How to Build Your Own Algo-Trading Robot in 2022

 

Many traders desire to be algorithmic traders but find it difficult to properly build their trading robots. Online, these traders will frequently come across disorganized and deceptive algorithmic coding knowledge, as well as bogus promises of instant wealth. Lucas Liew, the developer of the online algorithmic trading course AlgoTrading101, is one potential source of credible information. Since its inception in 2014, the course has attracted over 30,000 students. 

 

The goal of Liew's program is to convey the fundamentals of algorithmic trading in a clear and orderly manner. He is unwavering in his belief that algorithmic trading is "not a get-rich-quick scheme." The fundamentals of designing, building, and maintaining your own algorithmic trading robot are outlined below (drawn from Liew and his course).

 

What Is a Trading Robot and How Does It Work?

An algorithmic trading robot, at its most basic level, is a computer program that can produce and execute buy and sell signals in financial markets. Entry rules, which indicate when to buy or sell, exit rules, which indicate when to close the current position, and position size rules, which define the amounts to purchase or sell, are the key components of such a robot.

 

To become an algorithmic trader, you'll obviously need a computer and an internet connection. Then you'll need a compatible operating system to operate MetaTrader 4 (MT4), an electronic trading platform that employs the MetaQuotes Language 4 (MQL4) to code trading strategies. 2 Although MT4 is not the only software available for building robots, it does offer a lot of advantages.

 

While foreign exchange (FX) is MT4's primary asset class, it may also be used to trade equities, equity indexes, commodities, and Bitcoin utilizing contracts for difference (CFDs). Other advantages of using MT4 (as opposed to other systems) include its ease of use, multiple FX data sources, and security.

 

Trading Strategies Using Algorithms

Reflecting on some of the key qualities that every algorithmic trading strategy should have is one of the first steps in building an algorithmic strategy. The strategy should be market sensible, in the sense that it is fundamentally sound from a market and economic perspective. Furthermore, the mathematical model utilized to construct the approach should be founded on strong statistical principles.

 

Next, figure out what data your robot is attempting to collect. Your robot must be able to collect identified, persistent market inefficiencies in order to have an automated approach. The occurrence of one-time market inefficiency is not enough to build a strategy around. Algorithmic trading techniques follow a tight set of rules that take advantage of market behavior.

The goal of preliminary research is to design a plan that is tailored to your unique personality. When establishing a strategy, consider factors such as your own risk profile, time commitment, and trading capital. Then you can start looking for the above-mentioned persistent market inefficiencies. You can start coding a trading robot matched to your own unique qualities once you've recognized a market inefficiency.

 

Optimization and Backtesting

Backtesting focuses on validating your trading robot, which includes checking the code to ensure it is doing what you want and understanding how the strategy performs over various time frames, asset classes, or market conditions, particularly in so-called "black swan" events like the financial crisis of 2007-2008.

 

You'll want to maximize the robot's performance while minimizing the overfitting bias now that you've coded it to work. You must first choose a good performance measure that captures risk and reward factors, as well as consistency, in order to maximize performance.

 

Meanwhile, an overfitting bias happens when your robot is too closely based on historical data; such a robot will appear to be performing well, but it may ultimately fail because the future never perfectly mimics the past. Overfitting can be avoided by using more data, deleting extraneous input features, and simplifying your model.

 

Execution in real time

You're now ready to start playing with real money. Apart from being prepared for the emotional ups and downs that you may encounter, there are a few technical issues to be handled. Selecting a proper broker and adopting procedures to handle both market and operational risks, such as potential hackers and technology outages, are among these concerns.

 

It's also crucial to check that the robot's performance is comparable to what was seen during the testing stage. Finally, the robot must be monitored to ensure that the market efficiency for which it was created is still present.

 

Conclusion 

It is entirely possible for inexperienced traders to succeed if they are given a precise set of rules. Aspiring traders, on the other hand, should keep their expectations in check.

 

The most crucial aspect of algorithmic trading, according to Liew, is "knowing which types of market situations your robot will work in and when it will break down," as well as "understanding when to interfere." Algorithmic trading can be lucrative, but knowledge is the key to success. Any course or teacher that promises huge benefits without a thorough understanding of the subject should be avoided at all costs.

 

We hope that this blog post will be beneficial for you. We will continue to create useful works in order to get inspired by everyone. We are sure that we will achieve splendid things altogether. Keep on following Finage for the best and more.

You can get your Real-Time and Historical Market Data with Finage free API key.

Build with us today!

Start Free Trial