Algorithmic Trading vs Active Investment: Which Strategy Wins?

This article concisely examines algorithmic trading and active investment strategies, emphasizing real-world scenarios where these approaches consistently generate positive returns. It also explores the ideal frequencies for executing algorithmic trades and active investment actions.

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Key Points

• Exploring the Dichotomy: Algorithmic Trading vs. Active Investment Strategies
• The Most Optimal Algorithmic Trading Strategies
• When Active Investment Outperforms Algorithmic Trading
• Optimal Trading Frequencies for Algorithmic and Active Investment Strategies

Exploring the Dichotomy: Algorithmic Trading vs. Active Investment Strategies

In the realm of financial markets, algorithmic trading and active investment represent two distinct methodologies that cater to different investor needs and preferences. Each approach brings its own set of strengths and challenges to the table. Algorithmic trading is renowned for its speed and operational efficiency, whereas active investment offers the flexibility to leverage in-depth analysis and human insight for potentially greater returns. Here, we examine the core attributes of these two strategies:

Algorithmic Trading

Automated Execution: This strategy uses computer algorithms to automatically execute trades based on specific, predefined criteria. High-frequency trading (HFT) is a subset of this approach, characterized by executing many orders at incredibly rapid speeds. Efficiency and Speed: Algorithms can process enormous data sets and execute trades within milliseconds or microseconds, making them exceptionally adept at capturing minute price differences. Data-Intensive: Algorithmic trading relies on real-time data feeds, technical indicators, and occasionally news events to inform trading decisions. It employs complex mathematical models and artificial intelligence. High Volume, Low Margin: Typically, algorithmic trading involves large trade volumes with narrow profit margins per transaction, aiming to exploit numerous small opportunities. Emotion-Free Execution: By removing human emotion from the equation, algorithms deliver a level of consistency in performance that can be difficult to achieve otherwise.

Active Investment

Human Judgment: This approach is characterized by the involvement of human traders or fund managers who make decisions based on thorough research, analysis, and instinct. It necessitates continuous market observation. Extended Horizons: Active investors often maintain positions over longer durations, from days to years, seeking to capitalize on significant market movements. Fundamentally Driven: The strategy leans heavily on fundamental analysis, assessing corporate financial health, industry trends, and broader economic factors. Adaptive Flexibility: Human traders can adjust to evolving market conditions and make discretionary choices that may not be within the scope of algorithmic programming. Emotional Influence: While human emotions can affect trading decisions, they can also serve as an advantage or disadvantage, contingent on the context.

As of today, the market size of active investing is substantial, with estimates suggesting it accounts for approximately?$15 trillion?globally. This includes various asset classes and investment strategies managed by active fund managers who aim to outperform market benchmarks through selective stock picking and strategic asset allocation.

Main Assets Used for Active Investment Strategies in 2025

1. Equities: Equities remain a cornerstone of active investment strategies. Active managers focus on individual stock selection, aiming to identify undervalued companies or those with strong growth potential.
2. Fixed Income: Active management in fixed income involves selecting bonds based on credit quality, interest rate outlook, and economic conditions. This includes government bonds, corporate bonds, and municipal bonds.
3. Alternative Investments: These include hedge funds, private equity, real estate, and commodities. Active managers use these assets to diversify portfolios and seek higher returns through specialized strategies.
4. International Stocks: Investing in international markets allows active managers to capitalize on global economic trends and opportunities outside their home country.
5. Sector-Specific Investments: Active managers often focus on sectors such as technology, healthcare, or energy, where they believe they have an informational edge or see significant growth potential.

The Most Optimal Algorithmic Trading Strategies

Over time, several algorithmic trading strategies have emerged as particularly effective. Among the most reliable are:

• Trend-Following: This strategy thrives in trending markets by capitalizing on significant price movements. Algorithms are designed to identify and follow market trends, thus capturing potential gains.
• Mean Reversion: Suitable for stable markets, this approach involves betting that prices will return to their historical averages. It is particularly effective in less volatile environments.
• Arbitrage: By exploiting price discrepancies between markets or instruments, this strategy requires rapid execution and is most effective in highly liquid markets.
• Statistical Arbitrage: Utilizing statistical models, this strategy identifies and capitalizes on price inefficiencies. It often involves pairs trading and demands sophisticated modeling techniques.

