Quantitative Trading Concepts and Three Classic Strategies
Summary
The document introduces quantitative trading as the use of computers, mathematics, statistics, and a systematic process to develop signals for buying and selling. It briefly describes the field’s history, including Jules Regnault’s work on price variation, and presents backtesting, rule based decisions, speed, and portfolio diversification as potential advantages. These are general claims rather than evidence from tested strategies, and the text does not establish that quantitative methods reliably improve returns or remove risk.
Three examples illustrate how rules can be expressed as strategies. An opening range approach takes a position based on whether the first half hour’s candle closes up or down, then exits near the close. A Donchian approach buys above a prior N bar high or sells below a prior N bar low. A futures spread approach trades two delivery months when their price difference departs from an assumed historical range, expecting it to revert. The document gives no performance data, transaction cost analysis, or detailed risk controls for these examples.
Key ideas
- Quantitative trading uses computational and statistical methods to create systematic buy and sell signals.
- Backtesting can examine how a rule would have behaved on historical data, but does not establish future performance.
- An opening range strategy takes a position according to the direction of the first half hour’s candle.
- A Donchian breakout strategy trades when price exceeds a prior high or falls below a prior low.
- Intertemporal futures arbitrage trades delivery month contracts when their spread departs from an assumed range.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.