Algorithm trading is a system of trading which facilitates transaction decision making in the financial markets using advanced mathematical tools. Description: In this type of a system, the need for a human trader’s intervention is minimized and thus the decision making is very quick.
In this type of a system, the need for a human trader’s intervention is minimized and thus the decision making is very quick. This enables the system to take advantage of any profit making opportunities arising in the market much before a human trader can even spot them.
As the large institutional investors deal in a large amount of shares, they are the ones who make a large use of algorithmic trading. It is also popular by the terms of algo trading, black box trading, etc. and is highly technology-driven. It has become increasingly popular over the last few years.
Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume. This type of trading attempts to leverage the speed and computational resources of computers relative to human traders.
Algorithmic trading involves three broad areas of algorithms: execution algorithms, profit-seeking or black-box algorithms, and high-frequency trading (HFT) algorithms. While not wholly separated in real-world applications, these are all automated processes for financial trades and decision-making that use price, timing, volume, and more, along with sets of rules, to tackle trading problems that might once have required a team of financial specialists.
》 Algorithmic Trading Types
The algorithms used in financial trading are rules or instructions designed to make trading decisions automatically. They range from simple single-stock to more complex black-box algorithms that analyze market conditions, price moves, and other financial data to execute trades at optimal times for the least cost-to-maximum profit ratio. The crossover of computer engineering and finance is notorious for its leaden jargon, so we won’t weigh you down with too many terms here. While some phrases might change slightly from one trading firm to the next, the following should give you an idea of the wide uses for algorithmic trading
▪︎ Basket algorithms: Also called portfolio algorithms, these execute orders while calculating the effects on other decisions and securities in a portfolio. For example, even if a security is available at the right price, the algorithm may decide to hold off trading if doing so would increase risk for the portfolio as a whole. Constraints put into the algorithm include cash balancing, self-financing, and minimum and maximum participation rates.
▪︎ Implementation shortfall algorithms: These automated rules aim to minimize implementation shortfall, the cost of executing an order when it differs from the decision price.
▪︎ Arrival price algorithms: These are designed to execute trades as close as possible to the stock price when the order was placed. These are useful for minimizing the market impact and the risk of price moves after the order is made.
▪︎ Percentage of volume : These algorithms adjust order sizes in reaction to real-time market trading volume. The purpose is to preserve a predetermined percentage of the total market volume, balancing market impact and timing.
▪︎ Risk-aversion parameter : This will vary depending on the trader and the strategies needed, but it’s often put alongside other algorithms to adjust trading aggressiveness based on the trader or client’s risk tolerance.
▪︎ Time-weighted average price (TWAP): These algorithms distribute trades evenly across a set period to attain an average price mirroring the time-weighted average of the stock price. They are employed to minimize market upheaval when putting in large orders.
▪︎ Volume-weighted average price (VWAP): These algorithms execute orders at a price that closely matches the volume-weighted average price of the stock over a specific period.
▪︎ Single-stock algorithms: These algorithms are designed to optimize the trade execution of a single security, considering factors like market conditions and order size.
》 KEY TAKEAWAYS
▪︎While it provides advantages, such as faster execution time and reduced costs, algorithmic trading can also exacerbate the market’s negative tendencies by causing flash crashes and immediate loss of liquidity.
▪︎Algorithmic trading has grown significantly since the early 1980s and is used by institutional investors and large trading firms for diverse purposes.
▪︎Black-box or profit-seeking algorithms can have opaque decision-making processes that have drawn the attention and concerns of policymakers and regulators.
▪︎Algorithmic trading involves employing process- and rules-based computational formulas for executing trades.
》 Advantages and Disadvantages of Algorithmic Trading
Advantages
▪︎ Speed and efficiency: Implicit in all the above advantages is how financial algorithms can execute orders far faster than humans, allowing traders to capitalize on market opportunities more quickly.
▪︎ Precision : Algorithmic trading enables the execution of orders in highly specified conditions while reducing the probability of human error.
▪︎ Potential for increased transparency: While black-box algorithms have raised issues of opaque processes when the operational details for execution algorithms are shared in advance, investors know exactly how their shares will be traded in the market.
▪︎ Market access: Algorithmic trading provides quicker access to markets and exchanges via high-speed networks. In addition, clients without these high-end systems can now take advantage of benefits like co-location and low-latency connections.
▪︎ Less information leakage: Since brokers do not receive detailed information about the investor’s orders or trading intentions, the risk of information leakage is reduced. Traders buying a security, for example, only need to communicate their trading needs and instructions through the selection and parameter settings of the algorithm.
▪︎ Anonymity: Trading is automated, with orders processed by computers and networks across platforms. This automation means that orders aren’t exposed or discussed openly on the trading floor as they used to be. In addition, certain algorithms can ensure that major trades are spread out to hide major transactions, which could reveal the parties involved in smaller sectors.
Disadvantages
▪︎Technological dependence: Reliance on computerized systems means glitches, connectivity issues, and system failures can lead to significant losses or missed opportunities.
▪︎ Systemic risk: This has been widely discussed among regulators and political representatives since this kind of trading began. For example, it’s feared that broadly using similar algorithms could increase systemic risk and market volatility, as seen in events like flash crashes. For example, on May 6, 2010, the Dow Jones Industrial Average, along with other indexes, had a sudden and abrupt drop, falling 1,000 points before rebounding quickly. The crash was initially triggered by a large sell order in the futures market, setting off a flurry of high-frequency trades.
▪︎ Price discovery challenges: The shift from traditional specialists and market makers to algorithm-based trading has complicated price discovery, especially at market openings. While algorithms efficiently include price information for strategizing, they can quickly struggle to determine a security’s fair market value.
▪︎ Rigidity in the face of events: Algorithms execute precisely as programmed, which can be problematic during market events they aren’t designed to handle, potentially leading to inferior performance and increased costs.
▪︎ Illiquidity: Another disadvantage of algorithmic trades is that it can cause liquidity to disappear quickly. Algorithmic trading was said to be a major factor in causing a loss of liquidity in currency markets after the Swiss franc discontinued its euro peg in 2015.
▪︎ Historically optimized: There’s a risk of creating complex algorithms that fit historical data but fail in real-market conditions
▪︎ Cost : Creating and executing algorithmic trading systems is a cost that not all firms can afford, and there are also ongoing fees for networking power, hardware, and applications.
▪︎ Complacency: Traders may become overly reliant on familiar algorithms, using them regardless of changing market conditions.