Algorithmic Trading

Algo trading is the use pre-programmed trading systems that execute trades automatically based on rules you've defined. These computerized trading algorithms constantly browse markets at lighting-speed and take advantage of trading opportunities most humans could never find or execute quick enough.

Algorithmic trading systems can be expensive to power and run continuously, and their level of sophistication means institutional traders like hedge funds, asset managers, and financial institutions often employ highly advanced programmers to develop the systems.

How do algorithmic trades work?

Algorithmic trades work on a set of pre-defined rules coded by traders and investors, when the specific conditions programmed into the computer are met, the algo trading system executes trading decisions on behalf of the trader/investor.

Algorithmic trading systems can be programmed for any type of strategy, some of which we'll cover in the next section, and they are most effective in fast-moving, highly liquid markets like forex, cryptocurrencies, derivatives, and the stock market.

The StoneX One Pro trading platform, designed for professional traders, provide access to the technology and liquidity needed for optimized algo performance. Learn more about StoneX electronic trading and execution.

The use of AI in algorithmic trading strategies

Depending on the sophistication of your system, some algo trading strategies utilize AI techniques like machine learning to adapt to market trends or large language models (LLMs) to monitor financial news and off-market sentiment.

The sophistication of algo systems employing AI is limitless, as long as you have the technical know-how and computing power to fuel it. However, many algorithmic trading systems should not be confused for AI-powered just because they employ advanced systems of technical and quantitative analysis. Algo trading, for the most part, is limited by the parameters it is programmed for.

Algorithmic trading strategies

Algorithmic traders often focus on taking advantage of miniscule, inexpensive movements in the market that are too obscured for human traders to focus on. Algo trading works best with these strategies, and systems with deep coffers and wide access can be the impetus for wider market movements.

If you're just getting started with algo trading and simply want to automate some trades to make your life easier or take advantage of more market opportunities than a single person reasonably can, it's best to start off programming simple tried-and-true strategies.

Simple trading algorithm examples

An example of a simple algorithmic trading system uses basic technical analysis such as moving averages and price channel breakouts. These don't require price forecasting or far-ranging market predictions and are fairly easy to implement using algorithmic trading.

With these simple technical strategies, a trade is entered at the occurrence of easily identifiable signals. The same technical signals are also used to flag exit opportunities in this example. Your trade will then be executed based on the best price available, whether you have a long or short position, as soon as market conditions are met.

To set up a simple trading algorithm like this, all you need is a platform with the ability to integrate automatic trading systems into your account. StoneX offers electronic trading and execution with a full OTC algorithmic suite across multiple global exchanges and venues including over 185 foreign exchange markets, dozens of derivatives exchanges, and hundreds of OTC products.

The StoneX electronic trading platform is available for both self-directed and professional traders.

Moving average trading algorithm example

Moving average trading algorithms are highly popular and simple to implement. These algorithms buy a security (e.g., stocks) if its current market price is below its average price over a specified period and sell it if the price is above this average.

Consider a 20-day moving average trading algorithm. For instance, the algorithm would buy Microsoft (MSFT) shares if the current price is lower than the 20-day moving average and sell if the price exceeds the 20-day moving average. Algorithmic trading strategies can be as simple as this example, or they can be much more complex.

High frequency trading algorithms

A high-frequency trading (HFT) algorithm operates by executing a large number of orders at extremely fast speeds to capitalize on minute price discrepancies. Here’s a brief example:

1. Data Collection: The algorithm continuously collects real-time market data on stock prices, order book depth, and market trends from multiple exchanges.

2. Signal Generation: The algorithm detects a temporary price discrepancy between two exchanges for the same stock, such as Company XYZ trading at $100 on Exchange A and $100.10 on Exchange B.

3. Order Execution: Instantly, the algorithm places a buy order on Exchange A at $100 and a sell order on Exchange B at $100.10.

4. Profit Realization: The algorithm profits from the $0.10 price difference per share, executing these trades in milliseconds before the discrepancy corrects.

5. Repetition: This process repeats thousands of times throughout a single day, continuously scanning for similar opportunities and executing trades to profit from tiny price differences using extreme trading volume.

Is algo trading profitable?

Algo trading can be profitable, as long as you take proper steps to ensure an airtight strategy. Like any other trading strategy, proper backtesting and validation methods are crucial before entering live markets. Typical risk management like stop losses should also be coded into your algorithm to prevent losses from adding up.

