Quantitative Trading: Signals, Backtesting, and Three Basic Strategies
Summary
The document defines quantitative trading as using computers, mathematics, and statistical methods to build systems that generate buy and sell signals. It outlines the field’s development and describes backtesting, objective decision rules, faster monitoring, and portfolio diversification as potential advantages. These are presented as general benefits rather than demonstrated outcomes; backtests rely on historical data and cannot establish that a strategy will work in future markets.
Three examples illustrate different approaches. An opening range rule takes a position based on whether the first half-hour candle is positive or negative and closes before the market ends. A Donchian breakout buys above the recent N-bar high or sells below the recent N-bar low. An intermonth futures spread trade pairs contracts when their price difference departs from its usual range, anticipating a return toward that range. The examples are introductory and provide no performance tests, transaction cost analysis, or detailed risk controls.
Key ideas
- Quantitative trading uses computational and statistical tools to define signals and trading decisions.
- Backtesting can evaluate a system on historical data, but does not guarantee future performance.
- An opening range strategy takes a directional position based on the first half-hour candle.
- A Donchian rule trades breaks above recent highs or below recent lows.
- Intermonth futures arbitrage pairs contracts when their spread moves away from an observed range.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.