Name: MQL4/5 Expert Advisor Development
Price: 3999 INR
Availability: InStock

Course Overview

MetaTrader remains the world's most widely used retail trading platform — and MQL5 is its native programming language. An Expert Advisor (EA) is a fully automated trading program that runs directly inside MetaTrader: it reads the market, evaluates your strategy rules, and places, modifies, or closes trades without any manual intervention. Building your own EA is the difference between a trader who depends on someone else's black box and one who owns their edge completely.

This course teaches you to write professional-grade Expert Advisors in MQL5 from the ground up. You will start from the MQL5 language itself — data types, control flow, and the MetaTrader event model — and progressively build toward complete, deployable EAs: one trend-following system, one mean-reversion system, and a capstone EA of your own design. Along the way you will write custom indicators, implement multi-symbol and multi-timeframe logic, build a reusable risk management library, and run rigorous backtests and optimisations in the Strategy Tester.

The course covers both MQL5 (MetaTrader 5) and essential MQL4 concepts for traders who work with MT4 brokers. By the end, you will understand the differences between the two environments and be able to adapt code between them. Everything is written in Windows MetaEditor — the IDE included with MetaTrader — and all code is tested against real broker demo accounts.

This is a coding course. You will write code in every single session. No trading results are guaranteed — the goal is to give you complete technical mastery of the MetaTrader automation environment so you can build, test, and deploy any rule-based strategy you design.

What You Will Learn

Write syntactically correct MQL5 programs — EAs, custom indicators, and scripts — using MetaEditor

Implement any rule-based trading strategy in code: entry conditions, exits, trailing stops, and break-even logic

Build reusable risk management functions: fixed lot, percentage-of-equity, and ATR-based position sizing

Code custom indicators in MQL5 and call them from within your EA for cleaner, modular architecture

Run and interpret backtests and optimisations in MetaTrader's Strategy Tester with realistic spread and slippage

Implement multi-timeframe logic: use a higher-timeframe filter to validate signals on the entry timeframe

Deploy a working EA to a live broker demo account on a Windows VPS for 24/5 automated execution

Debug common EA failures — re-quotes, off-quotes, context busy errors, and drawdown emergencies

Who This Course Is For

Forex and CFD traders on MetaTrader who want to automate their existing manual strategies into Expert Advisors
Python algorithmic traders who want to add MetaTrader EA skills to their toolkit — MQL5 syntax will feel familiar
Traders who have purchased EAs online and want to understand, audit, and modify the code themselves rather than operating blindly
Developers or programmers who trade and want to move into financial software development specifically for the MetaTrader ecosystem
Serious manual traders who want to remove emotional execution errors by automating their rule-based setups

Prerequisites

Prior programming experience in any language is strongly recommended. MQL5 is a C++-style language with strict typing. Students with zero coding background will struggle significantly. You do not need to know C++ specifically, but you should be comfortable with variables, loops, conditions, and functions in at least one language (Python, JavaScript, C, Java, etc.). If you have completed the Algorithmic Trading with Python course, you have more than enough coding foundation for this course.

Basic programming knowledge in any language — variables, loops, if/else, and functions
MetaTrader 5 installed (free download from MetaQuotes or your broker) with a demo account
Understanding of how trading works — chart reading, candlesticks, and basic technical indicators (EMA, RSI, ATR)
Windows PC or a cloud Windows instance (MetaEditor runs on Windows; Mac users need a VM or cloud solution)
No prior MQL knowledge required — the language is taught from scratch in Module 1

Curriculum

6 Modules 36 Lessons 6 weeks duration 3 complete EAs built 1 capstone project

01

MQL5 Language Foundations & MetaTrader Architecture

Week 1 · 7 Lessons

7 lessons

// Your first EA skeleton — Module 1.3
#property copyright "Vianmax Academy"
#property version "1.00"

void OnInit() { // runs once on attach }
void OnDeinit(const int reason) { }
void OnTick() { // runs on every new price tick }

1.1 MetaTrader 5 Architecture — How EAs, Indicators & Scripts Differ

The three MQL5 program types: Expert Advisors (run on every tick, place trades), Custom Indicators (calculate and draw values, cannot trade), and Scripts (run once on demand). How MetaTrader's terminal, charts, and MetaEditor relate. The MQL5 file structure: .mq5 source and .ex5 compiled files. Where EAs are stored and loaded.
1.2 MetaEditor IDE — Setup, Compilation & Debugging

