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.
1. Trend following
This strategy involves identifying and following market trends based on historical data. It assumes that a market will continue moving in its current direction, prompting traders to align their positions accordingly. By analysing past price movements, traders aim to capitalise on sustained trends rather than predicting reversals.
2. Arbitrage
Arbitrage exploits price differences for the same asset across multiple markets. Traders use advanced algorithms to simultaneously buy and sell an asset at varying prices, generating returns from these discrepancies. Due to the speed required, arbitrage strategies often rely on automated systems for rapid execution.
3. Mean reversion
This strategy operates on the principle that asset prices tend to revert to their historical average over time. Traders identify price deviations from the long-term mean and expect that the price will eventually return to its average level. This method is particularly effective in markets with frequent fluctuations.
4. Index fund rebalancing
Index funds periodically adjust their holdings to match benchmark indices. Traders using this strategy anticipate these adjustments and position their trades accordingly. Large-scale buying or selling by index funds can influence market movements, creating opportunities for traders who predict these shifts accurately.
5. Market timing
Market timing strategies involve analysing various indicators to determine optimal trade entry and exit points. By assessing market conditions, traders aim to enhance returns by executing trades at the most advantageous moments. This approach requires a deep understanding of market behaviour and the ability to adapt quickly to changes.
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
Difference Between Online And Offline Trading
What Is Commodity Trading
