Algorithmic Trading on the CMT Exam
Algorithmic trading implements technical analysis rules systematically through computer programs. As technology transforms markets, this topic is increasingly tested on CMT Level 2 (quantitative methods) and Level 3 (integration).
For the full guide, see the CMT Exam Guide 2026.
Types of Algorithmic Strategies
Trend-Following Algorithms
- Implement moving average crossover systems
- Use ADX to filter trend strength
- Apply trendline breaks as signals
- Managed futures (CTA) funds use these extensively
Mean-Reversion Algorithms
- Buy when RSI drops below threshold
- Bollinger Band bounce strategies
- Statistical arbitrage (pairs trading based on correlation)
- See mean reversion vs. momentum for details
Market-Making Algorithms
- Provide liquidity by quoting bid and ask prices
- Use market microstructure data
- Profit from bid-ask spread
Execution Algorithms
- TWAP (Time-Weighted Average Price): Execute evenly over time
- VWAP (Volume-Weighted Average Price): Execute proportional to volume
- Implementation Shortfall: Minimize slippage vs. decision price
Building Algorithmic Systems
The development process follows backtesting principles:
- Strategy design: Translate technical analysis rules into code
- Historical testing: Run against years of market data
- Optimization: Find parameter ranges that work (avoid overfitting)
- Walk-forward validation: Rolling out-of-sample testing
- Paper trading: Live simulation without real capital
- Live deployment: Start with small capital, scale up
Machine Learning & Technical Analysis
Feature Engineering from TA
Many ML models use technical indicators as input features:
- RSI, MACD values, Bollinger Band position
- Volume ratios, volatility measures
- Relative strength rankings
- Sentiment data
Common ML Approaches
| Method | Application | Caveat |
|---|---|---|
| Random Forest | Feature importance ranking | Overfitting risk |
| Neural Networks | Pattern recognition | Black box |
| Clustering | Market regime detection | Requires domain knowledge |
| Reinforcement Learning | Position sizing optimization | Data hungry |
Impact on Market Structure
Algorithms have fundamentally changed markets:
- Faster execution: Millisecond-level trading
- Tighter spreads: More efficient pricing
- Flash crashes: Risk of cascading automated selling
- Pattern adaptation: Patterns may work differently in algo-dominated markets
- Volume interpretation: Much volume is algorithmic, changing traditional volume analysis
CMT Exam Application
- Level 2: Understand algorithmic strategy types, execution methods, and their market impact
- Level 3: Discuss how algorithms affect traditional technical analysis, integrate with portfolio management and risk management
Practice algo-related questions in our test bank. Full guide: CMT Exam 2026.
Algorithmic Trading — Strategy Type Distribution
Breakdown of algo strategies by AUM allocation (institutional)
Impact of Algorithmic Trading on Market Quality
Estimated effect on key market metrics since algo adoption (index: 100 = 2005)