Tour of MetaEditor: navigator, code editor, toolbox, and compiler output panel. Creating, compiling, and attaching your first program to a chart. How to use Print() for console debugging. Setting breakpoints and using the debugger. Common compilation errors and how to read them.
1.3 MQL5 Syntax — Data Types, Variables & Constants

MQL5's strict typing system: int, double, string, bool, datetime, and color. Declaring and initialising variables. MQL5-specific types: MqlRates (OHLCV bar), MqlTick (bid/ask/time), MqlTradeRequest, and MqlTradeResult. Predefined constants: EMPTY_VALUE, NULL, WHOLE_TRADE. Naming conventions and scope.
1.4 Control Flow — Conditions, Loops & Functions

if/else, switch, for loops, while loops, and do-while in MQL5. Writing reusable functions with parameters and return values. Understanding function overloading. Common MQL5 patterns: the "bar close" trigger (only act once per completed candle using a saved time variable), and the session filter.
1.5 The Event Model — OnInit, OnTick, OnBar & OnDeinit

How MetaTrader triggers your EA: OnInit() runs once at start, OnTick() on every price update, OnTimer() on a fixed interval, OnChartEvent() on user input. The crucial difference between tick-based logic and bar-based (OHLCV) logic. Why most strategies should use bar-close confirmation, not raw ticks.
1.6 Accessing Price Data — CopyRates, CopyBuffer & Arrays

CopyRates() for fetching OHLCV bar data into a MqlRates array. CopyClose(), CopyHigh(), CopyLow() for individual series. Working with array indexing in MQL5 — why index [0] is the most recent bar (opposite of most languages). ArraySetAsSeries() — when to use it and why it matters for indicator calculations.
1.7 MQL4 vs. MQL5 — Key Differences for Migration

The major differences: MQL5 uses an object-oriented trade class (CTrade) instead of OrderSend(); arrays are indexed differently; history iteration is different; and MQL5's trade execution model is asynchronous. How to read and understand MQL4 code from online sources, and the mental model for converting it to MQL5.

Key Takeaway: You can write, compile, and run basic MQL5 programs in MetaEditor, understand the event-driven architecture of Expert Advisors, and read price data from any symbol and timeframe.

02

Indicators in MQL5 — Built-in & Custom

Week 1–2 · 6 Lessons

6 lessons

2.1 Calling Built-in Indicators — iMA, iRSI, iATR, iMACD

How to call MetaTrader's built-in indicator functions from inside an EA. iMA() for moving averages, iRSI() for RSI, iATR() for Average True Range, iMACD() for MACD. The handle-based system in MQL5: creating an indicator handle in OnInit() and copying values with CopyBuffer() in OnTick(). Why creating handles once (not per tick) is critical for performance.
2.2 Multi-Timeframe Indicators — Using HTF Data in Your EA

Calling iMA() with a different timeframe parameter (PERIOD_H4 on an M15 chart). The challenge of data synchronisation — why the HTF bar count may differ. Best practices for multi-timeframe indicator calls: requesting the minimum required bars, handling IsSynchronized() checks, and avoiding look-ahead bias.
2.3 Writing Your First Custom Indicator

Creating a custom indicator (.mq5 type: INDICATOR). Declaring indicator buffers with SetIndexBuffer(). The OnCalculate() function — how it differs from OnTick(). Building a simple double EMA ribbon indicator that draws two EMA lines on the chart. Adding input parameters for the user to configure periods and colours.
2.4 Calling a Custom Indicator from an EA

Using iCustom() to load your custom indicator inside an EA and read its buffer values. Advantages of modular architecture: your EA logic stays clean while indicator logic lives in a separate file. How to pass input parameters to iCustom(). Debugging buffer alignment — ensuring the value you read corresponds to the correct bar.
2.5 Coding a Volatility Indicator — ATR Bands

Building an ATR Bands custom indicator: a central EMA with upper and lower bands placed at ±N×ATR. Practical coding exercise: computing the bands, writing them to two separate buffers, and styling them on the chart (dashed line style, custom colour). This indicator will be used inside EA 2 in Module 4.
2.6 Indicator Quality — Repainting, Look-Ahead & Lag

The three cardinal sins of indicator development: repainting (recalculating past bars), look-ahead bias (using future data in historical calculation), and excessive lag. How to test your indicator for repainting. Why indicators that look perfect in the Strategy Tester's visual mode but fail in live trading are almost always repainting. How to write non-repainting code.

