01 / Data workflows
Tradings.Guru
AI-assisted market research with clearer data boundaries.
Problem
Stock research meant moving between market data, social discussion, and other sources. Collecting that information manually was repetitive and hard to keep current.
Result
A deployed research platform that brings fragmented information into one interface, with more explicit sourcing and methodology in its options-flow tool.
Limits & lessons
Coverage and freshness depend on upstream providers. Some tools remain heuristic or estimated. This is research support requiring human judgment, not evidence of predictive accuracy or investment performance.
Approach
- Brought market and social-data tools into one research workflow using APIs, MCP integrations, and stored context for agents.
- Built a shared caching layer to manage repeated upstream requests; the broader workflow supports automated updates and a customer-facing chatbot.
- Replaced synthetic options-flow metrics with actual options-chain volume and open interest, removed unsupported sweep detection, and included the methodology and update time in the response.
Evaluation & judgment
Checked where outputs came from rather than relying on feature labels. The source catalog distinguishes provider data, heuristics, and estimates; the indicator-based signal route is separate from the broader AI-assisted workflow.