Connecting Ranked Stock Data to a BigQuant Backtest

Summary

The document describes a beginner’s question about passing results from earlier BigQuant modules into a backtest. The proposed strategy uses a fixed universe of ten stocks, ranks them daily by five-day return in ascending order, buys the five lowest-ranked stocks, and sells holdings that leave that group. The author asks whether the backtest inputs for instruments, options data, and sorted history data can provide the information needed to implement those trades.

The post does not give an answer or explain how to access the ranked data, schedule rebalancing, or submit orders. It therefore identifies a practical workflow problem without teaching a working solution. Readers can learn the intended ranking and portfolio turnover rules, but would need further platform documentation or an answer from the community to implement them. No backtest results, execution assumptions, or risk analysis are provided.

Key ideas

• The proposed strategy ranks a ten-stock universe by five-day return each day.
• It buys the five stocks with the lowest ranked returns and sells stocks that leave the selected group.
• The author asks how earlier module outputs become accessible to the backtest logic.
• The post does not explain the required data access or order implementation.

Tags

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