quant-factor-screener

A multi-factor model for A-share stock screening calculates per-stock scores across chosen factors like value, momentum, and quality. It ranks stocks by composite score to surface favorable factor exposures for objective, data-driven investment decisions.

Yes, you can use this tool for Smart Beta construction and factor investing. It systematically computes and ranks A-share stocks based on formal factor exposures, supporting academic-factor based stock selection and objective portfolio decisions.

Yes, you need the findata toolkit for data inputs. The multi-factor screener relies on this toolkit to supply the underlying market data required to compute and rank A-share stocks based on your configured factors.

The best way to configure value, momentum, and quality factors is to select your desired stock pool and specify the target factor set. The screener then computes scores and generates a ranked list of candidates with favorable exposures.

Stocks are ranked by a composite score derived from calculated per-stock factor exposures. The output is sector-aware and percentile-based, ensuring objective comparison across different market segments during the screening process.

Systematic factor screening across the All A-share market requires comprehensive data inputs via the findata toolkit. The primary limitation is data dependency; the screener relies entirely on these inputs and repository references to compute sector-aware, percentile-based rankings.