There's no denying that algorithms are completely taking hold of trading markets. As experienced investor Dan Calugar points out, the proliferation of emerging technologies and the fact that this technology that used to be relegated only for big corporations is now available to everyday people has brought algo trading into the mainstream.
Major trading firms and brokerages are investing loads of time, money, and effort to ramp up their own programmatic trading offerings. Citigroup, for instance, hired 2,500 programmers for its investment banking and trading units a few years ago. Goldman Sachs, meanwhile, hired engineers, data scientists, and coders to man the trading floor.
It's not just major financial institutions that are going this route, either, as mentioned.
However, success in algorithmic trading requires a lot of study, trial and error, and investment, Dan Calugar says. Yet, even beginners can learn the basics of building effective trading algorithms.
Below, Daniel Calugar will provide a step-by-step guide for beginners in algorithmic trading. He'll offer practical insights on how to construct robust and successful trading algorithms and discuss key considerations, programming techniques and risk management strategies.
Step 1: Understanding Algorithmic Trading
Before diving head-first into coding your own trading algorithms, it's important to understand what algorithmic trading is. Algo trading utilizes computer programs that follow defined instructions to notify investors when to make a trade. In many cases, investors today will take algo trading to the next level and write code to execute trades automatically when specific criteria are met.
By using algorithms and computer programming in this way, investors can execute trades at a frequency and with a timing that's impossible for human traders to do.
This rapid trading allows investors to take advantage of even the slightest change in price by executing a trade at a precise moment in time, which helps to maximize returns. Some algo traders will practice high-frequency trading in this way so that they can realize immense profits by identifying the smallest of price discrepancies based on a set of data.
Algorithmic trading helps investors execute trades at the optimal time, giving them the best prices available when they buy and sell. It ensures that trades are placed instantly and accurately without human error. It can also reduce the cost of transactions.
Moreover, Dan Calugar says algorithmic trading helps to remove the human emotional element from investing. Algorithms will only execute trades based on pure data rather than any human psychological factor.
However, there are some downsides to algo trading that you should be aware of. It does take out the element of human judgment. Algorithms will automatically execute trades based on pure data, which doesn't allow humans to use their judgment if they feel as though there might be a statistical anomaly occurring.
It also relies heavily on technology, meaning that you need to ensure that you have an internet connection that is not only high-speed but also very reliable. Even a minor hiccup in the connection can result in missed opportunities or poorly-executed trades.
In addition, market disruptions that might be unforeseen could result in major losses, as algo trading uses mathematical models and historical data in an attempt to predict where the markets may move in the future.
Clearly, though, with the rise in popularity of algorithmic trading, there are far more benefits than there are drawbacks -- as long as you integrate a well-designed trading strategy.
Step 2: Figure Out What Algorithmic Trading Strategies to Use
Algorithmic trading is a general term that describes how the trades are executed. It isn't one particular strategy, Daniel Calugar explains, but rather a basic way of approaching trading.
So, now that you understand what algo trading is, you need to decide which trading strategies to use.
There are limitless algo trading strategies you can use, but here are some of the most common ones to consider.
1. Momentum Trading
This strategy has been used for decades in trading. Today, investors have designed trading algorithms to take advantage of it. The fundamentals of momentum trading include making predictions about the future values of assets based on previously-observed values of those same assets.
Momentum trading will follow individual stock performance to take advantage of historical trends. If a stock's price is increasing, you'll purchase the stock to help drive its price even higher. Then, once the price reaches a particular threshold, you'll sell the stock to maximize returns.
The most successful momentum traders take advantage of very short windows to buy and then sell. By using a trading algorithm for this strategy, you can capitalize on more potential trading opportunities.
2. Mean Reversion
Mean reversion, sometimes referred to as trading range, is based on the concept that all stock prices will eventually revert to their average value -- or mean -- periodically. The low and high prices of a stock, then, are considered to be only a temporary event.
Algo trading, using this strategy, will define a specific price range for a stock and then execute trades automatically once its price falls outside of that defined range. For instance, if a stock's price drops below the defined range, the algorithm will execute a purchase. Then, if it increases above the defined range, it'll execute a sale.
