Algorithmic trading, while offering significant potential benefits, is not without its inherent challenges. Traders in capital markets typically employ trading approaches that best suit their individual styles and risk tolerances. For example, a swing trader might specialise in identifying setups with high potential for breakouts. However, even highly effective strategies have limitations. A swing trader seeking a breakout may be forced to endure periods of prolonged consolidation, requiring patience and discipline. It's crucial to recognise that every trading approach, including algorithmic trading, has its own set of advantages and disadvantages.
What is algo trading, and how can you use this technique? In this article, we will take you through the meaning of algo trading, the various algo trading benefits, and the different strategies you can use to automate your trades.
What is algorithmic trading?
Algorithmic trading leverages computer programs executing pre-defined instructions (algorithms) to automate trade execution. This approach enables trading at speeds and frequencies surpassing human capabilities, potentially maximizing profit generation. These algorithms incorporate factors such as timing, price, quantity, and mathematical models to guide trading decisions. Beyond profit enhancement, algorithmic trading contributes to increased market liquidity and promotes a more systematic and objective trading environment by minimising the influence of human emotions on trading activities.
Examples of simple trading algorithms
Algorithm 1: GBP/USD Shorting Strategy
- Trigger: Initiate a short position of 20 lots of GBP/USD when the currency pair's price exceeds 1.2012.
- Scaling rules:
- Price increase: Cover the short position by 2 lots for every 5-pip increase in the GBP/USD price.
- Price decrease: Increase the short position by 1 lot for every 5-pip decrease in the GBP/USD price.
Algorithm 2: AAPL stock trading strategy
- Trigger: Execute a buy order for 100,000 shares of Apple (AAPL) stock when the price falls below 200.
- Scaling rules:
- Price increase: Purchase an additional 1,000 shares for every 0.1% increase in the AAPL stock price above 200.
- Price decrease: Sell 1,000 shares for every 0.1% decrease in the AAPL stock price below 200.
How algorithmic trading works?
1. Trade criteria
The trading strategy employed involves the following criteria:
- Buy signal: Initiate a long position (purchase 50 shares) when the 50-day moving average of the stock price surpasses the 200-day moving average.
- Sell signal: Liquidate the existing position (sell all shares) when the 50-day moving average falls below the 200-day moving average.
2. System implementation
This trading strategy is executed through an automated system. The system continuously monitors the stock price and calculates the 50-day and 200-day moving averages in real-time. Upon detection of the specified buy or sell signal, the system automatically places the corresponding order.
3. Benefits
This automated approach eliminates the need for manual price monitoring, chart analysis, and order placement. By identifying and capitalizing on trading opportunities algorithmically, the system enhances efficiency and reduces the potential for human error.
Strategies for algo trading
If you are a seasoned trader, you may already be familiar with various manual trading strategies. Many of these techniques can also be used in algo trading. Let’s take a closer look at how to do algo trading using some popular trading strategies.
- Mean reversion
Mean reversion is a strategy based on the assumption that every asset has a mean or average price — and that its market price will converge or revert to this mean price over time. You can use algo trading to leverage the temporary highs or lows of an asset before its price reverts to the mean range. - Scalping
Scalping is the process of making multiple trades in the same asset within a short period to profit from minor price changes. When done right, it could result in numerous small profits that add up to significant gains. Scalping works best in highly volatile markets, and algorithmic trading helps you execute trades swiftly in such uncertain conditions. - Arbitrage
The arbitrage trading strategy involves selling and buying the same asset in different market segments to take advantage of the minor price differences between the two. With algo trading, you can identify opportunities for arbitrage and take advantage of them promptly before the prices align in the two market segments. - Momentum trading
Momentum trading works best when there is a strong and definite trend in the market. You can capitalise on this trend and exit your position when a trend reversal occurs. Algorithmic trading makes it easier to identify such strong trends and exit at the point of trend reversal efficiently. You can integrate technical indicators into the algorithm to exit the trade at the right time. - Index fund rebalancing
Index funds are required to align their asset portfolios to match the benchmark index they track. As a part of the rebalancing effort, these funds may sell or buy stocks and securities in large volumes, creating minor market trends. With algorithmic trading, you can identify such opportunities and leverage them to make quick profits.
Benefits of algo trading
Now that you know what algo trading is and how to do algo trading using common trading strategies, let’s take a closer look at the top benefits of algo trading. These benefits include the following:
- High-speed execution of high-volume orders
- Reduced transaction costs in the long run
- Immunity to sudden price changes since the orders are executed in milliseconds
- Automated and highly accurate trades that are not prone to human errors
- Opportunity to take advantage of rapid price fluctuations in the market
Disadvantages of algorithmic trading
Drawbacks of algorithmic trading are as follows:
- Latency: Algorithmic trading necessitates rapid execution speeds and minimal latency to prevent missed opportunities or financial losses due to delayed trade execution.
- Black swan events: Algorithmic trading relies on historical data and mathematical models to predict market trends. However, unforeseen market disruptions, commonly referred to as "black swan events," can lead to significant losses for algorithmic traders.
- Technological dependence: Algorithmic trading heavily relies on technology, including computer programs and high-speed internet connections. Technical issues or failures can disrupt trading processes and result in financial losses.
- Market impact: Large algorithmic trades can significantly impact market prices, potentially leading to losses for traders who cannot adjust their positions accordingly. Moreover, algorithmic trading has been implicated in increased market volatility, including instances of "flash crashes."
- Regulatory compliance: Algorithmic trading is subject to various regulatory requirements and oversight, which can be complex and time-consuming to adhere to.
- High capital costs: The development and implementation of algorithmic trading systems can be expensive. Traders may also incur ongoing fees for software and data feeds.
- Limited customisation: Algorithmic trading systems operate based on predefined rules and instructions, potentially limiting traders' ability to tailor their trades to specific needs or preferences.
- Lack of human judgment: Algorithmic trading relies on mathematical models and historical data, neglecting subjective and qualitative factors that can influence market movements. This absence of human judgment can be a disadvantage for traders who prefer a more intuitive or instinctive trading approach.
Conclusion
You need the right platforms and tools to fully leverage the many algo trading benefits. Today, many leading stockbrokers offer algo trading apps to help retail traders automate their trading strategies. However, before you use these tools, you must become well-acquainted with how to do algo trading in different market conditions.
Related articles
How To Do Bank Nifty Intraday Options Trading
What Is The Difference Between Demat And Trading Account
How To Use Pivot Point In Intraday Trading
