Factor mining is where quant research starts — but distinguishing a real factor from a backtest-pretty one requires strict discipline. This post outlines the factor taxonomy for China A-shares and the validation pipeline we use (methodology only; we do not publish factor names, formulas, or parameters).
A-share quant factors fall into seven broad families, capturing different micro- and fundamental signals:
| Type | Signal source |
|---|---|
| Momentum | Persistence of price trends |
| Reversal | Short-term mean reversion |
| Volatility / Volume | Volatility regime + volume structure |
| Microstructure | Order book, tick-level, large-order classification |
| Money-flow | Main capital, Northbound flow, Dragon List |
| Fundamentals | F10, financial statements, valuation, dividends |
| ML-synthesized | Non-linear composition of multiple primitive factors |
Every type depends on a clean, consistent data substrate — which is exactly what ReachRich provides: unified data contract, adjusted-price continuity, multi-source cross-validation.
The single biggest factor-mining trap is overfitting: test enough variants on the same history, and you will always find one with a great-looking Sharpe — but it is fitting noise, not signal. To separate real alpha from noise, every candidate factor must pass three gates:
Serious quant teams disclose validation methodology and out-of-sample performance, not factor names/formulas/parameters/model weights. What is reproducible is the discipline, not the alpha itself.