Algorithmic trading, also referred to as "Algo trading," is a trading process in which the trader purchases and sells financial instruments. Usually, traders set preset guidelines for the financial instruments to implement, and when these conditions are met in the markets, an order is executed. These conditions are based on specific market variables, such as price, and they govern when trades are opened and closed. Traders, for example, could set the directions to sell an item when the Relative Strength Index (RSI) indicator rises above 70 and buy it back when it falls below 30.
The software will then execute the orders based on the coded instructions. Algo trading is objective and automated, which eliminates the desire to actively check markets as well as subjective or emotional trading. Furthermore, since everything is handled automatically, you can devote more time to market research and technical analysis, as well as testing out new trading strategies. Algo trading, which is primarily used by institutional traders and market makers, can also be utilized in several other ways by retail traders, depending on their trading platform.
Algorithmic trading can be defined as the use of computer codes and operating systems to open and close trades based on predefined rules, such as market movements in an underlying market. Trading algorithms (algos) can implement a buy or sell order on your behalf if the current market conditions match any predetermined criteria, reducing your workload by eliminating the need to manually scan the markets.
To place a trade, algorithmic trading (also known as automated trading, black-box trading, or Algo-trading) employs a software program that needs to follow an outlined list of instructions (an algorithm). Timing, price, quantity, or any analytical method are used to define sets of instructions. Aside from profit opportunities, Algo-trading makes markets more liquid and buying and selling more systematic by removing the influence of human emotions on trading activities.
Algorithmic trading methods require making investment choices based on pre-programmed computer rules. When certain conditions are met, a merchant or investor needs to write code that executes trades on their behalf.
Moving average transaction algorithms are extremely popular and simple to implement. The algorithm buys a security, for example, a stock, if its current value is less than its average market price over a given period and sells a security if its current market price is greater than its average market price over that period.
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Advantages of Algorithmic Trading
Here are a few of the potential advantages of Algo trading:
1. Reduce impact on prices: A large trade has the potential to change the market price because it distorts; such a trade is known as a distortionary trade. To avoid this situation, traders generally open large positions that might also cause the market to move in steps. For example, an entrepreneur who wishes to purchase one million shares of Apple may do so in 1,000-share increments. The investor could buy 1,000 shares every five minutes for 60 minutes and then assess the impact of the trade on Apple stock prices. If the price does not change, the investor will proceed with his purchase.
This strategy enables the investor to purchase Apple stock without raising the price. However, the strategy has its drawbacks, in that if the investor is required to pay a fixed fee for each transaction, the strategy may incur high transaction costs. It then requires a massive amount of time to accomplish the strategy. Therefore, if the investor buys 1,000 shares every five minutes, the transaction would take him just over 83 hours (more than three days).
A trading algorithm can resolve the issue by purchasing shares and instantly determining whether the purchase has affected the market price. This simply means it can decrease both the number of transactions required to complete the trade and the time required to complete the trade.
2. Ensures rule-based decision-making: Brokers and investors are frequently convinced by sentiment and emotion, and their trading strategies are ignored. For example, in the run-up to the 2008 Global Financial Crisis, financial markets indicated that a crisis was looming. However, many investors ignored the warning signs because they were engrossed in the mid-2000s "bull market frenzy" and did not believe a crisis was possible. Algorithms, therefore, resolve the issue by helping to ensure that all trades follow a set of rules.
3. Algorithm trading decreases the risk of human error, and automated trading tends to execute precisely when trading instructions are met, lowering the risk of human error.
4. Algorithm trading prevents you from acting emotionally; the algorithm is unaffected by emotional swings, which could impair its validity when attempting to make trading decisions.
5. Automated trading is quick and efficient because the software monitors each market movement on a large scale and executes orders instantly.
6. Algorithm trading can be tested. You can put your algorithmic trading strategies to the test by using previous price data to see how well they work.
The Disadvantage of Algorithmic Trading
Missed trades: A trading algorithm may miss out on trades because the latter does not display any of the signs the algorithm has been programmed to look for. To some extent, missed trades can be reduced by simply increasing the number of indicators the algorithm must also look for, but such a list can never be exhaustive.
What percentage of trading is performed by algorithms?
According to Wall Street data, Algo trading accounts for only 60% to 73% of US equity trading activity, and the importance of speed and efficiency cannot be overstated.
Although following the minute-by-minute market can be tedious, time-consuming, and costly, algorithmic trading software solves these problems and can also open and close trades almost instantly, allowing you to capitalize on even the most volatile opportunities. Furthermore, algo trading helps remove emotion from trading and does not get upset when a trade fails, nor does it get overjoyed when things go well. Unlike humans, whose judgment may be clouded by emotions, algorithms simply eliminate emotional bias from trading.
Popular trading strategies based on algorithms
You can use a variety of algorithmic trading strategies, the most popular of which are:
1. Trend-following: This strategy generates trading signals based on technical indicators such as moving averages and breakouts, with the primary objective of acting in response to market trends.
2. Arbitrage: Arbitrage is the process of buying and selling the same resource on two different markets at the same time, and the difference in prices represents the trader's profit or loss.
3. Index fund rebalancing: Index funds are used to adjust holdings regularly to match the values reflected by indices. Trading algorithms can then be employed to make quick moves during this brief period of readjustment.
4. Price action: Algorithms can be programmed to buy or sell if prices rise or fall above or below a certain threshold. This is especially useful when processing a large number of orders.
Conclusion
Trading algorithms is not a sure method for making money. However, it is a useful tool for automated trading that increases the performance of your strategies. It can also be a useful addition to your general trading strategy when paired with the appropriate risk management measures.
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