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Algorithmic Trading - What Is, Working, and Examples

Algorithmic Trading - What Is, Working, and Examples

In this blog, we will explain how algorithmic trading works and the strategies it uses. We’ll explore the advantages and disadvantages linked to this approach, supported by examples to enhance comprehension of its principles.

Table of Contents

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What is Algorithmic Trading?

Algorithmic trading, also referred to as automated trading, black-box trading, or algo trading, involves the use of computer programs designed to execute specific instructions for trading. It tries to generate profits at a frequency that surpasses human capabilities. The main goal of any algorithmic trading strategy is to identify opportunities that can improve earnings or reduce costs in the financial markets. 

The strategies involved in algorithmic trading rely on predefined sets of rules, often grounded in mathematical models that consider factors like timing, price movements, quantities, and other relevant market indicators. By adhering to these rules, algorithmic trading seeks to optimize trading decisions and execution.

However, algorithmic trading isn’t solely profit-driven. It significantly impacts market dynamics by improving market liquidity—the ease of buying or selling assets—and standardizing trading practices. In addition, algorithmic trading ensures that trades are executed based on logic and predetermined parameters rather than emotional reactions.

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How Does Algorithmic Trading Work?

Let’s explore the process of algorithmic trading using an example.

Consider a trading algorithm for a well-known cryptocurrency like Bitcoin. The tactic is to keep track of price changes at frequent intervals, say every 5 minutes. The algorithm might automatically place a buy order if the price of Bitcoin exhibits an upward trend for three successive periods, foreseeing a probable price increase. The program or the algorithm may initiate a sell order to minimize potential losses if the price experiences a continuous drop over three periods.

How Does Algorithmic Trading Work

The steps involved in the whole process mentioned above are as follows:

Step 1: Gather Bitcoin price information in real time

Step 2: Keep track of price variations every 5 minutes

Step 3: Recognize recurring upward or downward trends as patterns

Step 4: If an upward trend persists for three successive intervals, indicating a potential profit, put in a purchase order.

Step 5: Execute a sell order to limit possible losses if a downward trend continues for three successive periods.

In this instance, traders can benefit from transient price changes in the Bitcoin market using algorithmic trading, which involves quick data processing and automated decision-making. By enabling traders to respond quickly to changes in the market, these algorithms can improve the overall effectiveness of their trading tactics.

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Algorithmic Trading Strategies

Algorithmic Trading Strategies

Algorithmic trading strategies aim to identify profitable opportunities for improved earnings or cost reduction. Some common strategies include the following:

  1. Trend-Following Strategies: These strategies involve following trends in moving averages and price levels without needing to predict market trends. Traders often use the 50- and 200-day moving averages to implement this strategy.
  2. Arbitrage Opportunities: Traders make profits by purchasing stocks at a lower price and selling them at a higher price in different markets where the prices differ.
  3. Index Fund Rebalancing: Algorithmic traders benefit from expected trades during index fund readjustments, aiming to take advantage of profit opportunities resulting from these changes in the index.
  4. Mathematical Model-Based Strategies: Using proven mathematical models, traders employ strategies such as delta-neutral trading to balance multiple positions and reduce risks.
  5. Trading Range (Mean Reversion): This strategy involves identifying price ranges where assets typically revert to their average value, allowing for automated trades when prices move in and out of these ranges.
  6. Volume-Weighted Average Price (VWAP): Traders break up large orders into smaller ones based on stock-specific historical volume profiles, aiming to execute orders close to the volume-weighted average price.
  7. Time Weighted Average Price (TWAP): Traders divide large orders into smaller ones over evenly spaced time intervals, aiming to execute orders close to the average price during the specified time period.
  8. Percentage of Volume (POV): This strategy involves sending partial orders based on the defined participation ratio and market volume until the entire trade order is filled.
  9. Implementation Shortfall: Traders aim to minimize execution costs by trading in real-time markets and adjusting participation rates based on stock price movements.

Example of Algorithmic Trading

Let’s understand algorithmic trading with the help of an example.

Suppose an investor is using a computer program to trade stocks actively. The program keeps a close watch on the prices of TechCorp and CyberSolutions in different stock markets. Let’s consider the U.S. (NASDAQ) and the UK (LSE). Because the U.S. market opens before the UK market, the program takes advantage of the time difference to spot any significant price differences. Here it should be noted that the time difference affects the price difference because when one market opens before another, it creates an opportunity for prices to adjust based on new information, supply and demand, and trading activity.

If the program sees that one market has a much cheaper price for these stocks, considering things like transaction costs and changes in the U.S. dollar to British pound exchange rate, it automatically performs some appropriate action. It buys the stock where it’s cheaper and sells it where it’s more expensive. The goal is to make money by making the most of these price differences.

However, being successful in this kind of trading is about more than just having a fast and accurate program. It’s also about dealing with risks like sudden changes in the market, delays in making trades, and technical issues. Experienced traders understand the importance of using advanced programs and fast trading systems, along with plans to manage risks effectively. This helps them navigate the complexities of global markets, seize trading opportunities, and deal with challenges, all with the aim of achieving success and making profitable trades in the dynamic world of algorithmic trading.

