A-Share Screening with Convertible Bond, Amplitude, and Company Filters

Summary

This post describes an A-share stock screen combining a daily price-amplitude threshold, a nonempty convertible-bond name field, and a company classification filter that can be configured for criteria such as industry or state ownership. It proposes ranking qualifying stocks by average daily traded value. A Python example adds valuation filters using price-to-book and trailing price-to-earnings ratios, as well as price, listing, and trading-data exclusions.

The post cautions that company classification alone omits important measures such as earnings and growth, and that classification data may be incomplete or asymmetric. It suggests adding industry trends, competitive strength, growth measures, or timing indicators. The examples do not provide backtest results or evidence that the screen identifies undervalued stocks; moreover, the narrative criteria and code do not align perfectly, so implementation details would need checking before use. The screen is an illustrative recipe, not a validated investment strategy.

Key ideas

• The proposed screen combines price amplitude, convertible-bond information, and a configurable company classification filter.
• Qualifying stocks are ranked by average daily traded value.
• The Python example adds price-to-book and trailing price-to-earnings filters alongside other exclusions.
• The post warns that company classification alone omits earnings, growth, and other relevant factors.
• The written rules and code contain differences, and the post supplies no performance validation.

Tags

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