Algorithmic Trading Explained: How It Works and the Risks It Creates

On May 6, 2010, US equity markets lost almost a trillion dollars of value in minutes, then recovered most of it just as quickly, in an event now known as the Flash Crash. The trigger and amplification mechanism involved automated trading algorithms interacting with each other faster than any human could observe or intervene. That single event reshaped how regulators, exchanges, and market participants think about algorithmic trading, not as a niche technical curiosity but as a force capable of reshaping market stability within minutes.

Algorithmic trading uses computer programs to execute trading decisions based on predefined rules, including timing, price, quantity, and market conditions, without requiring real time human intervention for each individual trade. It spans a wide spectrum, from relatively simple execution algorithms that break large orders into smaller pieces to minimize market impact, to sophisticated high frequency trading strategies that hold positions for fractions of a second and depend on microsecond level infrastructure advantages.

The Spectrum of Algorithmic Trading Strategies

The Flash Crash: A Case Study in Systemic Risk

On May 6, 2010, a large sell order in E-mini S&P 500 futures, executed by an algorithm without adequate regard for price or time, triggered a cascading sequence of events. Other algorithmic trading systems, designed to respond to unusual selling pressure, began selling in response, which in turn triggered further automated selling from other participants. High frequency trading firms, facing unusually erratic price behavior, rapidly withdrew from providing liquidity, removing a significant source of market depth at precisely the moment it was most needed to absorb the selling pressure. The combination produced a rapid, severe price collapse across major indices within minutes, followed by an equally rapid recovery once the triggering conditions subsided and normal trading resumed.

The regulatory investigation into the Flash Crash fundamentally changed how market structure regulators think about algorithmic trading risk. Rather than treating each algorithm as an isolated system, regulators recognized that the interaction effects between many independent algorithmic systems, each individually rational but collectively capable of producing emergent, destabilizing behavior, represented a systemic risk that existing market structure and circuit breaker mechanisms had not adequately anticipated.

Algorithmic Trading and Broader Market Risk Management

Algorithmic trading strategies exist within, and are shaped by, the broader risk frameworks that govern financial market participants. The market risk and liquidity risk distinctions covered in our guide on market risk versus liquidity risk are directly relevant to algorithmic trading, since the withdrawal of algorithmic liquidity providers during periods of stress, exactly what occurred during the Flash Crash, illustrates how algorithmic market participants can transform a manageable market risk event into an acute liquidity crisis for other market participants trying to execute trades during exactly the moment liquidity evaporates.

The portfolio management principles covered in our guide on portfolio management and Modern Portfolio Theory also connect directly to algorithmic trading, since many algorithmic strategies, particularly statistical arbitrage and systematic trend following approaches, are essentially automated implementations of portfolio construction and risk management principles applied at much higher frequency and scale than traditional discretionary portfolio management allows. Understanding both disciplines together gives finance professionals a more complete picture of how modern markets actually function, since the line between algorithmic execution and portfolio strategy has increasingly blurred as automation has extended deeper into every layer of investment management practice.

Infrastructure and the Economics of Speed

High frequency trading strategies depend on an infrastructure arms race that has fundamentally reshaped the economics of market access. Co-location, physically placing trading servers within the same data center as an exchange’s matching engine, reduces network latency to the physical limit imposed by the speed of light over the remaining cable distance, and firms compete intensely for the marginal microsecond advantage this proximity provides. Beyond co-location, firms have invested in increasingly exotic infrastructure, including microwave and laser transmission links between financial centers that transmit data marginally faster than fiber optic cable, purely to gain a speed advantage measured in single-digit milliseconds or less over longer distance routes such as between Chicago and New York trading venues.

This infrastructure investment has generated ongoing debate about whether the resources devoted to shaving fractions of a millisecond off execution speed represent genuine value creation for markets, through tighter bid-ask spreads and improved price discovery, or largely reflect a zero-sum competitive race among sophisticated participants that adds cost without proportionate benefit to the broader market and long-term investors who do not compete on this speed dimension. Some exchanges have experimented with mechanisms like randomized order processing delays specifically designed to reduce the value of pure speed advantages, reflecting genuine uncertainty within market structure policy circles about the optimal balance between rewarding technological sophistication and ensuring markets remain broadly fair and accessible to participants without HFT-scale infrastructure investment.

For most institutional and retail investors, the practical relevance of this speed race is indirect rather than direct. Few investors compete on microsecond execution timing themselves, but the market structure that high frequency trading and infrastructure competition have produced shapes the bid-ask spreads, execution quality, and liquidity conditions that every market participant experiences when placing an order, regardless of whether they are aware of the underlying algorithmic infrastructure making that execution possible.

Frequently Asked Questions

What is the difference between algorithmic trading and high frequency trading?
Algorithmic trading is the broad category of using computer programs to execute trading decisions based on predefined rules, encompassing strategies operating across timeframes from microseconds to months. High frequency trading is a specific, speed-focused subset of algorithmic trading, characterized by extremely short holding periods, very high trade volumes, and heavy investment in low-latency infrastructure to gain microsecond execution advantages over competitors.

Is algorithmic trading only used by large institutions?
While the most sophisticated, infrastructure-intensive strategies like high frequency trading require substantial capital investment generally only accessible to large institutions and specialized trading firms, algorithmic trading tools and platforms have become increasingly accessible to smaller institutional investors and even sophisticated retail traders, though typically without the extreme speed advantages that define the highest end of institutional high frequency trading.

What caused the 2010 Flash Crash specifically?
Regulatory investigation identified a large automated sell order in E-mini S&P 500 futures, executed without adequate regard for the price or time impact of the sale, as the triggering event. This interacted with existing market conditions and other algorithmic trading systems to produce a rapid cascading price decline, amplified by high frequency trading firms withdrawing liquidity provision during the period of extreme volatility rather than continuing to provide the market depth that might have absorbed the initial selling pressure.

Do circuit breakers fully prevent flash crash type events?
Circuit breakers and related market structure safeguards significantly reduce the likelihood and severity of Flash Crash style events by forcing trading pauses when price movements exceed defined thresholds, giving markets time to reassess rather than allowing algorithmic cascades to continue uninterrupted. However, they do not eliminate the underlying risk entirely, and smaller scale disruptive events have continued to occur in various markets since 2010, demonstrating that algorithmic trading risk remains an active area of ongoing regulatory attention.

How is artificial intelligence changing algorithmic trading?
Machine learning and AI techniques increasingly supplement or replace traditional rule-based algorithmic trading logic, allowing trading systems to identify complex, non-linear patterns in market data that traditional statistical models might miss. This evolution introduces new challenges around model interpretability and risk management, since AI-driven trading decisions can be more difficult for risk managers and regulators to fully understand and predict compared to traditional, explicitly rule-based algorithmic strategies.