August 01, 2023
4 mins read
Algorithmic trading or algo trading, is a way of placing trades using a computer program that follows a set of rules. When certain conditions are met, the program automatically places orders at a speed and frequency that humans can't match.
Traders can create algorithms to generate buy or sell signals, but they still need to manually place orders as full automation isn't allowed for individual traders. If you're interested in algo trading but don't know how to code or understand the technical details, there are resources available to help you get started.
Algorithmic trading is the process of using a computer program or algorithm to automatically carry out a trading strategy. It’s especially useful for large investment firms or fund houses that need to place a high volume of orders quickly and efficiently, something that would be impossible for humans to do manually.
To explain how it works, let’s consider an example.
Mr. A, an algorithmic trader, decides to use a new strategy. He wants to buy a stock every time its Relative Strength Index (RSI) goes above 60. Instead of logging into his account, entering the trade details, and manually clicking to execute the order, Mr. A creates an algorithm. This algorithm automatically places a buy order whenever the RSI of a stock goes above 60. Mr. A also sets the details like the quantity to buy and the stop-loss level. Once set, the algorithm will monitor the market and place the trade automatically whenever the conditions are met
Additional Read: Scalping Trading
Algorithmic trading, also known as algo trading, involves the use of automated systems programmed to follow defined sets of instructions to place trades. These instructions are usually based on time, price, volume, or other market-related variables. Traders use algorithms to identify potential trading opportunities and execute orders at optimal speeds. The main objective is to remove human emotions from the trading process, improve efficiency, and reduce transaction costs.
Key elements of algorithmic trading:
You can automate any trading strategy by giving sufficient instructions. Here are some of the most commonly used algorithmic trading strategies.
In arbitrage trading strategy, algorithms analyze stock prices from different stock exchanges. It then buys a stock trading at a lower price from a specific exchange and sells it into a different stock exchange where the price of the same stock is relatively higher. This strategy aims to profit from price differences of the same stock in different stock exchanges. This process of buying and selling is done within seconds!
This is one of the most commonly used and simple Algo trading strategies adopted by most investors and traders. What makes this simple is that the trades in this strategy are executed by following the trend and the market’s momentum. Trend-following tools such as moving averages, trendlines, and chart patterns are used to identify an entry and exit in the trade. When the algorithm meets with a proper set of instructions required for this strategy, the strategy gets executed automatically.
Index funds have to adjust frequently to match their underlying asset’s performance. This strategy aims to take advantage of this minor opening and grab the opportunity by taking a trade for making a profit as low as .20 to .80 basis points.
These orders are executed in microseconds which is why they are humanly impossible to execute.
“Mean” means the average price of a stock. It is said that the underlying fluctuation in a stock is temporary, and the stock will always revert to its mean. In this strategy, algorithms define a specific range for a stock. And similarly, they buy and sell orders as the price of the stock gets in or out of the defined range.
These strategies rely on quantitative models that use historical price data and mathematical calculations to forecast future price movements. They include statistical arbitrage, mean reversion, and market-making models. Traders deploy these algorithms to identify pricing inefficiencies across markets or securities and execute trades within milliseconds to capture small profits repeatedly.
VWAP strategies aim to execute orders in line with the average price weighted by volume over a specific period. The goal is to minimise the market impact by breaking large orders into smaller parts, executing more trades when market volumes are high. It’s widely used by institutional investors for benchmarking performance.
TWAP strategies divide a large order into equal parts and execute them at regular time intervals. This method ensures steady execution throughout the trading session, irrespective of market volume. It helps avoid sudden price fluctuations and is ideal when the goal is to maintain price neutrality.
In a POV strategy, the algorithm adjusts order size dynamically based on real-time trading volume in the market. If the market is active, larger portions of the order are executed; during quieter periods, the algorithm slows down. It helps traders stay in sync with overall market activity.
This strategy focuses on reducing the difference between the decision price (when a trade is initiated) and the final execution price. It aims to minimise trading costs, including delay, market impact, and opportunity costs. Implementation shortfall is widely used by asset managers focused on cost-sensitive execution.
Advanced algorithmic strategies go beyond simple execution models. These include machine learning-based algorithms, sentiment analysis from news and social media, and adaptive strategies that tweak execution rules in real-time based on market conditions. Such models aim to capture alpha while maintaining execution efficiency and risk controls.
Also Read : Benefits of Using Automated Trading Tools
Here are some of the reasons why algorithmic trading is being adopted so quickly by many investors and traders.
While algorithmic trading offers speed and automation, it also comes with several limitations that you need to consider before diving in:
Algorithmic trading, or algo trading, uses computer programs to execute trades at high speeds based on predefined criteria. Examples include:
Trend-Following Strategies: These algorithms identify and follow market trends, buying in an uptrend and selling in a downtrend.
Index Fund Rebalancing: Algorithms manage large-scale buy and sell orders efficiently during periodic rebalancing.
Market Making: Algorithms continuously place buy and sell orders to profit from the bid-ask spread.
High-Frequency Trading (HFT): These algorithms execute numerous trades within milliseconds to capitalize on tiny price changes.
If you're planning to use algorithmic trading, you'll need a few technical essentials in place. These elements help you automate trades, cut down delays, and keep your strategy stable. Here’s what you should have set up:
Getting these right helps you build an algo system that reacts fast, works reliably, and stays in sync with market conditions.
Algorithmic trading blends speed, data, and automation to offer a powerful approach to modern markets. But success in algo trading isn't just about technology—it also depends on your understanding of market dynamics, risk tolerance, and strategy design. Relying solely on automation without robust testing or monitoring can result in significant losses. To truly benefit from algorithmic systems, you need to stay informed, continually evaluate performance, and make adjustments as market conditions evolve. With the right foundation and discipline, algorithmic trading can help you scale your efforts while keeping your decisions rule-based and data-driven.
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The different algorithmic trading strategies involve trend-following strategies, index fund rebalancing, and mathematical model-based strategies.
Yes, every category of investor can use this trading system for different purposes. Hedge funds can use it to take opposite positions and hedge their investments. Institutional investors use it to buy large quantities of stock without creating an impact on the price of the quantity.
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