Key Takeaway: You can call any built-in MetaTrader indicator from inside an EA, write your own custom indicators with multiple buffers and input parameters, and call them from EAs using a clean modular architecture.

03

EA 1 — Trend-Following EMA Crossover Expert Advisor

Week 2–3 · 7 Lessons

7 lessons

3.1 Trade Execution with CTrade — OrderSend, Modify & Close

MQL5's CTrade class: the recommended way to place, modify, and close trades. trade.Buy() and trade.Sell() with symbol, volume, price, SL, TP, and comment parameters. trade.PositionClose() and trade.PositionModify(). Checking trade.ResultRetcode() for execution errors. Comparison with MQL4's OrderSend() for traders coming from MT4.
3.2 Position Management — Reading Open Positions

Checking whether a position is open: PositionsTotal(), PositionSelect(), PositionGetDouble(POSITION_SL), PositionGetInteger(POSITION_TYPE). Why EAs must check for existing positions before placing new ones — avoiding duplicate entries. Managing multiple positions: filtering by magic number to distinguish your EA's trades from manual trades.
3.3 Strategy Logic — EMA Crossover with HTF Trend Filter

Building the complete signal logic: fast EMA crosses above slow EMA on M15, only when price is above the 200 EMA on H4 (HTF trend filter). The "state machine" approach: storing the previous bar's crossover state and only triggering on the change. Coding the long and short conditions as clean boolean expressions.
3.4 Risk Management Module — ATR Stop-Loss & Fixed-Risk Sizing

Computing the stop-loss distance in pips using iATR() × multiplier. Converting pips to price distance accounting for the instrument's Point and Digits. Computing lot size from a fixed percentage risk (e.g. 1% of account): lot = (AccountEquity × risk%) / (SL_in_pips × pip_value). Writing this as a reusable RiskToLot() function.
3.5 Trailing Stop & Break-Even Logic

Implementing a trailing stop: on every tick, if price has moved N×ATR in profit, move the stop-loss to a new level. The "break-even" trigger: when profit reaches 1R, move SL to entry. Coding both as a position modification function. Why these must use PositionModify() with a valid price, and how to handle "invalid stops" errors from brokers.
3.6 Input Parameters & EA Configuration Panel

Declaring EA inputs with input keyword: fast EMA period, slow EMA period, HTF period, ATR multiplier for SL, risk percentage, magic number, and trading session hours. Best practices for parameter naming (prefix with underscore or use groups). Why exposing too many inputs encourages over-optimisation. Essential inputs vs. fixed internal constants.
3.7 EA 1 Completion — Code Review & First Backtest

Complete code walkthrough of the finished EMA Crossover EA. Running the first backtest in Strategy Tester on EURUSD H1 (2020–2025). Reading the backtest report: net profit, drawdown, profit factor, Sharpe ratio, and trade count. Common first-backtest mistakes: forward-filling errors, tick model selection, and why "Every Tick" mode is essential for correct SL/TP simulation.

Key Takeaway: You have built your first complete, deployable EA — with indicator-based entry signals, a multi-timeframe trend filter, ATR-based risk management, trailing stop, and input parameter configuration.

04

EA 2 — RSI Mean-Reversion Expert Advisor

Week 3–4 · 6 Lessons

6 lessons

4.1 Mean-Reversion Strategy Design in Code

Translating the RSI mean-reversion logic into MQL5: buy when RSI crosses above 30 from below (oversold recovery), sell when RSI crosses below 70 from above (overbought drop). Adding an ADX trend filter: only take mean-reversion trades when ADX is below 25 (ranging market). Encoding the crossover as a state change rather than a level check to avoid repeated triggering.
4.2 Using the ATR Bands Custom Indicator Inside an EA

Calling the ATR Bands indicator built in Module 2.5 via iCustom(). Using the upper/lower bands as dynamic take-profit levels instead of fixed-pip targets. How to ensure the custom indicator .ex5 file is accessible to the EA at runtime. Testing that buffer values align correctly with the chart display.
4.3 Session Filter — Trading Only During Specific Market Hours

