Next.js 15 + TypeScript app implementing the fully-specced first change. Pipeline: resolve -> market data -> pure evaluation engine -> budget guard -> analysis agent -> report. - market-data: DataProvider interface, offline FixtureProvider (DE/SPY seeded from the reference example), FmpProvider (FMP free tier), TTL cache + retry. - technicals: pure MA/volatility/swing/52-week math. - evaluation: instrument-aware pure engine; equity branch built, ETF gated to "not yet supported". Reproduces the DE example (P/E 34.5, fwd 29.3, $167.6B). - agent: AnalysisAgent interface; default Claude Code CLI transport (headless, subscription-backed, web-grounded), Anthropic API alternate via config. - cost-controls: price table, spend store, monthly budget guard. - UI: ticker search + deep-dive toggle, report view, price chart with marked entry/exit/stop levels, cost/budget display, ETF/not-found states. 31 vitest tests, typecheck, production build, and lint all pass. Verified end-to-end via the API for DE, SPY, and an unknown ticker. Live Claude CLI agent test is the documented pick-up point (see README). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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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.localhandling and.env.exampleforMARKET_DATA_API_KEYandANTHROPIC_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 +
Evaluationobject skeleton withunavailablemarkers) with aninstrumentTypediscriminant (equity|etf); fundamentals/valuation sections under a per-type branch, instrument-agnostic sections shared
2. Market-data layer (market-data spec)
- 2.1 Define the
DataProviderinterface and normalized models (CompanyProfile,Fundamentalsincl. optional segments,PriceHistory,AnalystCoverage,Estimates), each withasOf+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, an
instrumentType(equity/etf) classification, and a 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
Evaluationobject with explicit unavailable markers (no fabrication) + mandatory disclaimer; carryinstrumentType, populate the equity branch, and return an unsupported-type result for non-equity instruments (no equity-branch fabrication) - 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
AnalysisAgentinterface (input:Evaluation+ options incl. deep-dive flag; output: narrative sections + sources + reported usage/cost) with a config-selected transport - 5.2 Implement the default Claude Code CLI transport: spawn
claude -p --output-format json --model <opus|fable> --json-schema <narrative schema> --append-system-prompt <instructions> --allowedTools "WebSearch WebFetch" --permission-mode dontAsk; parse the JSON result (structured output +total_cost_usd+ usage) - 5.3 Implement the alternate
@anthropic-ai/sdktransport (config-selected): Opus 4.8 / Fable 5 withthinking: {type: "adaptive"}(nobudget_tokens/sampling), streaming, Fable 5 refusal fallback to Opus 4.8, server-side web tools - 5.4 Per-request model selection (
opusdefault,fablefor deep-dive) wired through both transports; ground with web search/fetch scoped to the ticker and capture source attributions - 5.5 Design the prompt + structured schema so narrative maps to spec sections and cites only supplied figures or attributed web sources
- 5.6 Preserve input caveats/discrepancies and enforce the disclaimer in the narrative
- 5.7 Implement graceful degradation: CLI missing/unauthenticated and no API key → structured evaluation with narrative unavailable + how-to-enable; web grounding error → analysis from structured data, note grounding unavailable
- 5.8 Implement typed agent error handling (spawn failure/non-zero exit/timeout for CLI; auth/rate-limit/timeout for API) that retains the computed evaluation
- 5.9 Test model selection, transport selection (CLI vs API), grounding attribution, agent-unavailable, grounding-error, and refusal-fallback paths with a mock/stubbed transport
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 and a clear "ETF evaluation not yet supported" message when an
etfis resolved - 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
- 7.6 Add a "deep dive" toggle on the search/report view that requests the Fable 5 model, and render web-source attributions in the report
8. Cost controls (cost-controls spec)
- 8.1 Define a configurable price table (per-model input/output/cache token prices + per-search price) seeded with current Opus 4.8 and Fable 5 rates
- 8.2 Capture per-report usage from the agent API response (tokens, cache tokens, web-search count, model) including on partial/error results
- 8.3 Compute per-report cost from usage + price table; flag unpriced models rather than recording zero
- 8.4 Implement lightweight spend storage: per-report history + month-to-date total with month-boundary reset (no database)
- 8.5 Implement the monthly budget guard: soft-threshold warning, hard-cap block, deep-dive pre-emption, and disabled-when-zero; check before dispatching the agent
- 8.6 Surface per-report cost in the report view and month-to-date spend vs budget in the app
- 8.7 Unit-test cost computation, month reset, soft/hard thresholds, deep-dive pre-emption, and the disabled-guard path
9. Verification
- 9.1 End-to-end run on
DE(with keys) producing a report structurally matching the example; note any data gaps from the free tier - 9.2 End-to-end run with no agent key confirming structured evaluation still renders
- 9.3 Verify cost is captured and displayed, and that the budget guard warns and blocks at its thresholds
- 9.4 Run the full test suite and lint; confirm green
- 9.5 Update README with setup, env vars, provider choice, and run instructions
- 9.6 Run
openspec validate stock-deep-evaluationand archive the change when complete