Algorithmic trading isn't a magic ticket to profitable trades. Like any other trading method, the strategies used and signals included, essentially your ability to create a system that executes trades at the best possible price is what will determine the profitability of your algo trading.

Advantages of algo trading

There are many advantages to algo trading depending on the type of player and market traded in. The main advantage of algo trading is its use in eliminating emotional decision making. By setting the parameters for your trading strategy and allowing the algorithm to execute independently, you avoid the risk of being swept away in a tide of sentiment-based decisions that might occur during periods of extreme market volatility.

Another example, institutional investors looking to not distort the market with outsized orders can use an algo system to open orders in a number of smaller batches to avoid making waves in the market. The algorithmic trading strategy can optimize this process to reduce the total time such a lengthy process might take, as well as lowering transactional costs.

Additionally, some trading strategies mentioned above, such as high frequency trading, are only possible with algorithmic systems. Being able to build profits in a quiet market with small movements is a relatively new development in trading, all made possible by algorithmic strategies. These rapid trades also reduce implementation shortfall, which occurs when a trader receives a different price than expected due to lags in the trading process.

Disadvantages of algo trading

The disadvantages to algorithmic trading include the barriers to entry and tunnel vision of the algorithm. Algo trading requires access to liquid and fast-moving markets, the technical skills to code well-performing algorithms, and a platform that makes it possible to run automated trades.

The other main disadvantage of algorithmic trading strategies is their inability to adapt to new market trends. The only trades your algo strategy will execute are those you program into it. Your system will only ever be as powerful as the indicators you program into it. Without manual oversight, you could miss lucrative trading opportunities all because your algorithm isn't triggered by their movements.

Algorithmic trading relies heavily on advanced technology and robust architecture. Any malfunction, outage, or error can negatively impact the trading algorithms. A defect within data feeds or the order execution system might also derail the algorithm and result in significant losses. This is why institutional traders who can ensure robust system design and continual management are best set up to monitor the trading activities of algo systems.

FAQs

Is algo trading legal?

Yes, algo trading is legal in the United States. Like all financial markets, algo trading is regulated by agencies including the SEC, CFTC, and FINRA.

• Securities and Exchange Commission (SEC): Oversees securities markets and enforces regulations that apply to trading practices, including algorithmic trading.

• Commodity Futures Trading Commission (CFTC): Regulates the futures and options markets and supervises algorithmic trading in these markets.

• Financial Industry Regulatory Authority (FINRA): A self-regulatory organization that oversees brokerage firms and exchange markets, including their use of algorithms in trading.

Does algorithmic trading improve liquidity?

Yes, algorithmic trading generally improves liquidity in financial markets. The high volume of trades processed by most algorithmic trading increases the overall market volume by increasing the efficiency of trades, contributing to greater liquidity through its market impact. Algorithms also narrow the bid-ask spread by exploiting the small inefficiencies between them, placing orders at slightly better prices which contribute to narrower spreads and higher liquidity. Because algorithms operate as market makers, their constant activity provides an ever-available stream of buy and sell orders for all other players, further increasing liquidity.

Does algorithmic trading reduce information acquisition?

Algorithmic trading reduces traditional information acquisition by focusing on speed and real-time data analysis over in-depth research. Algorithms prioritize quick reactions to price movements, often using big data and alternative sources like social media. This enhances the processing of vast information but may decrease fundamental market research. Despite this, human traders and analysts provide essential insights, ensuring a balanced market. Regulatory measures help maintain informed and transparent markets. Overall, while algorithmic trading shifts how information is acquired, it broadens and accelerates information processing in financial markets.

Do hedge funds use algorithmic trading?

Yes, hedge funds extensively use algorithmic trading to execute trades quickly and efficiently, leverage complex strategies, and exploit market inefficiencies. Algorithms help hedge funds analyze vast amounts of data, manage risk, and enhance trading precision. By automating processes, they achieve better execution, reduced costs, and improved performance.

Hedge funds and other institutional investors also benefit from holding a large amount of resources which enable them to employ the brightest minds to create these algorithms and provide enough money for the algorithms to make large, prolonged moves within the market.

Do prop firms allow algo trading?

Yes, prop (proprietary) trading firms often allow and even encourage algorithmic trading. They use algorithms to execute trades rapidly, optimize strategies, and capitalize on market inefficiencies. Prop firms leverage advanced technology and data analysis to gain a competitive edge, making algorithmic trading a key component of their operations.