Quantitative Trading Models and Example Strategies Across Stocks and Futures

Summary

This overview outlines a quantitative workflow: collect and clean data, develop a strategy, manage risk, backtest on historical data, and automate execution. It then sketches strategies for Chinese equities and futures, including Turtle-style breakouts, moving-average crossovers, valuation and multi-condition stock screens, earnings-event selection, a broad-market risk filter, GBDT-based ranking, and futures signals using MACD, price extremes, and Bollinger Bands.

A pairs example estimates a long-run relationship between two stocks and trades deviations in a standardized residual, while the summary section groups approaches into trend following, mean reversion, arbitrage, market neutral, high-frequency, statistical arbitrage, machine learning, and event-driven methods. The document gives rules and example thresholds, but no backtest results, transaction-cost analysis, or evidence that the strategies are profitable. Its brief descriptions leave important implementation questions unresolved, including validation, execution assumptions, and how risk controls should be calibrated.

Key ideas

• A quantitative workflow links data preparation, strategy design, risk management, backtesting, and execution.
• The examples cover breakout, moving-average, valuation, earnings-event, and machine-learning stock selection methods.
• Futures examples use MACD crossovers, recent price extremes, and Bollinger Band thresholds.
• The pairs strategy trades deviations from an estimated relationship between two stocks.
• The document lists strategy rules but does not provide performance evidence or detailed validation methods.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.