Algorithmic Trading

Overview

Algorithmic trading uses computer programs and mathematical models to automate trade decisions and executions in financial markets. Algorithms react to price, volume, timing, and other inputs to place orders faster and more precisely than human traders, improving efficiency while introducing new operational and systemic risks.

How it changed markets

• Adoption began with electronic trading systems in the 1970s and accelerated as exchanges and networks modernized.
• By the 2000s, a large portion of trades in major markets were executed algorithmically.
• The rise of high-frequency trading (HFT) spurred investment in low-latency infrastructure and intensified competition based on speed.

Common types of execution algorithms

• Arrival price: Execute close to the price at order placement to minimize market impact.
• Basket/portfolio: Trade multiple securities with portfolio-level constraints (cash balance, risk, participation limits).
• Implementation shortfall: Minimize the difference between decision price and execution price.
• Percentage of volume (POV): Scale order size to maintain a target share of market volume.
• Single-stock: Optimize execution for one security using market conditions and order size.
• VWAP (Volume-Weighted Average Price): Aim to match the security’s VWAP over a given period.
• TWAP (Time-Weighted Average Price): Spread trades evenly over time to achieve a time-weighted average.
• Risk-aversion parameter: Adjust aggressiveness based on risk tolerance and objectives.

Example

A simple strategy: buy 100 shares when the 75-day moving average crosses above the 200-day moving average (a bullish crossover). An execution algorithm monitors those indicators and places the order automatically when the condition is met—removing emotion and the need for constant manual monitoring.

Black-box algorithms

• Definition: Systems (often using machine learning) whose internal decision processes are opaque—even to designers.
• Strengths: Can process vast, complex datasets and adapt to changing conditions, potentially outperforming rigid rule-based methods.
• Weaknesses: Lack of explainability raises accountability, compliance, and risk-management challenges. Opaque behavior can complicate legal and ethical responsibility and make it hard to diagnose failures.

Open source and crowdsourcing

• Open-source tools and platforms have lowered barriers, enabling individual developers and smaller firms to experiment and contribute algorithms.
• Some financial firms host competitions and share improvements; others remain cautious to protect proprietary strategies.
• Open collaboration has grown in data science and ML within finance but is balanced against concerns over intellectual property and operational security.

Benefits

• Speed: Much faster order placement and response to market events.
• Accuracy and precision: Reduced manual errors and highly specific execution conditions.
• Efficiency: Can operate continuously without fatigue, improving order completion.
• Emotion-free: Removes psychological biases from trading decisions.
• Backtesting: Strategies can be validated on historical data before live deployment.
• Reduced information leakage: Automated orders can conceal intent better than manual execution.

Risks and drawbacks

• System failures and technical glitches can produce large losses.
• Over-optimization to historical data (curve-fitting) may fail in live markets.
• Potential to exacerbate market volatility and systemic risk (e.g., flash crashes).
• Liquidity can evaporate quickly if many algorithms withdraw simultaneously.
• Compliance and regulatory requirements are evolving and may be costly to meet.
• Rigid execution can perform poorly during atypical or extreme events.

High-frequency trading (HFT) vs. algorithmic trading

HFT is a subset of algorithmic trading that emphasizes extremely high speed and very large numbers of transactions, often leveraging colocated servers, proprietary networks, and low-latency hardware. HFT strategies can execute in micro- or nanoseconds.

Getting started

• Learn programming (Python, C++, Java commonly used) and data handling.
• Develop or select a trading strategy and backtest it on historical data.
• Choose a brokerage or platform that supports algorithmic execution and provides APIs.
• Start with simulated or small-scale live trading, monitor performance, and iterate.
• Build robust monitoring, risk controls, and fail-safes to manage outages and unexpected behavior.

Capital and costs

• Required capital varies widely by strategy, market, and execution venue.
• Costs include development, data feeds, licensing, connectivity (low-latency or colocated options), and ongoing maintenance.
• Some strategies are accessible with modest capital; HFT typically requires substantial investment.

Conclusion

Algorithmic trading delivers speed, precision, and scale, transforming how markets operate and how trades are executed. Its advantages can reduce costs and human error, but the approach demands disciplined development, continuous monitoring, and strong risk controls to manage technical, regulatory, and systemic risks.

Further reading:
– SEC release on electronic trading reforms
– Michael Lewis, Flash Boys
– Fintech Open Source Foundation (FINOS) reports on open-source in financial services