Mean-reversion strategies work best in ranging sessions (e.g., Asian session for JPY pairs). Implementing a time filter in MQL5: using TimeGMT(), TimeCurrent(), and MqlDateTime structure to extract current hour and minute. Restricting the EA to enter trades only between a user-defined start and end time. Handling midnight rollover and DST offset complications.
4.4 Max Daily Loss Limit & Drawdown Protection

Implementing a daily loss limit: track today's starting equity in OnInit() and on each OnTick(), if today's equity drawdown exceeds N%, close all positions and block new trades for the rest of the day. Using OrderHistory() to compute today's realised P&L. Why a hard daily loss limit is non-negotiable for any automated system running unsupervised.
4.5 Handling News Events — Economic Calendar Filter

Why automated strategies often blow up during high-impact news events. Two approaches: (1) hard-code a block window around known scheduled events (e.g., US NFP every first Friday); (2) use the MQL5 Calendar API (CalendarValueHistory()) to query upcoming events and auto-pause during high-impact releases. Implementing option 1 as a practical, broker-agnostic solution.
4.6 EA 2 Completion — Backtest on USDJPY & GBPUSD

Running EA 2 in Strategy Tester on USDJPY M15 and GBPUSD M15 independently. Comparing performance across both symbols — do the same parameters work on different pairs? Why mean-reversion EAs are often pair-specific. Walk-forward test: train on 2020–2023, test on 2024–2025. Evaluating whether the edge survives out-of-sample.

Key Takeaway: You have built a second EA with a completely different strategy type — mean-reversion — including a custom indicator integration, session filter, and drawdown protection. Your MQL5 toolkit is now broad enough to implement almost any systematic strategy.

05

Backtesting, Optimisation & Strategy Tester Mastery

Week 4–5 · 5 Lessons

5 lessons

5.1 Strategy Tester Deep Dive — Tick Models & Data Quality

The three tick models: "Every Tick Based on Real Ticks" (most accurate, requires real tick data), "Every Tick Based on 1-Minute OHLC" (good approximation), and "Open Prices Only" (fastest, suitable for bar-close strategies). Why "Every Tick" is required for any EA that uses SL/TP or trailing stops. Downloading historical tick data for your symbols directly from MetaTrader's history centre.
5.2 Reading the Backtest Report — Metrics That Matter

Net profit, profit factor (target: above 1.3), Sharpe ratio, recovery factor, expected payoff, maximum drawdown (absolute and relative), and consecutive losses. Why a high net profit with a 50% drawdown is not a tradeable result. Understanding the equity curve shape — smooth growth vs. single lucky spike. How to save and compare reports across parameter sets.
5.3 Optimisation — Grid Search in the Strategy Tester

Running a full optimisation: setting parameter ranges and steps for fast EMA (5–30 step 5), slow EMA (20–80 step 10), ATR multiplier (1.0–3.0 step 0.5). Choosing the optimisation criterion: balance, profit factor, or custom. Reading the optimisation results table. Sorting by profit factor, not net profit. Identifying robust parameter zones vs. isolated peaks.
5.4 Walk-Forward Testing — Validating the Out-of-Sample Edge

The only meaningful optimisation test: train on 70% of data, test on the remaining 30%, never touching the test set until after parameter selection. Why any result optimised across the full history is circular. MetaTrader 5's built-in Walk-Forward Optimisation tool — how to set it up, interpret the WFE (Walk-Forward Efficiency) ratio, and what constitutes a credible result.
5.5 Custom Optimisation Criteria — Writing Your Own Fitness Function

MQL5 allows you to define a custom fitness function that the optimiser maximises. Writing an OnTester() function: compute your own metric (e.g., profit factor × recovery factor / max_drawdown%) and return it as a double. Why this is powerful: you can force the optimiser to find parameter sets that are balanced across multiple quality dimensions, not just maximum profit.

Key Takeaway: You can run rigorous, realistic backtests and optimisations in the Strategy Tester — and more importantly, you know how to evaluate results honestly and avoid the most common mistakes that make backtests meaningless in live markets.

