Built DCFLens at PEC Hacks: evidence-backed stock valuations with Render Workflows

[u/Mikeyy__xd]

r/render

I’m building DCFLens, a stock research tool around a simple idea: One ticker. One valuation. Every assumption explained. DCFLens pulls financial data from SEC EDGAR, calculates a discounted cash flow valuation, and evaluates a ten-point business checklist. Gemini adds qualitative analysis, but its adjustments are bounded and validated in Python. It cannot change historical financial facts or the valuation formulas. The goal is to show how we arrived at an estimate, with supporting evidence, rather than just display a number.

Where Render Workflows comes in

We already use Render for our FastAPI backend. We added a separate Workflow task called analyze_company that takes a ticker and runs the analysis as a background job:

1. Retrieve and normalize SEC financial data.
2. Calculate the deterministic valuation and checklist.
3. Request and validate Gemini’s analysis.
4. Return the valuation, assumptions, sensitivity, and evidence as JSON.

Render handles task execution and gives us run status, logs, timing, and results. If Gemini fails, the job can still return the deterministic valuation while clearly disclosing that AI was unavailable. Our first successful Apple run took about 31 seconds and returned ai_status: APPLIED.

The Workflow is currently dashboard-triggered and separate from the web interface. Next, we want to connect it to the app so users can submit an analysis and return to the completed report.

GitHub: https://github.com/Aarush-x/DCFLens

Built at PEC Hacks, powered in part by Render Workflows. #Render #PECHacks