Before selecting the most suitable strategy, consider the following critical factors:

• Market Environment Understanding: Volatility: High volatility can favor strategies like trend-following and momentum trading, whereas low volatility may benefit mean reversion approaches. Liquidity: Ensure the assets have adequate liquidity to minimize slippage and guarantee smooth execution. Historical Data: Engage in extensive backtesting using historical data to evaluate how the algorithm would have performed under various market conditions. Optimization: Continuously refine your algorithms based on backtesting outcomes. Ensure robust performance on out-of-sample data to prevent overfitting.

Real-World Example: Developing an Algorithmic Trading Strategy for Apple Inc. (AAPL)

Apple Inc. (AAPL) serves as an exemplary case for exploring algorithmic trading strategies. Given its status as one of the most actively traded stocks with extensive historical market data, it is an ideal candidate for a systematic trading approach.

Strategy Selection: Mean Reversion In light of Apple’s consistent price movements and substantial liquidity, a mean reversion strategy can be particularly effective. This approach is predicated on the assumption that Apple’s stock price will revert to its historical average over time.

Algorithm Development

• Data Collection: Compile historical price data for AAPL, encompassing daily open, high, low, and close prices, as well as trading volume.
• Technical Indicators: Employ indicators such as Bollinger Bands, Relative Strength Index (RSI), and Moving Averages to pinpoint overbought and oversold conditions.
• Entry and Exit Rules: Entry: Initiate a buy when the stock price reaches the lower Bollinger Band and the RSI indicates oversold conditions (e.g., RSI < 30).Exit: Execute a sell when the stock price returns to the middle Bollinger Band or the RSI reflects neutral conditions (e.g., RSI between 40 and 60). Stop-Loss: Implement a stop-loss order at 2% below the entry price to manage potential losses. Take-Profit: Establish a take-profit order at 5% above the entry price to secure gains.

Backtesting

• Historical Data: Conduct backtesting using historical AAPL data to assess the strategy’s performance across various market conditions.
• Performance Metrics: Evaluate key metrics, including the Sharpe ratio, maximum drawdown, and total return, to determine the strategy’s efficacy.

Optimization

• Parameter Tuning: Refine the parameters of the technical indicators (e.g., Bollinger Bands periods) to enhance the strategy’s effectiveness.
• Out-of-Sample Testing: Validate the optimized algorithm on out-of-sample data to ensure its robustness in new market scenarios.

Live Trading

• Paper Trading: Begin with paper trading to simulate real-world trading without financial risk, allowing for real-time adjustments.
• Deployment: Upon verifying the algorithm’s reliability, proceed to deploy it for live trading on a dependable platform with API support.

Let’s consider that you want to achieve a daily profit of $200-300 through algorithmic trading; it’s essential to consider the average profit per trade and the trading volume required. Here are some steps to determine the optimal volume:

Determine Average Profit Per Trade

• Average Trade Profit: Let’s assume an average profit of $0.10 per share per trade, which is a reasonable estimate for many algorithmic trading strategies.

Calculate Required Trading Volume

• Daily Profit Goal: To achieve $200-300 per day, you need to trade enough shares to reach this profit target.
• Volume Calculation: For $200 daily profit:?$200/$0.10=2,000?shares per day. For $300 daily profit:?$300/$0.10=3,000?shares per day.

Example Calculation

If you trade?Apple Inc. (AAPL), which has a high average daily trading volume and sufficient liquidity:

• Stock Price: Assume AAPL is trading at $232.
• Required Volume: For $200 daily profit:?2,000 shares×$232=$464,000?daily trading volume. For $300 daily profit:?3,000 shares×$232=$696,000?daily trading volume.

By focusing on stocks with high liquidity and moderate volatility, you can optimize your algorithmic trading strategy to achieve your daily profit goals. While algorithmic trading offers the potential for consistent profits, it also comes with risks. The probability of success depends on the robustness of the strategy, market conditions, and effective risk management. Losses are an inherent part of trading, and even the best algorithms will experience them.

Today, algorithmic trading continues to dominate a significant portion of the financial markets. It accounts for approximately?70-75% of all U.S. equity trading. This high market share reflects the growing reliance on automated systems for executing trades efficiently and at high speeds.

Most Traded Asset Classes in Algorithmic Trading (2025)

1. Equities: Equities remain the most traded asset class in algorithmic trading, contributing significantly to the overall market share. Algorithms help manage large volumes and execute trades with minimal human intervention.
2. Futures: Futures contracts are also heavily traded using algorithms. The ability to quickly respond to market movements and execute trades based on pre-set criteria makes algorithms ideal for futures trading.
3. Options: Options trading benefits from algorithmic strategies that can handle complex calculations and execute trades based on various market conditions and volatility.
4. Fixed Income: Although traditionally less automated, the fixed income market has seen a rise in algorithmic trading, particularly for government and corporate bonds.
5. Commodities: Algorithmic trading is increasingly used in commodities markets, where speed and precision are crucial for capturing price movements.

