Key Algorithmic Trading Strategies

Why This Matters

Algorithmic trading isn't just about speed—it's about understanding why certain strategies work and when they're most effective. You're being tested on the underlying logic that drives automated trading: concepts like market efficiency, price discovery, liquidity provision, and statistical relationships between assets. These strategies represent different bets on how markets behave, from the assumption that prices revert to historical norms to the belief that trends persist.

Don't just memorize strategy names. Know what market condition each strategy exploits, what risks it carries, and how it contributes to (or potentially disrupts) market functioning. When exam questions ask about fintech's impact on financial markets, these strategies are your concrete examples of technology reshaping price discovery, execution quality, and market structure.

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Liquidity and Market Structure Strategies

These strategies focus on the mechanics of how markets function—providing liquidity, reducing transaction costs, and ensuring efficient price discovery. They profit from the spread between buy and sell prices or by minimizing market impact.

High-Frequency Trading (HFT)

• Executes thousands of orders in milliseconds—capitalizing on tiny price discrepancies that exist for fractions of a second
• Requires massive infrastructure investment including co-location services (servers physically near exchanges) and low-latency connections
• Faces ongoing regulatory scrutiny due to concerns about flash crashes, market manipulation, and unfair advantages over retail investors

Market Making

• Provides continuous liquidity by always quoting both buy and sell prices for securities
• Profits from the bid-ask spread—the difference between what buyers pay and sellers receive
• Reduces market volatility by ensuring there's always a counterparty available, making markets function more smoothly

Volume-Weighted Average Price (VWAP)

• Serves as an execution benchmark calculated as $\text{VWAP} = \frac{\sum(\text{Price} \times \text{Volume})}{\sum \text{Volume}}$ over a trading period
• Minimizes market impact for institutional traders executing large orders by spreading trades throughout the day
• Measures execution quality—traders compare their average price against VWAP to assess performance

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Price Reversion Strategies

These strategies bet that prices will return to some equilibrium level after temporary deviations. They assume markets occasionally overshoot and correct, creating profitable opportunities.

Mean Reversion

• Assumes prices revert to historical averages—traders identify overbought or oversold conditions and bet on correction
• Uses statistical measures like standard deviation to determine when prices have deviated significantly from the mean
• Requires careful risk management because trends can persist longer than expected, causing losses if reversion doesn't occur

Statistical Arbitrage

• Exploits pricing inefficiencies between related financial instruments using quantitative models
• Creates market-neutral portfolios with simultaneous long and short positions to profit from relative price movements
• Relies on historical correlations—assumes statistical relationships that held in the past will continue

Pairs Trading

• A market-neutral approach that goes long on an undervalued asset while shorting its overvalued correlated pair
• Bets on convergence—profits when the spread between two historically correlated assets returns to normal
• Requires continuous monitoring of correlation strength, as relationships can break down during market stress

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Trend-Based Strategies

These strategies assume that price movements tend to persist—what's rising keeps rising, what's falling keeps falling. They profit by identifying and riding established directional moves.

Momentum Trading

• Capitalizes on existing trends by buying rising securities and selling falling ones
• Uses technical indicators like moving averages and Relative Strength Index (RSI) to confirm trend direction
• Requires fast execution because momentum opportunities can be short-lived and crowded

Trend Following

• Rides established market trends across various time frames, from days to months
• Employs stop-loss orders to protect against sudden reversals and limit downside risk
• Demands emotional discipline—the strategy fails when traders exit positions prematurely or chase false signals

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Data-Driven and Adaptive Strategies

These strategies leverage advanced analytics, machine learning, and alternative data sources to identify patterns humans might miss. They represent fintech's cutting edge—where finance meets data science.

Machine Learning-based Strategies

• Analyzes massive datasets to identify non-obvious trading patterns using supervised, unsupervised, and reinforcement learning
• Adapts to changing conditions by continuously learning from new data and updating models
• Requires significant resources—both computational infrastructure and expertise in data science and quantitative finance

News-based Trading

• Trades on information events including earnings releases, economic indicators, and breaking news
• Employs Natural Language Processing (NLP) to assess sentiment, relevance, and likely market impact of text data
• Highly time-sensitive—profits depend on executing before the market fully prices in new information

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Quick Reference Table

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Self-Check Questions

1. Which two strategies both assume prices will return to equilibrium, but differ in whether they focus on single assets or relationships between assets?

2. A large institutional investor needs to sell 500,000 shares without moving the market price. Which strategy concept should guide their execution, and why?

3. Compare and contrast HFT and Market Making: What do they share in terms of market function, and how do their profit mechanisms differ?

4. If an FRQ asks you to explain how technology has changed price discovery, which strategies would you use as examples of beneficial market impacts versus potentially harmful ones?

5. A quant fund notices that two historically correlated ETFs have diverged significantly. Which strategy would they likely employ, and what's the key risk they face?