Backtest to Insight: Building an Index Futures Strategy from Stock-Level Signals
A methodology for building an index futures trading strategy by aggregating stock-level signals. The approach covers signal generation, aggregation, and backtesting to derive actionable futures trading insights from individual stock data.
Background
- DolphinDB is a time-series database and analytics platform used in quantitative finance, especially in China. The author is its official account, so the piece is partly marketing.
- "Stock-level signals" means using data from individual stocks to generate a trading signal for a futures contract on an entire index — here the CSI 300 (top 300 stocks on Shanghai/Shenzhen exchanges).
- Index futures let traders bet on the whole market. The challenge: deciding whether to go long or short based on what individual stocks are doing — a "bottom-up" approach.
- The article demonstrates a full backtesting workflow in DolphinDB's own scripting language (not Python/R), from signal construction to performance metrics like Sharpe ratio and drawdown. The pitch: doing everything in one system, faster and without data exports.