A grounded, self-learning read

Stock Research

Researches a single name end to end: company data, prices, news, technicals, sentiment, and a forecast. Most of the work is deterministic and statistical; a model is used only to summarize at the end.

What this demonstrates: Cost-tiered resolution: cheap deterministic and statistical steps do the work, with a single distilled model call at the finish. The forecast learns from realized outcomes.
Enter a ticker or a company nameEither one resolves to a symbolGather company data, prices, and newsServed from a read-through cacheCompute technical indicatorsDeterministic, no model neededScore news sentimentResolved at the cheapest correct tierForecast with a self-learning ensembleWeights shift toward what proved accurateSummarize the findingsOne distilled model call, the only generative stepResearch reportForecast cone, signals, and a calibration readout