Do Traders Need to Know How to Code?


When someone says stocks, trading, shares, or even stock market, the image that comes to anyone’s mind consists of numbers, decimals, and many things that do not make sense. It is safe to say that trading has now evolved to include finance and, to some extent, programming.

Traders need to know how to code if they want to establish a lasting career. For a market maker or a discretionary trader, coding may not be required. However, with the changing landscape of the stock market, knowing how to code will gradually become a seemingly more necessary skill.

In this article, you will learn about the types of trading and which of these require coding. You will also learn the benefits of using algorithms to decide whether it is something you would want to invest in as a skill.

IMPORTANT SIDENOTE: I surveyed 1500+ traders to understand how social trading impacted their trading outcomes. The results shocked my belief system! Read my latest article: ‘Exploring Social Trading: Community, Profit, and Collaboration’ for my in-depth findings through the data collected from this survey!

When Do Traders Need to Know Coding?

The commodities market functions based on many different strategies, which come from other types of buyers. While this unique market cannot be easily categorized in boxes, there are generally two types of trading depending on which a trader needs to know how to code.

Discretionary Trading

As the name suggests, discretionary traders like to work with freedom. They have a set of rules or a system to follow, similar to a computer programmed to do tasks, but their practices are often subject to change. Judgment, emotions, and intuitions heavily influence their actions. 

They rely on experience, analysis of reports or articles, and price trend charts available to them. Hence, discretionary traders decide based on using specific metrics and what we usually call ‘gut feeling’ to make profits.

Algorithm Trading

An algorithm is defined as a set of instructions or a straightforward procedure based on which a computer program or software works. Systematic or quantitative (quant) traders use this mechanical system to make decisions. The program is fed specific criteria, and if a stock falls in that, the trade is made. 

These traders cannot rely on merely studying a chart and making decisions based on intuition. They require concrete facts and a predictable pattern based on historical data to form algorithms, code them, and apply them to stock transactions. Their job then becomes to monitor algorithms and change them based on repeated trends. 

To develop an algorithm for this type of trade, you need to know to code and know the stock market. Algorithms are designed, keeping in mind a few factors such as:

  • Historical data patterns
  • Derivative knowledge
  • Predictions and forecasting
  • Grasp over coding
  • Risk management skills

So if you are the type to work for other clients or enjoy trading where you rely on a mixture of luck, instinct, emotions, and research, you do not need to know coding. However, if you want to become a quant trader and do trading electronically, you will need to know Python or JAVA.

Do Traders Need Programming?

Now that you have decided you want to do algorithm-based trading, you will need to understand how to compute and input complex mathematical formulas, study mathematical models and financial analysis and have a certain level of insight into the stock market. You can either buy an algorithm trading program or build them yourself, depending on what criteria’s matter the most.

Benefits of Knowing Programming and Algorithm Trading

The use of algorithm trading is increasing. The big markets such as the New York Stock Exchange and NASDAQ and even smaller markets such as the OTC Bulletin Board and Pink Sheets quote prices electronically. 

Algorithms can aid all electronic trading, and you may want to retain your competitive edge by learning to program. And while learning programming is not easy, it does have its benefits, which are becoming a necessity in modern trading.

Removes the Element of Human Emotions

Some may say that intuition itself is a skill necessary to perform well, especially if you have a good amount of experience being in the corporate and have been around a while to learn how stocks tend to rise and fall.

Stocks are a fluctuating market, so you will need a system to bring some stability to your decisions. With algorithm trading, human emotions tend to stay out of the way because the program decides for you. Traders may lose money if they depend on inconsistent decisions based on subjectivity. 

If you have already formed an impression about something, applying any historical data or information will only create conflict, ultimately resulting in poor strategies. Algorithm trading is based on mathematical models with a clear set of instructions that do not depend on factors other than reliable figures removing interventions such as greed, fear, uncertainty, or incorrect assumptions.

Create or Test Your Codes

It is human to take the success and failure of one’s strategies personally. That is why you can be more stoic about market feedback on your algorithm’s returns instead of your personal trades’ performance.

If you know how to program, you can create your own algorithms and test them out as you please. The element of dependency on another supplier for a trading algorithm is reduced, and you have control over the criteria of what stock works best for you and what does not. 

