Stock Trading Bot: Coding Your Own Trading Algo

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Stock Trading Bot: Coding Your Own Trading Algo

Many traders desire to be algorithmic traders but fail to correctly develop their trading robots. These traders will often come across unorganized and deceptive algorithmic coding knowledge online, as well as bogus promises of instant wealth. Lucas Liew, the developer of the online algorithmic trading course AlgoTrading101, is one possible source of credible information. Since its inception in 2014, the course has attracted over 30,000 students.

Liew’s curriculum is structured on introducing the principles of algorithmic trading. He is convinced that algorithmic trading isn’t a “get-rich-quick scam.” The fundamentals of designing, building, and maintaining your own algorithmic trading robot are outlined here (drawn from Liew and his course).

Key Takeaways

  • Many prospective algo-traders struggle to get the appropriate knowledge or coaching to effectively develop their trading robots.
  • AlgoTrading101 is a potentially credible source of teaching, with over 30,000 subscribers since its inception in 2014.
  • A trading algo or robot is computer code that detects buy and sell opportunities and can execute entry and exit orders.
  • To be lucrative, the robot must uncover consistent and lasting market efficiencies.
  • While instances of get-rich-quick scams abound, wannabe algo traders would be well to keep their expectations in check.

Rise of the Robo Advisors

What Is a Trading Robot?

At its most basic, an algorithmic trading robot is a piece of computer code that can produce and execute buy and sell recommendations in financial markets. The key components of such a robot are entry rules that indicate when to buy or sell, exit rules that indicate when to close the existing position, and position size rules that specify how much to purchase or sell.

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To become an algorithmic trader, you’ll obviously need a computer and an internet connection. Following that, a suitable operating system is required to run MetaTrader 4 (MT4), an electronic trading platform that employs MetaQuotes Language 4 (MQL4) to code trading strategies. Although MT4 is not the only program that can be used to design a robot, it does provide a number of key advantages.

While foreign currency (FX) is MT4’s primary asset class, the platform may also be used to trade stocks, equity indexes, commodities, and Bitcoin through contracts for difference (CFDs).Other advantages of utilizing MT4 (as compared to other systems) include its ease of use, extensive FX data sources, and low cost.

Algorithmic Trading Strategies

One of the first stages in building an algorithmic strategy is to consider some of the key characteristics that every algorithmic trading strategy should include. Market prudent means that the plan is fundamentally solid from a market and economic viewpoint. Furthermore, the mathematical model utilized to construct the approach should be based on reliable statistical methodologies.

Determine what information your robot is attempting to collect. To have an automated approach, your robot must be able to identify and exploit persistent market inefficiencies. Algorithmic trading techniques adhere to a strict set of rules that exploit market behavior, and the occurrence of a single instance of market inefficiency is insufficient to support a strategy. Furthermore, if the source of market inefficiency cannot be identified, it is impossible to determine whether the strategy’s success or failure was due to chance or not.

With the above in mind, there are many approach types to consider while designing your algorithmic trading robot. These include techniques that take use of one or more of the following (or any combination of them):

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

Preliminary study focuses on building a plan that is tailored to your unique personality. When establishing a strategy, it is critical to consider factors such as personal risk profile, time commitment, and trading money. You may then begin to detect the previously indicated persistent market inefficiencies. After identifying a market inefficiency, you may begin to develop a trading robot based on your unique traits.

Backtesting and Optimization

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 across different time frames, asset classes, or market conditions, particularly during “black swan” events like the 2007-2008 financial crisis.

Now that you’ve written a working robot, you’ll want to enhance its performance while eliminating overfitting bias. To optimize performance, you must first choose a suitable performance metric that incorporates risk and reward factors as well as consistency (e.g., Sharpe ratio).

Meanwhile, an overfitting bias happens when your robot is very dependent on prior data; such a robot will seem to be doing well, but since the future never perfectly replicates the past, it may fail. Overfitting may be avoided by training with additional data, deleting extraneous input characteristics, and simplifying your model.

Live Execution

You are now ready to start spending real money. Aside from being prepared for the emotional ups and downs that may occur, there are a few technological difficulties that must be handled. These challenges include choosing a competent broker and putting in place methods to control both market risks and operational risks, such as possible hackers and downtime in technology.

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Traders may learn a lot before going live by employing simulated trading, which is the practice of practicing a strategy using live market data but not actual money.

It is also critical at this stage to ensure that the robot’s performance is comparable to what was seen during the testing stage. Finally, monitoring is required to guarantee that the market efficiency for which the robot was developed remains.

The Bottom Line

It is quite possible for unskilled traders to become successful after being given a tight set of standards. However, potential traders should keep their expectations in check.

The most significant aspect of algorithmic trading, according to Liew, is “knowing under which sorts of market situations your robot will perform and when it will break down,” as well as “understanding when to interfere.” Algorithmic trading may be profitable, but knowledge is the key to success. Any course or instructor who promises big rewards without appropriate comprehension should be a significant red flag to avoid.

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