Automating Trades from Predictions Embedded in Simulation Logs

Summary

This article outlines a workflow for moving prediction data from a BigQuant simulation into a separate live-trading setup. The strategy writes selected data into simulation logs as Base64 text; an external Python process retrieves the latest log, decodes the payload into a data table, and passes the resulting information to a live execution interface. The author also describes a basic schedule for submitting and cancelling orders around the Chinese market open and close.

The focus is infrastructure and data transfer, not the predictive model or its trading edge. The article includes implementation details for encoding, log retrieval, and order submission, but provides no performance results or reliability analysis. It depends on access to the platform, login credentials, and a separate broker or execution channel; the proposed schedule is an author’s example and does not establish suitability across instruments, brokers, or changing market conditions.

Key ideas

• Selected simulation outputs can be encoded and written into strategy logs for later retrieval.
• An external Python process can fetch the latest log and decode its prediction data.
• A separate live execution interface is required to turn retrieved predictions into orders.
• The author gives an example order schedule around Chinese market hours.
• The article describes a data pipeline, not evidence of predictive performance or trading profitability.

Tags

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