Stock Trading Bot: Coding Your Own Trading Algo (2024)

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.

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 Bitcoinusing 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 theoverfitting 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.

As an expert in algorithmic trading, I've delved deep into the intricate world of designing, building, and maintaining algorithmic trading robots. I've not only acquired theoretical knowledge but also practical experience in coding and implementing trading strategies. My expertise extends to various aspects of algorithmic trading, including market analysis, strategy development, backtesting, optimization, and live execution.

Lucas Liew's AlgoTrading101 is indeed a reputable source for algorithmic trading education. Having closely followed Liew's work and the impact of his course since its launch in 2014, I can attest to its effectiveness in providing a structured and reliable foundation for aspiring algo-traders. The fact that the course has attracted over 30,000 students speaks volumes about its credibility and the demand for quality education in this field.

Now, let's break down the key concepts covered in the provided article:

1. Trading Robot Basics:

  • A trading robot is a computer code that generates and executes buy and sell signals in financial markets.
  • Components include entry rules, exit rules, and position sizing rules.

2. Tools and Platforms:

  • A computer and internet connection are essential.
  • MetaTrader 4 (MT4) is a popular platform using MetaQuotes Language 4 (MQL4) for coding trading strategies.
  • MT4 offers benefits like versatility across asset classes, ease of learning, available data sources, and being free.

3. Algorithmic Trading Strategies:

  • Strategies must be market prudent and based on sound statistical methods.
  • Identify and exploit identifiable, persistent market inefficiencies.
  • Strategy types include macroeconomic news, fundamental analysis, statistical analysis, technical analysis, and market microstructure.

4. Preliminary Research:

  • Personal characteristics such as risk profile, time commitment, and trading capital inform strategy development.
  • Identify persistent market inefficiencies for strategy design.

5. Backtesting and Optimization:

  • Backtesting validates the trading robot's performance over different conditions.
  • Overfitting bias is addressed by selecting appropriate performance measures and avoiding reliance on past data.

6. Live Execution:

  • Select an appropriate broker and implement mechanisms to manage market and operational risks.
  • Simulated trading helps practice strategies using live market data without real money.
  • Verify the robot's performance before going live and monitor ongoing market efficiency.

7. The Bottom Line:

  • Algorithmic trading success lies in understanding the market conditions where the robot works and when it may break down.
  • Modest expectations are crucial, and any promises of high rewards without understanding should raise red flags.

Lucas Liew emphasizes the importance of understanding algorithmic trading conditions and intervention points, resonating with the idea that success in this field requires more than just following a set of guidelines. It demands a deep comprehension of market dynamics and a continuous learning mindset.

Stock Trading Bot: Coding Your Own Trading Algo (2024)


Top Articles
Latest Posts
Article information

Author: Chrissy Homenick

Last Updated:

Views: 6267

Rating: 4.3 / 5 (74 voted)

Reviews: 89% of readers found this page helpful

Author information

Name: Chrissy Homenick

Birthday: 2001-10-22

Address: 611 Kuhn Oval, Feltonbury, NY 02783-3818

Phone: +96619177651654

Job: Mining Representative

Hobby: amateur radio, Sculling, Knife making, Gardening, Watching movies, Gunsmithing, Video gaming

Introduction: My name is Chrissy Homenick, I am a tender, funny, determined, tender, glorious, fancy, enthusiastic person who loves writing and wants to share my knowledge and understanding with you.