3. Index Fund Rebalancing
Index funds are typically great long-term investment vehicles, but they can also be used in algo trading as a short-term "hold." Every index fund has a defined period where they rebalance their holdings so they're back on par with the indices they're benchmarked to.
When this happens, there's an opportunity for a solid profit. Algo trading can capitalize on trades that are expected to occur based on how many stocks the fund has right before it rebalances. To take advantage of these profit opportunities, you'll need to execute trades in a very timely fashion and at the best price possible.
4. Arbitrage
Stocks listed on multiple markets present potential arbitrage opportunities. Investors can buy a stock that's listed for a lower price at one market while at the same time selling that stock at another market where the price is higher.
This is essentially a risk-free chance to profit from the price discrepancies in the two markets. The same concept can be used when comparing stock prices to futures, as there are sometimes major price differentials.
Trading algorithms are a necessity to take advantage of these arbitrage opportunities.
5. Market Timing
Market timing includes executing trades at the exact perfect time. This can be extremely challenging with manual trading, as it's very easy for humans to miss out on that perfect price. They can also second-guess themselves at times as they either wait for the perfect price to come along or execute a trade too soon based on their gut feelings.
Algo trading helps to time the market perfectly. It'll use current trends in the market and then compare them to historical activity to determine the best timing to execute trades.
It obviously isn't a perfect strategy, but it significantly helps traders eliminate false starts.
Step 3: Figure Out Which Programming Technique to Utilize
Up to this point, most of the work has revolved around researching and acquiring knowledge about algo trading in general and specific strategies for its use. Now, Daniel Calugar says it's time to get into the nitty gritty of building effective trading algorithms.
This involves actually building the algorithms that you will use to notify you when trades should be executed or to make the trades automatically for you. Investors will have two main options in this regard -- writing the code themselves or using an already-built platform that doesn't require knowing how to write code.
Algorithmic Trading without Using Code
If you don't want to write any code -- or hire someone else to do it on your behalf -- you can always opt for an off-the-shelf solution. Here are some of those options.
1. Microsoft Excel
If you're new to algorithmic trading, a great place to start is with a program that you likely already have loaded onto your computer -- Microsoft Excel. This is an extremely powerful program that you can actually use with a lot of success to write and test complicated algorithms.
While a majority of people only use the basic function of the program, you can use an Excel spreadsheet to test a huge amount of data against an algorithm to spit out projected outcomes.
2. Commercial platform
Many brokerage and investment firms today offer ready-to-use commercial platforms explicitly designed for the retail investor. One of the main advantages of this is that it's already been designed, created, and tested for you. It's an off-the-shelf solution, if you will.
The interface is typically very user-friendly and has all of the features that you would need, such as educational resources, newsfeeds, notifications, and reports built right in.
3. Tickerton
SAS Global Corp.'s subsidiary, Tickerton, has AI bots that are fully customizable. You'll get price alerts that can help you time trades for exchange-traded funds, cryptos, stocks, and forex -- all based on pattern recognition.
Using artificial intelligence tools, you'll be provided with the top price patterns for stocks on a daily basis, along with a confidence score for all of your trading ideas. The platform provides many forecasting tools so you can more accurately predict future prices.
4. EquBot
This platform uses IBM's Watson to help investors make their trading decisions. It even incorporates analysis of social media platforms and news articles. This platform could be particularly useful if you're focused on using alternative data.
5. Composer
This trading platform uses ChatGPT4, one of today's most well-known AI apps. The interface is simple to use, as it's just drag-and-drop. This allows traders to easily build and test complex trading strategies before deploying them.
The platform has a solid library of indicators, along with a wealth of data and multiple trading actions -- all of which are pre-built into it.
Algorithmic Trading Using Code
If you want a fully-customizable solution, you can learn a computer programming language and create the algorithms on your own, or you can hire someone to create them for you.
The biggest advantage to going this route is that you will be able to control all the "levers" in the process. You can implement and tweak each aspect of the trading algorithm to your liking, helping to ensure that you won't be reliant on a platform created for the masses.
The biggest disadvantage to writing your own code is that you will be left fully to your own devices. If you're thinking is incorrect -- or if the code that you write isn't accurate -- then you could make major mistakes that cost you a huge amount of money.
Even if you hire an experienced computer programmer to develop your trading algorithms, you'll be relying on the work of someone else. In other words, you'll need to trust that this person (or team) doesn't make any mistakes.