When Active Investment Outperforms Algorithmic Trading

While algorithmic trading offers numerous benefits, there are certain scenarios where active investment can yield superior returns. Here are situations where active management shines over algorithms:

1. Market Anomalies and Inefficiencies: Active investors can take advantage of market inefficiencies and anomalies that might elude algorithms. Human judgment is often more adaptable and responsive during market stress or unusual events than pre-set algorithms. A bright example of the stock market anomalies during which the algo trading models would typically fail is the following: Nvidia experienced a significant decline in January 2025 following the announcement of a new AI application by Chinese-based DeepSeek AI.? Another example is the current tariff-driven volatility in the stock market. The stock market has been experiencing volatility due to new tariff threats from President Trump. These tariffs, particularly on steel and aluminum imports, have created uncertainty and impacted various sectors differently. For instance, US steel companies saw a boost, while other sectors faced potential cost increases. In such cases, algorithmic trading might result in losses, mainly if a trader does not supervise the trading hourly or more frequently.
2. Complex and Illiquid Markets: Active investors can use their expertise and local knowledge to inform decisions in less liquid or complex markets, such as specific emerging markets or niche sectors. Algorithms may face challenges due to the scarcity of data or unique market characteristics. Especially in the case of IPOs or other deals related to M&As with a lack of information and historical data, active investment strategies play a critical role. Example: DGNX (Diginex Limited), which went public in January 2025, and the shares jumped by 451.2% YtD.
3. Long-Term Strategic Investments: For long-term strategic investments, deep research and a comprehensive understanding of a company’s fundamentals are vital. Human investors can evaluate qualitative aspects like management quality, corporate governance, and industry trends, which algorithms may find difficult to quantify.
4. Ethical and Socially Responsible Investing: Active investors can more effectively integrate ethical, social, and governance (ESG) criteria into their investment decisions. They can engage with companies on ESG issues and make nuanced choices based on a broader set of values.
5. Regulatory and Compliance Considerations: In heavily regulated industries or regions with strict compliance requirements, active investors are better equipped to navigate the regulatory environment. They can adapt to new regulations and ensure compliance more flexibly than algorithms.
6. Sentiment and Behavioral Analysis: Although algorithms can process vast amounts of data swiftly, they may not always grasp the nuances of market sentiment and investor behavior. Active investors can use their intuition and experience to interpret market sentiment and base their decisions on behavioral insights.

Optimal Trading Frequencies for Algorithmic and Active Investment Strategies

Determining the best trading frequency for algorithmic and active investment strategies hinges on various factors, including market conditions, the specific approach used, and individual investment goals. Below are key insights into these strategies:

Algorithmic Trading Frequencies:

• High-Frequency Trading (HFT): This method involves executing thousands of trades per second to exploit minute price movements. While potentially lucrative, HFT demands advanced technological infrastructure and specialized expertise.
• Intraday Trading: This strategy focuses on executing multiple trades within a single trading day to leverage short-term market fluctuations. It is most effective when supported by robust algorithms and real-time data analysis.
• Swing Trading: Positions are held for several days to weeks, aiming to capture short- to medium-term market movements. This approach strikes a balance between frequent trading and capturing larger price shifts.

Active Investment Strategy Frequencies:

• Monthly to Quarterly Adjustments: Active investors seeking double-digit returns may find monthly to quarterly portfolio adjustments beneficial. This frequency allows for the integration of new information and market trends while minimizing excessive trading costs.
• Event-Driven Strategies: Trades are based on specific events such as earnings reports, mergers, or economic data releases. While the frequency varies, it typically involves several trades per month.
• Sector Rotation: This approach involves reallocating investments among sectors in response to economic cycles and market conditions, with adjustments usually occurring quarterly or semi-annually.

Both algorithmic and active investment strategies necessitate a profound understanding of market dynamics and a clearly defined strategy. Algorithmic trading emphasizes the use of technology and data analysis, while active investment requires staying informed about market trends and economic indicators.

In my next research articles, we will examine real-world cases of artificial intelligence being used in active investment strategies. Please stay tuned with me!