Here we will look at how to establish a connection between MetaTrader 5 and Excel (a very simple method).
 

CSV Data Analysis (Part 6): Multi-Broker Result Normalization and Cross-Platform CSV Reconciliation

CSV Data Analysis (Part 6): Multi-Broker Result Normalization and Cross-Platform CSV Reconciliation

The preceding articles in this series built an export and analysis pipeline based on a single, stable data contract: one EA, one broker, one schema. In practice, advanced strategy development rarely confines itself to that boundary. A strategy under evaluation may run simultaneously on a live account at one broker and a demo account at another for execution quality comparison. An optimization campaign may sweep the same parameters across two broker environments to detect broker‑dependent results. A portfolio of strategies may be distributed across multiple brokers for counterparty diversification, with each account contributing a separate CSV export to a consolidated reporting layer.
CSV Data Analysis (Part 6): Multi-Broker Result Normalization and Cross-Platform CSV Reconciliation
CSV Data Analysis (Part 6): Multi-Broker Result Normalization and Cross-Platform CSV Reconciliation
  • 2026.07.07
  • www.mql5.com
This article presents a multi‑broker CSV normalization framework. An MQL5 include file enriches exports with broker metadata. A Python module resolves schema divergences — pip conventions, symbol aliases, time offsets, commission models, and currency denomination — producing a unified canonical dataset. Comparative visualizations of slippage distributions and net‑of‑cost performance enable reliable cross‑platform strategy analysis without silent data corruption.
 

CSV Data Analysis (Part 8): Building an SQLite Strategy Registry from Accumulated CSV Exports

CSV Data Analysis (Part 8): Building an SQLite Strategy Registry from Accumulated CSV Exports

The CSV-based pipeline built across Parts 1 through 7 is well-suited to the natural rhythm of a single research campaign: run an optimization sweep, export a result file, process it with Python, draw conclusions, and move on. For that workflow, flat files are not just adequate — they are optimal. They require no infrastructure, open in any text editor, and load into pandas with a single function call.
CSV Data Analysis (Part 8): Building an SQLite Strategy Registry from Accumulated CSV Exports
CSV Data Analysis (Part 8): Building an SQLite Strategy Registry from Accumulated CSV Exports
  • 2026.08.18
  • www.mql5.com
Flat files work well at the start of an MQL5 research pipeline, but they hinder cross-run queries and provenance once the archive grows. We build a Python-based SQLite registry that ingests CSV exports with SHA-1 deduplication, records EA version and run timestamps, applies forward-only schema migrations, and indexes common filters. You get a structured query layer for fast lookups, robustness checks, and version comparisons across all campaigns.
 

If anyone wants to extract a profit per hour report by DEAL_ENTRY_IN from a completed backtest to Excel (.xlsx), see the following MQL5 Service:

Profit By Deal-Entry-In Per Hour Report for MT5 (unlike deal-entry-out in the native Tester Report)
Profit By Deal-Entry-In Per Hour Report for MT5 (unlike deal-entry-out in the native Tester Report)
  • 2026.08.12
  • www.mql5.com
As the MT5 Tester does not natively support reporting hourly profit and loss by deal-entry-in (only deal-entry-out), I created this MQL5 Service to to generate custom backtest reports limited to, and
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