06

Live Deployment, VPS & Capstone EA

Week 5–6 · 5 Lessons

5 lessons

6.1 Pre-Deployment Checklist — From Backtest to Live Demo

The checklist before attaching any EA to a live account: (1) Verified walk-forward results. (2) Magic number set correctly. (3) Max lot size limit in code (failsafe). (4) Daily loss limit active. (5) News filter configured. (6) Tested on demo for a minimum of 30 trades. How to run the EA on a demo account for 2–4 weeks as a "forward test" before risking real capital. Warning signs to watch for in forward testing.
6.2 VPS Setup — Running Your EA 24/5 Without Your PC

Why a VPS (Virtual Private Server running Windows) is essential for any live EA — your home PC cannot be on 24/5. Setting up a low-latency Windows VPS (Contabo, Vultr, or MetaQuotes hosting). Connecting via Remote Desktop. Installing MetaTrader 5 on the VPS, copying your EA .ex5 file, attaching it to the correct chart, and verifying it is live. VPS latency: why server location relative to your broker matters.
6.3 Monitoring & Incident Response

How to monitor a live EA: checking the Experts tab for error logs, watching position P&L daily, and setting up MT5's email/push notification alerts for trade events. Common live trading errors: "trade context is busy" (another EA or manual trade is executing), "invalid volume" (lot size below broker minimum), and "off quotes" (requote during high volatility). Response procedures for each. When to disable an EA and review.
6.4 Capstone — Build Your Own EA

The capstone project: students design and build an EA based on a strategy of their own choice. Requirements: at least one indicator signal, a risk management module with percentage-based sizing, a session or time filter, and a daily loss limit. Submit source code (.mq5) and a Strategy Tester report on a chosen symbol and period. Instructor code review and written feedback provided for every submission.
6.5 MQL5 Resources & Continuing Development

The MQL5 ecosystem beyond this course: the MQL5.com community, Code Base (free EAs and indicators), Market (paid tools), and Signals (copy trading). How to use the MQL5 documentation effectively. Recommended next steps: building an EA with Order Blocks logic, implementing a Grid EA with position averaging (with caveats on risk), and exploring the MQL5 OOP model with classes for advanced architecture.

Key Takeaway: You have deployed an EA on a live demo account running on a VPS, built and submitted your own capstone EA with a custom strategy, and have a complete operational framework for monitoring and maintaining automated MetaTrader systems.

AlphaSync

Included with this Course

Practice Live on AlphaSync

While this course focuses on MetaTrader and MQL5, AlphaSync runs the same strategy logic on live Indian NSE/BSE data. Use it to validate your EA's logic against Indian market conditions, run parallel backtests, and compare performance across both ecosystems.

Module 1–2

Parallel Strategy Design

Take the same EMA crossover logic you're coding in MQL5 and build it in AlphaSync's no-code strategy builder. Compare how the same rules behave on EURUSD (MT5) vs. Nifty 50 (AlphaSync).

Open AlphaSync

Module 3–4

Cross-Platform Backtest Comparison

Run your RSI Mean-Reversion EA through MT5's Strategy Tester. Then replicate the same setup in AlphaSync's backtester on Nifty 50. Compare the win rate, drawdown, and expectancy across both platforms.

Open AlphaSync

Module 5

NSE Paper Trading Validation

Deploy your EA's entry/exit logic manually on AlphaSync paper trading for one week. This removes the MT5 execution layer — if the raw signals work here, your EA logic is sound. Document any mismatches.

Open AlphaSync

Module 6 — Capstone

Multi-Platform Performance Report

Compare your final capstone EA's MT5 backtest results with AlphaSync paper trading results for the same strategy logic. Write a one-page performance comparison and present it to the batch as part of your graduation.

Open AlphaSync

Learning Format

Recorded Sessions

HD walkthroughs of every coding lesson — code along at your own pace, rewind as needed.

Code-Along Labs

Every session has a live coding exercise. Students write code from scratch rather than copy-paste.

3 Complete EAs

Two structured EAs (trend-following + mean-reversion) plus a capstone EA of your own design.

Full Source Code

Complete .mq5 source for all three EAs and custom indicators, provided on Day 1 for reference.

WhatsApp Group

Batch group for code troubleshooting, error debugging, and peer discussion throughout the course.

Instructor Code Review

Personal code review and written feedback on your capstone EA submission from the instructor.

Expert Advisors You Will Build

1

EA 1 — EMA Crossover Trend-Following Expert Advisor

A complete trend-following EA with a fast/slow EMA crossover signal on M15, a 200 EMA multi-timeframe trend filter on H4, ATR-based stop-loss, percentage-of-equity position sizing, and a trailing stop with break-even trigger. Backtested on EURUSD over 2020–2025 using real tick data in the Strategy Tester with a full performance report submitted.

