Algorithmic trading has transformed financial markets by automating trading processes. It relies on computer programs to execute predefined strategies. These strategies analyze market data like price, volume, technical indicators, and financial statements to make decisions without human intervention.
Intraday trading involves short-term buying and selling of financial instruments, often within the same trading day. Common instruments include index futures and commodity futures. Developed markets offer additional trading options such as stocks, options, and currencies. The significant disparity between open interests in futures contracts and daily trading volume highlights the rapid growth of intraday trading in recent years.
This article focuses on the evolution of intraday trading and delves into the impact of computer algorithms on intraday trading, particularly algorithms in quantitative finance and high-frequency trading.
History of Intraday Trading
The majority of investments in the stock market are long-term investments. They can be in the form of ETF investing or stock selections. For instance, a smart-beta portfolio assumes the global economy will improve over an extended period, leading to long positions being held most of the time. In contrast, intraday trading involves speculating on securities within the same trading day. Traders buy and sell financial instruments, anticipating whether their prices will rise or fall during that day. Intraday traders also exploit potential market inefficiencies to seize short-term opportunities.
Typically, intraday trading involves closing all open positions before or during the ATC session to avoid overnight risks and price gaps. This practice is commonly known as overnight-0 strategies. However, as the field evolves, some traders employ a related form of intraday trading where they hold positions overnight. It certainly incurs higher fees but aims to capitalize on favorable price gaps when the market opens in the near term.
Intraday trading is relatively new in the context of stock market history, and has significantly impacted financial markets. The first stock ticker tape system was invented in 1867 by Edward Calahan. However, intraday trading didn’t gain prominence in the US market until the creation of electronic communication networks (such as Instinet) in 1969 and the automated quotation system (like NASDAQ) in 1971. Subsequent deregulation of commissions in 1975 allowed traders to execute more frequent trades without excessive costs, fostering active trading. The 1990s witnessed a revolution with the advent of electronic trading platforms powered by computers. These platforms enabled faster trade execution and significantly increased trading liquidity.
How Algorithms Impact Intraday Trading
Computers have revolutionized trading by allowing traders to execute predefined strategies through computer programs and algorithms. These lightning-fast machines enable algorithmic traders to react swiftly to market movements, benefiting intraday traders. Manually capturing rapidly arising and disappearing opportunities during the trading day has become nearly impossible.
Unlike human traders, computer programs operate neutrally, adhering strictly to predefined rules and avoiding impulsive decisions driven by emotions like fear or greed. Algorithmic trading efficiently manages large trading volumes across different stocks and future contracts. It helps traders handle a large number of positions in various markets.
In the field of quantitative finance, algorithms play a pivotal role in various intraday trading techniques. The following are examples of current practice.
High-frequency trading (HFT). HFT is a specialized form of algorithmic trading that aims to execute a large number of trades within extremely short time intervals, often measured in milliseconds or microseconds. Intraday traders utilize HFT strategies to take advantage of rapid price fluctuations. HFT algorithms analyze real-time tick data, identifying trading signals and executing orders at optimal prices. In practice, these algorithms, for example, react quickly to specific events, such as earnings announcements, economic data releases, or news articles, allowing traders to profit from short-term price movements. The speed and precision of HFT play a crucial role in achieving success in intraday trading.
Scalping. This strategy is a popular approach where traders aim to make small profits from multiple trades by capitalizing on small price movements. Unlike long-term strategies, scalping involves buying and selling trades within seconds or minutes. Scalping strategies often work futures contracts or on highly liquid stocks with tight bid-ask spreads. It’s advisable to avoid illiquid instruments since that may lead to price slippage and execution delays. In practice, scalping works best during strongly trending market conditions.
Statistical arbitrage. This trading strategy involves capitalizing on price differences by buying and selling the same or similar assets. The goal is to exploit inefficiencies in pricing across related assets. There are several common types of arbitrage, including pairs trading, a market-neutral approach, and risk arbitrage. Intraday traders employ these strategies to profit from short-lived discrepancies. For example, an algorithm might simultaneously purchase an undervalued stock while selling an overvalued stock in a correlated pair, with the expectation that their prices will eventually revert to their true values. Generally, this strategy can be an overnight-0 strategy, or it can hold assets over days or weeks before liquidating inventories at a favorable price.
Challenges in Intraday Trading
Most intraday trading requires careful parameter optimization and extensive backtesting. It faces challenges, however, related to optimization bias and overfitting. While backtesting provides insights, real-world performance can significantly differ. High-frequency strategies require a delicate balance between algorithmic complexity and robustness, especially factoring in returns, transaction fees and taxes.
High-frequency algorithms, while powerful, can exacerbate volatility and impact market dynamics. Backtesting often fails to capture these nuances and potential price slippage, where the executed price differs from the expected price. Increased volatility and rapid price fluctuations can be a double-edged sword. It creates opportunities, and also increases risks. Prices can swing wildly within seconds. Without discipline, traders can be caught off guard.
In trading, there’s also the chance of whipsaws. They are sudden reversals in price direction after a brief move in the expected direction. Traders receive these false signals, leading to losses without robust analysis and risk management. The fast pace of intraday trading can bring intense emotions. Greed and fear of loss often cloud judgements. Successful traders need to detach emotionally from trades, sticking to their strategies especially during adrenaline-fueled moments.
Position sizing and risk management are top priorities in high-frequency trading. Intraday traders must define risk tolerance and set stop-loss orders. Position sizing ensures that no single trade jeopardizes their capital significantly. Ignoring risk management can lead to fatal losses. Traders must protect their capital by setting realistic profit targets and adhering to risk limits. Capital preservation ensures long-term success in the market.
Lastly, ensuring technological reliability is critical. A single malfunction or downtime can lead to missed opportunities or financial losses. Moreover, accurate and timely data inputs are crucial for trading strategies, especially for micro-second scalping strategies. Flawed data often lead to poor trade decisions. Ensuring data integrity and quality is essential for reliable algorithmic performance.