Forex algorithmic trading course learn how to code on mql4 (step by step)

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1.1 About your Instructor .
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1.3 Why Automate Trading .
2.1 Expert Advisors .
2.2 Metaquotes Language .
2.3 EAs, Indicators and Scripts .
3.1 Printing out Statements .
3.2 Variables .
3.3 Variables Advanced .
3.4 Predefined Variables .
3.5 Arithmetic Operations .
3.6 Assignment Operations .
3.7 Relational Operations .
3.8 Logical Operations .
4.1 If Else Statements .
4.2 Switch Statements .
5.1 Functions .
5.2 Metatrader Functions .
5.3 Include Files .
5.4 Creating Our First Useful Function .
5.5 Calculating Take Profit and Stop Loss .
5.6 Assignment – Create Take Profit and Stop Loss .
5.7 Answer to Assignment .
5.8 Global Variables .
5.9 Input & Extern Variables .
Preview of the rest of the course .

In this course you will learn how to completely automate a Forex Trading Robot from scratch using the MQL4 Programming language.

You do not need any programming knowledge as we will learn all the basic programming concepts in the beginning of the course. The great thing about this course is that we view these programming concepts as they relate to trading, keeping the content extremely engaging.

We proceed by learning the ins and out of the MQL4 programming language. We see how to get live price updates, use most technical indicators in code, send and modify orders automatically and much much more.

We do all of this in a highly engaging manner as we code everything as we cover it. We also give you many assignments along the way making this an extremely practical and interactive course.

Once we have covered all the concepts necessary, we proceed by creating our fully automated trading robot – FOREX ALGORITHMIC TRADING. We backtest it to make sure it’s consistently profitable and see how to run it on a demo or live account.

All the codes created in the course are available to you.

Many traders aspire to become algorithmic traders but struggle to code their trading robots properly. These traders will often find disorganized and misleading algorithmic coding information online, as well as false promises of overnight prosperity. However, one potential source of reliable information is from Lucas Liew, creator of the online algorithmic trading course AlgoTrading101. The course has garnered over 30,000 students since its launch in 2014.

Liew’s program focuses on presenting the fundamentals of algorithmic trading in an organized way. He is adamant about the fact that algorithmic trading is “not a get-rich-quick scheme.” Outlined below are the basics of what it takes to design, build, and maintain your own algorithmic trading robot (drawn from Liew and his course).

Key Takeaways

  • Many aspiring algo-traders have difficulty finding the right education or guidance to properly code their trading robots.
  • AlgoTrading101 is a potential source of reliable instruction and has garnered more than 30,000 since its 2014 launch.
  • A trading algo or robot is computer code that identifies buy and sell opportunities, with the ability to execute the entry and exit orders.
  • In order to be profitable, the robot must identify regular and persistent market efficiencies.
  • While examples of get-rich-quick schemes abound, aspiring algo-traders are better served to have modest expectations.

3:20

Rise of the Robo Advisors

What Is a Trading Robot?

At the most basic level, an algorithmic trading robot is a computer code that has the ability to generate and execute buy and sell signals in financial markets. The main components of such a robot include entry rules that signal when to buy or sell, exit rules indicating when to close the current position, and position sizing rules defining the quantities to buy or sell.

Obviously, you’re going to need a computer and an internet connection to become an algorithmic trader. After that, a suitable operating system is needed to run MetaTrader 4 (MT4), which is an electronic trading platform that uses the MetaQuotes Language 4 (MQL4) for coding trading strategies. Although MT4 is not the only software one could use to build a robot, it has a number of significant benefits.

One advantage is that, while MT4’s main asset class is foreign exchange (FX), the platform can also be used to trade equities, equity indices, commodities, and Bitcoin using contracts for difference (CFDs). Other benefits of using MT4 (as opposed to other platforms) are that it is easy to learn, it has numerous available FX data sources, and it’s free.

Algorithmic Trading Strategies

One of the first steps in developing an algorithmic strategy is to reflect on some of the core traits that every algorithmic trading strategy should have. The strategy should be market prudent in that it is fundamentally sound from a market and economic standpoint. Also, the mathematical model used in developing the strategy should be based on sound statistical methods.