Should you decide to use code for algorithmic trading, these are the main programming languages to consider.
1. Python
Python is one of the most common computer programming languages used for algorithmic trading. This open-source language is fairly simple to use. You won't need to create nearly the amount of code with Python as you might with other programming languages in order to create an effective trading algorithm.
2. C++
Investors who use high-frequency trading will typically go with C++. This would be considered a mid-level language, so not too difficult to learn.
C++ is really good at efficiently processing huge data volumes, making it a great fit for high-frequency trading. Many legacy systems at large financial institutions also use C++.
3. Java
Java is great for low latency trading, simulation, and data modeling -- all reasons why algo traders tend to flock to it. In addition, this programming language is easy to learn, user-friendly, and provides plenty of flexibility. It's also an extremely secure programming language, which is why many large corporations' information technology departments write in it.
4. JavaScript
This programming language is used in a variety of applications in multiple industries. It works well for trading because it's very good at building financial planning solutions and conducting high-level analysis.
5. C#
C# is much more "low-level" than some of the other programming languages listed here, and that's one of the reasons why it's attractive to some algo traders. It's similar to C++ and Java in some ways, yet it's much simpler. Because of this, it helps reduce the time needed to develop trading algorithms.
6. R
To create an effective trading algorithm, Dan Calugar says you'll need to extensively back-test it (more on that in a bit). This is where the R programming language excels. It's great at helping to maximize returns from trading signals by extensively testing through sample data.
Even if your trading algorithm isn't written in R, you can use this programming language specifically for its back-testing component.
Step 4: Identifying Why Your Algorithms Work
Once you've decided how to create your algorithms and have them ready to go, it's time to test them out before you deploy them in the real world. You can use widely available data to backtest your algorithms to see if your theories would hold up in actual market scenarios.
To do this, you'll run your trading algorithms against historical data. This will allow you to see if the trading signals that your algorithms produce will result in successful trades.
A key component of backtesting is running it over a long period of time. There will be a profit or a loss associated with every trade that your algorithm executes during backtesting. You'll then accumulate this profit or loss for the entirety of the strategy to see whether it would result in a positive or negative outcome.
According to Daniel Calugar, backtesting is a great way to test out new algorithms or new models. It can be applied to market issues such as liquidity, transaction costs, latency, order routing and more.
It also allows you to improve your strategy's performance over time by modifying the values of all the parameters that you have inputted. Once you make these adjustments, you can then re-test it to see what the new outcomes would be.
When you backtest a trading strategy, you'll also be verifying that it has been implemented correctly. Sometimes, the theory behind the algorithm is correct, but the way it executes is incorrect. Backtesting will provide insight into what problems you might be having and where those problems are arising.
Ultimately, what you are after in this step is figuring out the specific reason why the algorithm works. Most trading algorithms are based on pattern recognition. Computers are excellent at picking out patterns from data that is, essentially, random.
If you aren't able to identify the specific reasons why a pattern has repeated itself consistently in the past -- which is referred to as the "edge" that's being exploited -- then the odds are that the pattern won't repeat in the future.
In other words, an essential part of backtesting is building upon recognition of a pattern to identify the reasons behind the pattern. Once you do that, you'll be able to invest using the algorithm with confidence.
Step 5: Put Your Algorithms to Use
After backtesting, tweaking your algorithms, and considering all risk management strategies, it's time to put your algorithms to use.
Dan Calugar says this involves connecting your algorithm to the trading platform that you use to execute the trades. Each platform will have its unique way that this needs to be done, so you'll need to consult with the platform to figure out the specifics.
Luckily, with algorithmic trading becoming commonplace today, every main trading platform should provide easy-to-access documentation on the required steps.
Keep in mind that even after you put your algorithms to use, you should always be analyzing the results. If you think something might be off or could be performing better, you can always backtest again to fully optimize your trading algorithms.
About Daniel Calugar
Daniel Calugar is a versatile and experienced investor with a background in computer science, business and law. While working as a pension lawyer, he developed a passion for investing and leveraged his technical capabilities to write computer programs that helped him identify more profitable investment strategies. When Dan Calugar is not working, he enjoys working out, being with friends and family, and volunteering with Angel Flight.
