Popular strategy categories
Algorithmic trading strategies vary by objective and time horizon. Beginners should focus on well-documented, widely used approaches before exploring bespoke or complex models.
Common strategy types:
- Trend following: Buys assets that are trending up and sells those trending down using indicators like moving averages or breakout systems.
- Mean reversion: Assumes prices revert to an average; common tools include Bollinger Bands and pair trading.
- Momentum: Capitalizes on short-term price momentum often using relative strength metrics.
- Market making: Provides liquidity by posting both buy and sell orders to capture bid-ask spread (requires low-latency infrastructure).
- Statistical arbitrage: Uses statistical relationships across securities to profit from temporary mispricings.
- News-based and event-driven: Trades around earnings, macro releases, or other scheduled events using natural language processing or rule-based reactions.
Practical considerations:
- Timeframe: Strategies range from high-frequency (milliseconds) to swing trades (days to weeks). Choose a horizon matching your capital, infrastructure, and regulatory constraints.
- Complexity vs robustness: Simpler rules are often more robust and easier to validate than complex, multi-parameter models that risk overfitting.
- Costs and liquidity: Frequent trading increases costs; ensure the instruments are liquid enough for your order sizes.
Getting started:
- Learn one strategy family thoroughly and build a basic version.
- Backtest across different market regimes with realistic fees.
- Monitor live performance and refine risk controls.
These mainstream approaches are a solid foundation. As you gain experience, you can combine ideas or introduce machine learning cautiously, always focusing on statistical robustness and risk management.