2

EA 2 — RSI Mean-Reversion Expert Advisor with Custom Indicator

A mean-reversion EA using RSI oversold/overbought crossovers filtered by ADX, with dynamic take-profit targets from a custom ATR Bands indicator built in Module 2, a session time filter for the Asian trading window, a news event block, and a daily loss limit. Backtested on USDJPY M15 with a walk-forward test covering 2020–2023 training and 2024–2025 out-of-sample validation.

3

Capstone EA — Your Own Strategy

Students design and build an EA based on a strategy of their own choice. Requirements include at least one indicator-based signal, a risk management module with ATR or percentage-based position sizing, a session or market condition filter, and a daily loss limit. Submit .mq5 source code plus a Strategy Tester report. Receives written instructor code review and feedback — this EA is designed to be portfolio-worthy and deployable to a demo account.

Course Materials

EA 1 complete .mq5 source code

EA 2 complete .mq5 source code

ATR Bands custom indicator .mq5

RiskToLot() reusable function library

MQL5 syntax quick-reference cheatsheet

CTrade class methods reference card

Strategy Tester metrics interpretation guide

VPS setup & MT5 deployment walkthrough

Pre-deployment checklist (printable)

MQL4 → MQL5 migration reference guide

Time Commitment

6

Weeks total duration

8–10h

Per week (study + coding)

3

Complete EAs produced

MQL5 is a compiled language with strict syntax — errors that Python forgives will crash your EA compilation. Budget extra time in Weeks 1 and 2 while you adapt to the compiler's error messages. Students with strong Python or JavaScript backgrounds typically find the transition smooth by Week 2; students with no prior coding background often need to supplement with basic programming resources before keeping pace.

The heaviest coding weeks are 2, 3, and 4 (building both EAs). The capstone (Week 5–6) requires independent work with minimal instructor scaffolding by design — the goal is to verify that you can build an EA end-to-end without being guided step-by-step. Students who code along with every lesson rather than just watching typically complete the capstone in 3–5 hours.

Certificate of Completion

Vianmax Academy — Certificate of Completion

Students who complete all 6 modules, submit EA 1 and EA 2 with Strategy Tester reports, and successfully pass the capstone EA review earn the Vianmax Academy Certificate of Completion — MQL4/5 Expert Advisor Development. Issued digitally with a unique verification code. The certificate is backed by three real, verifiable EA submissions — making it substantively more credible than a passive course attendance certificate. Suitable for inclusion on a trading or financial software development portfolio.

Instructor Information

Vianmax Academy Lead Faculty

Head of Algorithmic Systems & MetaTrader Development

9+ years MQL development

340+ students taught

4.9★ average rating

MT4/MT5 specialist

Our MetaTrader instructor has been writing Expert Advisors professionally since MT4's early days and transitioned fully to MQL5 when MT5's execution model matured for retail traders. Having built automated systems across Forex, indices, and commodities — ranging from simple EMA crossovers to multi-symbol portfolio managers with dynamic correlation filters — the course curriculum is grounded in what actually works in live deployment, not just clean backtests. The emphasis on robustness testing, error handling, and the pre-deployment checklist comes directly from hard-won experience watching over-optimised EAs blow up in live markets. Every piece of "don't do this" advice in the course is a personal cautionary tale.

Frequently Asked Questions

I know Python but have never coded in C++ or MQL. Will this course work for me?

Yes — Python programmers are among the fastest adapters to MQL5. The syntax is more verbose and strictly typed, but the logic structures (loops, conditionals, functions) are directly transferable. The main adjustment is understanding MetaTrader's event model (OnTick vs. OnBar), the handle-based indicator system, and CTrade's trade execution API. Module 1 is specifically designed to bridge the gap for programmers coming from other languages, not just absolute beginners. Students who have completed our Algorithmic Trading with Python course typically find MQL5 moderately challenging but very manageable within the first two weeks.

Do I need to buy MetaTrader? What broker should I use for the course?

MetaTrader 5 is free to download from MetaQuotes (metatrader5.com) or directly from your broker. You only need a demo account — no real money is required during the course. For the course, use any MT5 broker that offers EURUSD and USDJPY with tick history available for download — IC Markets, Pepperstone, or FP Markets are commonly used and provide excellent historical data quality. Avoid brokers with very high minimum lot sizes (some cent account brokers have unusual sizing) as it complicates the lot calculation exercises.

