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 with a deep understanding of the subject, I can attest to the challenges many traders face when aspiring to become algorithmic traders. The article rightly points out the struggle of coding trading robots properly and the abundance of disorganized and misleading information online. Having navigated through the intricacies of algorithmic trading, I can shed light on the valuable insights provided by Lucas Liew, the creator of AlgoTrading101, an online course that has gained significant recognition since its launch in 2014.

Lucas Liew's course is a reliable source of information, having attracted over 30,000 students. His approach focuses on presenting the fundamentals of algorithmic trading in an organized manner, emphasizing that algorithmic trading is not a "get-rich-quick scheme." Let's delve into the key concepts outlined in the article and provide additional insights:

1. What Is a Trading Robot?

At its core, a trading robot is computer code capable of generating and executing buy and sell signals in financial markets. The main components include entry rules, exit rules, and position sizing rules. To embark on algorithmic trading, a trader needs a computer, internet connection, and a suitable operating system to run platforms like MetaTrader 4 (MT4). MT4 is favored for its versatility, supporting various asset classes, ease of learning, numerous available data sources, and being free.

2. Algorithmic Trading Strategies

Developing an algorithmic strategy involves reflecting on core traits, ensuring it's market prudent and based on sound statistical methods. The strategy must capture identifiable, persistent market inefficiencies. Strategy types can include macroeconomic news, fundamental analysis, statistical analysis, technical analysis, and market microstructure. Personal characteristics, risk profile, time commitment, and trading capital are crucial factors in strategy development.

3. Backtesting and Optimization

Backtesting is vital for validating a trading robot, checking code functionality, and understanding strategy performance across different conditions. Optimization aims to maximize performance while minimizing overfitting bias. Choosing a suitable performance measure (e.g., Sharpe ratio) is crucial. Overfitting, a risk when a robot relies too closely on past data, can be mitigated by using more data, removing irrelevant features, and simplifying the model.

4. Live Execution

Moving to live trading involves addressing emotional ups and downs and technical issues such as selecting an appropriate broker and managing market and operational risks. Simulated trading is a valuable step before using real money. It's crucial to verify that the robot's performance aligns with the testing stage and continuously monitor market conditions.

5. The Bottom Line

Lucas Liew emphasizes the importance of understanding under which market conditions a robot works and when it might break down. Success in algorithmic trading lies in understanding, and traders should have modest expectations. Any course or teacher promising high rewards without sufficient understanding should be approached with caution.

In conclusion, algorithmic trading is a complex yet rewarding endeavor, and aspiring traders can benefit from structured education and realistic expectations. Lucas Liew's AlgoTrading101 course serves as a reliable guide in navigating this challenging field.

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