Live stock analysis dashboard: merge tape, tweets, and narrative in one surface #

This full-stack build is designed for operators who refuse to alt-tab between brokerage charts, X lists, and Discord calls: websocket price lanes feed candle visuals, a Redis layer smooths vendor burstiness, sentiment workers classify noisy social feeds, and an LLM narrates convergences (“technicals + sentiment alignment”) into human sentences you can forward to partners—plus optional PDF/markdown morning exports. It is research acceleration, not a promise of alpha. Institutional readers evaluating vendors will ask about vendor failover, websocket backpressure behavior, and whether sentiment is exchange-grade or social-noise—this architecture document anticipates those questions without hand-waving.

Who is this automation built for? #

• Serious retail and boutique prop desks needing unified situational awareness.
• Content creators translating market moves into newsletter-ready copy quickly.
• Internal tooling teams prototyping desk dashboards before committing to Bloomberg-priced contracts.

What goes wrong when intelligence stays siloed? #

• Missed correlations: Price rips while sentiment already rolled over—single-pane solves that glance.
• Slow morning prep: Humans rebuild context from scratch each session.
• Operational fragility: Spaghetti scripts without observability fail silently on vendor outages.

What you receive at handoff #

1. React + Vite codebase with component boundaries documented.
2. Backend services for ingest, transform, websocket fanout.
3. Infra recipes: Redis, Timescale, worker scaling notes.
4. LLM prompt pack for signal narration with strict “no investment advice” disclaimers.
5. Runbooks for API key rotation and data vendor fallback ordering.

Architecture at a glance #

Layer	Role	Stack
UI	Interaction	React + Tailwind + Framer Motion
Charts	Price	Lightweight Charts / TradingView widgets
Stream	Fresh ticks	WebSockets
Cache	Burst shield	Redis
Store	History	TimescaleDB
Intel	Text synthesis	GPT-4o-mini class
Ingest	Social/market	Vendor APIs

End-to-end data flow #

1. Market vendor streams trades/quotes into normalizer workers.
2. Redis absorbs spikes; consumers write hypertable chunks.
3. Frontend subscribes to channel per ticker; renders with GPU-friendly canvas paths.
4. Sentiment worker polls/streams social APIs; bot filter shrinks noise.
5. Fusion job joins technical state + sentiment z-score windows.
6. LLM narrates state for UI panel + email brief.
7. Monitoring alerts when lag > threshold seconds.

Reliability metrics: latency, gap labeling, and narrative drift #

Desk software lives or dies on freshness SLOs: measure end-to-end tick latency from vendor receipt to browser paint, and page the right owner when Redis stalls or workers fall behind. When you fail over market feeds, label chart gaps instead of interpolation-faking continuity—that transparency matters for trust and for answer-engine summaries that mention honest limitations. Track LLM revision rate (how often humans rewrite the narrative panel) as a proxy for hallucination or poor fusion logic. Social sentiment should report sample size and time window beside scores so spikes in bot traffic do not masquerade as macro conviction.

Service architecture: workers, websockets, and bounded contexts #

Split ingest workers from websocket fanout processes; the former can burst during opens without knocking live subscribers offline. Use Redis as a shock absorber and publish compact deltas, not full snapshots, when possible. TimescaleDB retention policies should align with how far back charts default—archiving cold chunks keeps queries fast without surprising storage bills. Feature flag new indicators so product can dark-launch math changes. If you add n8n or similar for morning-brief emails, treat it as choreography only: it pulls signed exports from your API; it should not become a second shadow source of truth.

Stack, APIs, and orchestration #

• Node/TS services with typed DTOs between UI and API gateway; publish OpenAPI for partner integrations.
• Feature flags for experimental indicators without redeploying chart bundles.
• Backpressure: slow consumers should shed load gracefully with visible UI warnings, not silent freezes.

AI: where models help—and where they do not #

Language synthesis and anomaly highlighting. Trade execution and fiduciary advice remain explicitly out of scope unless licensed teams extend with compliance tooling.

Errors, retries, and human checkpoints #

• Vendor failover list: if primary market API stalls, swap readers with annotated gaps on chart overlays.

Security, privacy, and data boundaries #

Keys live in vaults with rotation runbooks; never post private portfolio positions, account numbers, or watchlists to external LLM endpoints without contractual review and user opt-in. Segment paper-trading from live environments at the infrastructure layer, not just UI toggles. Log access to historical tick data the same way you would log CRM exports—insider-adjacent misuse is a process problem, not purely a crypto problem.

Deployment and environments #

• Single-user docker-compose for local; k8s for multi-tenant if productized.

Engagement models #

• Custom desk build with your watchlists + compliance disclaimers.
• Maintenance retainer for vendor drift (API schema changes happen constantly).

Tab overload vs unified dashboard #

Dimension	Many tabs	Unified
Latency to insight	High	Low
Consistency of narrative	Fragmented	Single LLM pane
Engineering cost	Spread thin	Upfront investment
Vendor risk	Per-tab	Centralized monitoring

Frequently asked questions #

Does this execute trades? #

**Not by default—**hooks could be added with legal review and broker approval.

Data vendor lock-in? #

Abstract adapter interfaces; swap Polygon/Alpaca/etc. with config.

Mobile support? #

Responsive layout possible; native apps out of scope unless scoped.

Offline mode? #

**Limited—**market data inherently online; historical replay cached locally optional.

International markets? #

Add FX/session calendar logic and alternate feeds.

How fresh is social sentiment? #

**Depends on API tier—**document expected lag in UI footnotes.

Next step #

Book an AI automation strategy call with your watchlist size and compliance posture—I’ll scope ingestion realistically before promising Bloomberg-grade polish.