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Algorithmic Trading Software

Large trading firms, such as hedge funds, investment banks, and trading firms, dominate the field of algorithmic trading. Experienced traders and quantitative analysts (quants) commonly use algorithmic trading at an individual level. Those less proficient in technology among traders often buy pre-built algorithmic trading software from brokers or external vendors. On the other hand, quants, having a strong understanding of both trading and programming, create their own trading software. In the following section, some of the top algorithmic trading softwares are listed:

Zerodha Streak

Zerodha Streak is an algorithmic trading platform offered by Zerodha, allowing traders to create, and deploy algorithmic trading strategies in real-time. It provides a user-friendly interface with a visual strategy builder which enables both new and experienced traders to design and implement complex trading strategies without the need for any programming knowledge.

Algo Trader

Algo Trader is a software or system that automates trading strategies. It executes buy or sell orders on financial markets based on predefined criteria, rules, or algorithms. This system can analyze market data, execute trades, and manage portfolios at a very high frequency in a short time, improving overall trading efficiency.

Robo Trader

Robo Trader is an advanced algo trading software that uses the Scalper propulsion strategy for gaining quick profits from short-term price movements. It employs multiple strategies simultaneously for effective risk management. It offers the flexibility to shift between automated and manual trading based on market conditions and personal preferences.

Meta Trader

Meta Trader provides a user-friendly interface with diverse tools and indicators for rich trading experience. You can benefit from Expert Advisors for accurate entry and exit guidance, advanced charting, and analytical tools for making trading decisions. It lets you trade across multiple markets with MT4 and MT5 versions. It utilizes powerful back-testing and optimization tools for strategy assessment and improved performance.

ACE FundTech

ACE FundTech is a software that helps in automated investment in companies with strong financials while considering technical evaluations as well for optimal investment decisions. This software can efficiently pick stocks using financial data and technical analysis. It can also automatically create diverse portfolios, and track portfolios 24/7. Moreover, it can achieve better returns compared to index performance. One can start trading with this software with a minimum investment of Rs. 1 lakh.

Advantages and Disadvantages of Algorithmic Trading

Advantages and Disadvantages of Algorithmic Trading

Algorithmic trading offers numerous benefits but also comes with its own set of drawbacks. Exploring the advantages and disadvantages of this trading approach provides a comprehensive understanding of its impact on financial markets and traders.

Advantages of Algorithmic Trading

First, let’s take a look at the advantages of algorithmic trading:

  1. Reduces Market Impact: Algorithmic trading minimizes the potential market impact of large trades, avoiding sudden price changes. By breaking down a large trade into smaller orders, the algorithm executes trades more strategically, preventing market disruption and excessive price fluctuations.
  2. Enforces Consistent Strategies: Emotions often influence human decision-making, leading to impulsive trading decisions. Algorithmic trading adheres to predefined rules and strategies, ensuring consistent and disciplined trading practices. It helps in avoiding impulsive actions driven by fear or excitement, leading to more rational investment decisions.
  3. Increases Trade Speed and Efficiency: Automated trading algorithms can execute trades at a much faster pace compared to manual trading, leading to improved trade efficiency and timely order placement. It enables traders to capitalize on market opportunities promptly and seize profitable trade positions without delay.

Disadvantages of Algorithmic Trading

Being aware of possible risks associated with algorithmic trading helps save money and maintain credibility in the dynamic financial markets. Some of the major drawbacks of algorithmic trading are discussed below:

  1. Potential Missed Opportunities: Trading algorithms may miss potential trading opportunities that do not align with their pre-programmed indicators. The rigidity of the algorithm’s criteria can lead to missed profitable trades that do not fit within the specific parameters set by the algorithm, potentially resulting in lost investment opportunities.
  2. System Vulnerabilities and Technical Glitches: Algorithmic trading systems are susceptible to technical issues and glitches, including system failures, network connectivity problems, and data inaccuracies. These vulnerabilities can lead to significant financial losses if not managed effectively, emphasizing the importance of robust risk management protocols and continuous monitoring of the trading system’s performance.
  3. Overdependence on Historical Data: Algorithms heavily rely on historical data and predefined patterns to make trading decisions. In rapidly changing market conditions or during unprecedented events, historical data may not accurately predict future market behavior, leading to suboptimal trading decisions and potential financial losses.

By considering these factors, traders can better understand the implications of using algorithmic trading and implement effective risk management strategies to optimize their trading activities.

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Algorithmic trading has revolutionized the financial landscape by automating trade execution through sophisticated computer programs. By leveraging mathematical models and high-speed computations, traders can swiftly analyze market data and capitalize on fleeting opportunities. Although it offers advantages such as minimized market impact, consistent strategies, and increased trade efficiency, it also poses challenges, including potential missed opportunities, system vulnerabilities, and overdependence on historical data. Therefore, implementing robust risk management practices and staying vigilant are crucial for navigating the complexities of algorithmic trading and maximizing its potential benefits.

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About the Author

Vice President

With an MBA in Finance and over 17 years in financial services, Kishore Kumar boasts expertise in program management, business analysis, and change management. Notable roles include tenure at JPMorgan, Nomura, and BNP Paribas. He is recognized for commitment, professionalism, and leadership.