Does the course cover MT4 or only MT5?

The primary language taught is MQL5 on MetaTrader 5, which is the current standard. Module 1.7 is dedicated to the differences between MQL4 and MQL5, and a reference guide is included that maps common MQL4 patterns to their MQL5 equivalents. This gives you the ability to read, understand, and adapt any MQL4 code you find online. However, we do not teach MQL4 as a primary language — MT4 is approaching end-of-life for many brokers and its futures are limited. Students who specifically need to deploy on MT4 will need to use the conversion reference and adapt the MQL5 code themselves, which is well within reach by the end of the course.

Can I use the EAs built in this course on a real live account?

Technically yes — the EAs are fully functional and deployable to any MT5 broker account. However, we strongly caution against going live with any automated strategy without a minimum of 2–3 months of forward testing on a demo account first. Backtests, even rigorous walk-forward tests, cannot predict future market conditions. The EAs taught in this course are designed as educational demonstrations of solid EA architecture — not as turnkey money-making systems. The real value of the course is the ability to build and deploy any strategy of your own — with proper robustness testing — not the specific EAs included as examples.

Is this course relevant for building EAs for NSE/BSE Indian markets?

MetaTrader is primarily used for Forex, CFD indices, and commodities — not for NSE equities or BSE stocks, which trade on Indian broker platforms using their own APIs (Zerodha Kite, Upstox, etc.). If your goal is to automate Indian equity or F&O strategies, our Algorithmic Trading with Python course (which covers the Kite API directly) is more appropriate. That said, if you trade Forex or multi-asset CFDs on a MetaTrader broker, this course is directly applicable. Some students take both courses — Python for NSE automation and MQL5 for their Forex/CFD systems on separate broker accounts.

How do I handle the capstone if I don't have a strategy idea of my own?

In Module 6.4, we provide a list of eight well-documented, classic trading strategies (with full signal rules) that students can choose from as capstone starting points if they don't have their own strategy. These include a Bollinger Band breakout EA, a Stochastic oversold/overbought mean-reversion EA, and a simple Support/Resistance breakout EA, among others. The important thing is that you write the code yourself — the strategy selection is secondary to the coding exercise. Students who arrive with their own strategy idea, however, consistently produce better capstone submissions because they are personally invested in the result.

MQL4/5 Expert Advisor
Development

This course includes

Course Overview

What You Will Learn

Who This Course Is For

Prerequisites

Curriculum

Practice Live on AlphaSync

Parallel Strategy Design

Cross-Platform Backtest Comparison

NSE Paper Trading Validation

Multi-Platform Performance Report

Learning Format

Recorded Sessions

Code-Along Labs

3 Complete EAs

Full Source Code

WhatsApp Group

Instructor Code Review

Expert Advisors You Will Build

EA 1 — EMA Crossover Trend-Following Expert Advisor

EA 2 — RSI Mean-Reversion Expert Advisor with Custom Indicator

Capstone EA — Your Own Strategy

Course Materials

Time Commitment

Certificate of Completion

Vianmax Academy — Certificate of Completion

Instructor Information

Vianmax Academy Lead Faculty

Frequently Asked Questions

Ready to build your first Expert Advisor?

This course includes

Good to Have Before This Course

What to Explore After

MQL4/5 Expert AdvisorDevelopment

This course includes

Course Overview

What You Will Learn

Who This Course Is For

Prerequisites

Curriculum

Practice Live on AlphaSync

Parallel Strategy Design

Cross-Platform Backtest Comparison

NSE Paper Trading Validation

Multi-Platform Performance Report

Learning Format

Recorded Sessions

Code-Along Labs

3 Complete EAs

Full Source Code

WhatsApp Group

Instructor Code Review

Expert Advisors You Will Build

EA 1 — EMA Crossover Trend-Following Expert Advisor

EA 2 — RSI Mean-Reversion Expert Advisor with Custom Indicator

Capstone EA — Your Own Strategy

Course Materials

Time Commitment

Certificate of Completion

Vianmax Academy — Certificate of Completion

Instructor Information

Vianmax Academy Lead Faculty

Frequently Asked Questions

Ready to build your first Expert Advisor?

This course includes

Good to Have Before This Course

What to Explore After

MQL4/5 Expert Advisor
Development