Overview of 21 Quantitative Trading Strategies and Their Core Signals
Summary
This survey outlines a broad set of systematic approaches across equities, currencies, futures, options, and fixed income. It describes cross-sectional signals such as price and earnings momentum, book-to-price value, volatility, and combinations of factors; technical rules using moving averages, pivots, and channels; and relative-value methods including pairs, merger, triangular, convertible-bond, and calendar-spread arbitrage. It also introduces market making, carry, alpha combination, and machine-learning sentiment analysis.
For each approach, the article sketches a basic signal or position construction, such as ranking securities, trading deviations between related instruments, following indicator crossovers, or processing social posts for sentiment. It offers conceptual descriptions rather than empirical support: it gives no consistent data, backtests, risk-adjusted comparisons, or implementation details. Many strategies face material limitations, including exchange-rate risk in carry trades, informed order flow in market making, model and data quality issues in sentiment work, and potential false signals in technical rules. The list is an introductory taxonomy, not evidence that any strategy will be profitable.
Key ideas
- Cross-sectional strategies rank securities using returns, earnings, valuation, volatility, or combinations of factors.
- Technical approaches use moving averages, pivot levels, and price channels to define entries and exits.
- Relative-value strategies trade price relationships among paired securities, currencies, corporate actions, or contract maturities.
- Carry, market making, sentiment models, and alpha combination rely on distinct sources of return and face different risks.
- The descriptions are introductory and provide no comparative performance evidence or complete implementation specification.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.