## 1. Project scaffold

- [ ] 1.1 Initialize Next.js (App Router) + TypeScript project at the repo root
- [ ] 1.2 Add Tailwind CSS and a base layout/theme (light + dark)
- [ ] 1.3 Add a charting library (Recharts or lightweight-charts) and confirm it renders
- [ ] 1.4 Set up `.env.local` handling and `.env.example` for `MARKET_DATA_API_KEY` and `ANTHROPIC_API_KEY`; ensure secrets never reach the client bundle
- [ ] 1.5 Configure lint/format/test tooling (ESLint, Prettier, Vitest/Jest) and a passing sample test
- [ ] 1.6 Define shared TypeScript types module (normalized data models + `Evaluation` object skeleton with `unavailable` markers)

## 2. Market-data layer (`market-data` spec)

- [ ] 2.1 Define the `DataProvider` interface and normalized models (`CompanyProfile`, `Fundamentals` incl. optional segments, `PriceHistory`, `AnalystCoverage`, `Estimates`), each with `asOf` + `delayed|realtime`
- [ ] 2.2 Spike two free providers (e.g. FMP, Finnhub) against `DE`; pick the default based on segment + forward-estimate coverage; record the choice in design Open Questions
- [ ] 2.3 Implement the default free-tier provider behind the interface
- [ ] 2.4 Implement ticker resolution with NYSE/Nasdaq validation and typed not-found result
- [ ] 2.5 Implement a fixture/mock provider seeded from the DE example for tests and offline dev
- [ ] 2.6 Add provider-boundary caching (in-memory, TTL, keyed by ticker+dataset) and retry/backoff for rate-limit/transient errors returning typed errors
- [ ] 2.7 Unit-test normalization, unavailable-field handling, and error/rate-limit paths

## 3. Technicals module (`market-data` spec)

- [ ] 3.1 Implement pure functions: 20/50/200-day moving averages, 52-week high/low distance, average volume + today's volume multiple
- [ ] 3.2 Implement swing high/low detection and realized (e.g. 60-day annualized) volatility
- [ ] 3.3 Handle insufficient-history cases (mark long-window indicators unavailable)
- [ ] 3.4 Unit-test all indicators against known values from the DE example

## 4. Evaluation engine (`equity-evaluation` spec)

- [ ] 4.1 Implement current-standing summary (price, day change abs/%, volume multiple, 52-wk distance, market cap, trailing P/E)
- [ ] 4.2 Implement earnings recap (EPS vs consensus, beat/miss, net income YoY, guidance changes) gated by a configurable recency window
- [ ] 4.3 Implement quality-of-earnings caveats (one-time items + EPS impact, price-vs-volume, wrong-baseline headline metrics)
- [ ] 4.4 Implement financial summary (current vs prior-year) and per-segment breakdown when available
- [ ] 4.5 Implement valuation view (TTM EPS, trailing/forward P/E, growth, FCF & dividend yield, vs own history) with conflicting-estimate reconciliation
- [ ] 4.6 Implement explicit over/undervalued reasoning (multiple-expansion vs earnings-growth, trough/peak earnings)
- [ ] 4.7 Implement macro/sector factor analysis (tariffs trajectory, rates, input-cost pressure, peer read-throughs)
- [ ] 4.8 Implement timing/volatility context (post-earnings behavior, implied vs actual move, monthly ranges, gap-fill status)
- [ ] 4.9 Implement entry levels (ranked levels with meaning, % below price, implied multiple at each) grouped into bands with starter/high-conviction tranches
- [ ] 4.10 Implement exit/targets (analyst avg/median/range + technical resistance) and stop-loss levels (technical/volatility-based, concrete prices + rationale)
- [ ] 4.11 Implement bull-vs-bear synthesis and actionable plan (tranche sizing, next dated catalyst, conditional rules, one metric to watch)
- [ ] 4.12 Emit the typed `Evaluation` object with explicit unavailable markers (no fabrication) + mandatory disclaimer
- [ ] 4.13 Unit-test each section, including missing-input and unavailable-value paths, seeded from the DE example

## 5. Analysis agent (`analysis-agent` spec)

- [ ] 5.1 Define the `AnalysisAgent` interface (input: `Evaluation`; output: narrative sections)
- [ ] 5.2 Implement the Claude default via `@anthropic-ai/sdk` (current model), key loaded server-side from config
- [ ] 5.3 Design the prompt + structured/JSON output so narrative maps to spec sections and uses ONLY supplied figures
- [ ] 5.4 Preserve input caveats/discrepancies and enforce the disclaimer in the narrative
- [ ] 5.5 Implement graceful degradation when no key is set (return structured evaluation, mark narrative unavailable)
- [ ] 5.6 Implement typed agent error handling (auth/rate-limit/timeout) that retains the computed evaluation
- [ ] 5.7 Test grounding (every cited figure exists in input), no-key, and error paths with a mock agent

## 6. Pipeline + API (`evaluation-app` spec)

- [ ] 6.1 Implement server-side route/action orchestrating resolve → data → evaluate → agent, returning `{ evaluation, narrative | error }`
- [ ] 6.2 Handle partial results (data ok / agent unavailable) and typed errors end-to-end
- [ ] 6.3 Ensure secret keys are used only server-side and surface missing-key state per capability

## 7. Web UI (`evaluation-app` spec)

- [ ] 7.1 Build the ticker search entry point with inline invalid/not-found handling
- [ ] 7.2 Build the report view rendering every section in order, tables as tables, unavailable values shown honestly
- [ ] 7.3 Build the price chart with moving averages and overlay markers for entry bands, exit/targets, and stop-loss levels
- [ ] 7.4 Implement loading state and readable error states (no blank screens)
- [ ] 7.5 Surface missing-key indicators and a settings/config affordance for keys

## 8. Verification

- [ ] 8.1 End-to-end run on `DE` (with keys) producing a report structurally matching the example; note any data gaps from the free tier
- [ ] 8.2 End-to-end run with no agent key confirming structured evaluation still renders
- [ ] 8.3 Run the full test suite and lint; confirm green
- [ ] 8.4 Update README with setup, env vars, provider choice, and run instructions
- [ ] 8.5 Run `openspec validate stock-deep-evaluation` and archive the change when complete