In the case of purchasing an algorithm software, programming may still come in handy. Many traders blindly believe in online trading programs and begin implementing them. Knowledge of coding will allow you to backtest whether these algorithms are even profitable.

Saves Time

Studying previous financial records and calling for different sources is a lengthy process of acquiring information. In addition to this, it may take time for you to conclude and systemize rules because your emotions may get in the way. 

Programming saves time; once you have input as a set of instructions that can be generalized, the hard part is over. Coding will help in reading signals and buying profitable stocks while you can focus on other things.

Saves Money

Algorithms are a one-time investment. Once you have learned how to manipulate it, time is saved, and costs are reduced. In large institutions, roles were typically divided based on traders and developers; one would create the strategy while the other would input it. Coding up your trading system gives you an edge.

Downsides of Programming for Trading

Speed Does Not Always Equal Good

While an algorithm’s inhuman speed saves time and even reduces costs, it does not necessarily mean good. When several incorrect decisions are made simultaneously, you are just automating a system of mistakes. Sometimes, human intervention is essential.

Complications

The algorithm can get real messy real fast if you do not have good experience in the stock market or do not know how to code. Your set of mathematical formulas may be based on uncertain judgments, and a single flaw in the algorithm will create a series of losses that will be too quick for you to stop. 

Before programming, you must have enough knowledge of both areas. Try to have an early start at studying programming language, possibly from graduate levels. And if you have crossed that phase, invest in good quality courses and instructors to help you.

Author’s Recommendations: Top Trading and Investment Resources To Consider

Before concluding this article, I wanted to share few trading and investment resources that I have vetted, with the help of 50+ consistently profitable traders, for you. I am confident that you will greatly benefit in your trading journey by considering one or more of these resources.

Conclusion

You can trade without knowing how to code. But programming can help you improve your returns and get consistent results. Programming, thus, proves to be an essential skill for programmers. If you are just starting out, don’t get involved in advanced programming because you will not need it for daily stock trading as much as you’ll need knowledge in stock valuation and trading strategies.

BEFORE YOU GO: Don’t forget to check out my latest article – ‘Exploring Social Trading: Community, Profit, and Collaboration’. I surveyed 1500+ traders to identify the impact social trading can have on your trading performance, and shared all my findings in this article. No matter where you are in your trading journey today, I am confident that you will find this article helpful!

Affiliate Disclosure: We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.

Subscribe To Our Mailing List

We send no more than 1 newsletter every month

and, you can unsubscribe at any time

    We respect your privacy. Unsubscribe at any time.

    1. Algorithmic Trading and Information. (n.d.). NYU. https://people.stern.nyu.edu/bakos/wise/2009/papers/wise2009-3b2_paper.pdf
    2. Day trading. (n.d.). Investor.gov. https://www.investor.gov/introduction-investing/investing-basics/glossary/day-trading
    3. Day trading: Your dollars at risk. (2005, April 20). SEC.gov. https://www.sec.gov/reportspubs/investor-publications/investorpubsdaytipshtm.html
    4. Fundamental analysis with algorithmic trading. (2020, March 23). QuantInsti. https://blog.quantinsti.com/fundamental-analysis-performed-algorithmic-trading/
    5. How to beat analysts and the stock market with machine learning. (n.d.). Knowledge@Wharton. https://knowledge.wharton.upenn.edu/article/beat-analysts-stock-market-machine-learning/
    6. Lu, N. (n.d.). A Machine Learning Approach to Automated Trading. Boston College. https://www.bc.edu/content/dam/files/schools/cas_sites/cs/pdf/academics/honors/16Lu.pdf
    7. Silicon Valley hedge fund takes on Wall Street with AI trader. (2017, February 7). SciPol.org. https://scipol.duke.edu/content/silicon-valley-hedge-fund-takes-wall-street-ai-trader

    Navdeep Singh

    Navdeep has been an avid trader/investor for the last 10 years and loves to share what he has learned about trading and investments here on TradeVeda. When not managing his personal portfolio or writing for TradeVeda, Navdeep loves to go outdoors on long hikes.

    Recent Posts