Next, determine what information your robot is aiming to capture. In order to have an automated strategy, your robot needs to be able to capture identifiable, persistent market inefficiencies. Algorithmic trading strategies follow a rigid set of rules that take advantage of market behavior, and the occurrence of one-time market inefficiency is not enough to build a strategy around. Further, if the cause of the market inefficiency is unidentifiable, then there will be no way to know if the success or failure of the strategy was due to chance or not.

With the above in mind, there are a number of strategy types to inform the design of your algorithmic trading robot. These include strategies that take advantage of the following (or any combination thereof):

  • Macroeconomic news (e.g., non-farm payroll or interest rate changes)
  • Fundamental analysis (e.g., using revenue data or earnings release notes)
  • Statistical analysis (e.g., correlation or co-integration)
  • Technical analysis (e.g., moving averages)
  • The market microstructure (e.g. arbitrage or trade infrastructure)

Preliminary research focuses on developing a strategy that suits your own personal characteristics. Factors such as personal risk profile, time commitment, and trading capital are all important to think about when developing a strategy. You can then begin to identify the persistent market inefficiencies mentioned above. Having identified a market inefficiency, you can begin to code a trading robot suited to your own personal characteristics.

Backtesting and Optimization

Backtesting focuses on validating your trading robot, which includes checking the code to make sure it is doing what you want and understanding how the strategy performs over different time frames, asset classes, or market conditions, especially in so-called “black swan” events such as the 2007-2008 financial crisis.

Now that you have coded a robot that works, you’ll want to maximize its performance while minimizing the overfitting bias. To maximize performance, you first need to select a good performance measure that captures risk and reward elements, as well as consistency (e.g., Sharpe ratio).

Meanwhile, an overfitting bias occurs when your robot is too closely based on past data; such a robot will give off the illusion of high performance, but since the future never completely resembles the past, it may actually fail. Training with more data, removing irrelevant input features, and simplifying your model may help prevent overfitting.

Live Execution

You are now ready to begin using real money. However, aside from being prepared for the emotional ups and downs that you might experience, there are a few technical issues that need to be addressed. These issues include selecting an appropriate broker and implementing mechanisms to manage both market risks and operational risks, such as potential hackers and technology downtime.

Before going live, traders can learn a lot through simulated trading, which is the process of practicing a strategy using live market data but not real money.

It is also important at this step to verify that the robot’s performance is similar to that experienced in the testing stage. Finally, monitoring is needed to ensure that the market efficiency that the robot was designed for still exists. 

The Bottom Line

It is entirely plausible for inexperienced traders to be taught a strict set of guidelines and become successful. However, aspiring traders should remember to have modest expectations.

Liew stresses that the most important part of algorithmic trading is “understanding under which types of market conditions your robot will work and when it will break down” and “understanding when to intervene.” Algorithmic trading can be rewarding, but the key to success is understanding. Any course or teacher promising high rewards without sufficient understanding should be a major warning sign to stay away.

S. Kovalyov

Programming in Algorithmic Language

Introductory Course

Nowadays, a personal computer became indispensable for everybody. The rapid development of Internet and performance of modern computers opened up new vistas in many fields of human activities. As early as ten years ago, the financial market trade was available only for banks and for a limited community of specialists. Today, anybody can join the world of professional traders and start independent trading at any time.

Hundreds of thousands of worldwide traders have already judged MetaTrader 4 Client Terminal on its merits. The use of its embedded programming language, MQL4, lifts traders to a new level of trading – to automated trading. Now, a trader can implement his or her ideas as an application program – write a custom indicator, a script to perform single operations, or create an Expert Advisor – an automated trading system (trading robot). An Expert Advisor (EA) can work on a 24/7 basis without any intervention – track security prices, send electronic messages, SMSes to your mobile phone, as well as do many other useful things.

The main advantage of applications is the possibility to make trades according to the algorithm set by the trader. Any ideas that can be described in an algorithmic language (intersection of two moving averages or digital processing of signals, three screens by Elder or Peters’ fractal analysis, a neural network or geometrical constructions) can be coded in an application and then used in practical trading.

Development of applications for MetaTrader 4 Client Terminal requires the knowledge of MQL4. This present textbook will help you create your own Expert Advisors, scripts and indicators and incarnate in them your ideas – your algorithms of profitable trading. The textbook is intended for a large number of readers without experience in programming that want to learn how to develop automated trading applications for MetaTrader 4 Client Terminal. The textbook is designed in such a method that to make learning MQL4 as convenient and consequent as possible.

Written by Jane