The open-source research terminal for equities, derivatives and quant work

Bloomberg-style workflows, primary-document intelligence and a tool-using AI analyst, self-hosted on your own hardware.

Quick start · Feature tour · Workflows · Architecture · AI models · Website

Why OpenTerminalUI

Most retail tools stop at price charts and ratios. OpenTerminalUI is built for the research that actually moves a position: reading what a company filed, tracking what management promised, mapping who it sells to and buys from, and testing an idea before money goes in.

• One terminal, many markets. NSE / BSE, NYSE / NASDAQ, F&O, commodities, forex, crypto, ETFs, bonds and mutual funds, behind a keyboard-first shell (Ctrl+G GO bar, Ctrl+K palette, Ctrl+J agent).
• Evidence, not vibes. Filings Intelligence reads 10-Ks, 10-Qs, earnings releases, annual reports and concall transcripts, and every growth engine or headwind it reports carries a verbatim, verified quote with document and page.
• An analyst that uses tools. The AI agent calls 40+ read-only tools (snapshots, technicals, filings search, screens, backtests, risk) and answers with the numbers it fetched, not ones it imagined.
• Honest data. Every quote carries provenance (live / delayed / cached / synthetic), blank metrics say why they are blank, and synthetic fallbacks are labelled, never passed off as real.
• Yours. MIT-licensed, self-hosted, works offline-first with free data sources, and runs AI on a local model if you prefer.

🧭 Feature tour

Screenshots are captured from the running app once each page has fully loaded (scripts/capture_readme.mjs), across a spread of popular stocks.

Security Hub

A single page per company: DES-style snapshot, interactive price chart with range tabs and hover readout, key ratios, filings signals, reverse DCF and catalyst conviction, with tabs for financials, chart, news, ownership, estimates, peers, ESG, tape, insiders and filings.

Filings Intelligence: RAG over company reports

Import a company's primary documents (SEC 10-K / 10-Q / 8-K earnings releases for US listings; annual reports, concall transcripts, investor presentations, order-win and USFDA announcements from NSE) or upload a PDF. The pipeline scores 21 drivers: growth engines such as order book, new client wins, capacity expansion, approvals and guidance raises, and headwinds such as client concentration, margin pressure, regulatory action and guidance cuts. Every finding is backed by a quote that must appear verbatim in the source, with document and page. You can also ask free-form questions and get cited answers, read concall summaries, and track management guidance over time.

Growth engines vs headwinds: scored drivers, each claim linked to its filing quote and page.	AI investment briefing: a balanced bull / bear / risks read built from fundamentals and live headlines.

Business research pack

Tijori-style company research, blended into the Security Hub: operating KPIs extracted from filings and charted over time, revenue mix and market share, a supplier → company → customer value chain with every link cited, competitors, raw-material exposure, and a reverse DCF that shows the growth the current price implies.

Operating KPIs pulled from filings with quote-level citations (MSFT).	Value chain: NVDA's foundry, memory and assembly suppliers from its 10-K, resolved to listed tickers where they exist.

AI research agent

A slide-over console (Ctrl+J) that researches on demand. It picks tools, fetches data, and writes the answer from the results, rendering snapshots and tables as cards. Modes include multi-agent debate (analyst team → bull vs bear → portfolio-manager decision), Strategy Lab (propose → backtest → iterate → out-of-sample validation), screen membership and ensemble analysis. The same tool registry is exposed as an authenticated MCP server for Claude Code, Claude Desktop and other MCP clients.

Markets, charts and discovery

Charting: candles, indicators, drawing tools, multi-timeframe, replay, compare and multi-pane workstations.	Screener: guru and thematic presets, a formula engine, filings-based fields and 15+ visualisations.
Heatmap: sector and market-cap map of the session, with drill-down and top movers.	Thematic indices: 18 investable themes (defence, railways, EMS, AI semis, GLP-1…) against a benchmark.

Derivatives, quant and cross-asset

F&O: option chain with Greeks, multi-leg strategy builder, OI and PCR analysis, options flow, futures term structure, expiry calendar.	Backtesting: 16+ strategy templates, realistic execution costs, walk-forward, Monte Carlo, Model Lab and Portfolio Lab.
Commodities: energy, metals and agriculture with curves, seasonality, and the listed companies each move hurts or helps.	Crypto: market board, movers, sectors, DeFi, derivatives and correlation.

Workspaces

Mission control: workspace presets for Trader, Quant, PM, Risk and Ops desks.	Launchpad: drag-and-drop panels (charts, order book, news, alerts, AI research) with pop-outs and saved layouts.

🛠 What you can do with it

🧱 Architecture

flowchart LR
  subgraph Client["Browser (React 18 + TypeScript + Vite)"]
    UI["Terminal shell<br/>GO bar · palette · workspaces"]
    Pages["100+ screens<br/>Security Hub · F&O · Quant · Risk"]
    AgentUI["Agent console<br/>SSE stream"]
  end

  subgraph API["FastAPI backend"]
    Routes["80+ route modules<br/>JWT auth · REST · WebSocket"]
    Agent["Agent orchestrator<br/>40+ tools · debate · Strategy Lab"]
    MCP["MCP server<br/>stdio / HTTP"]
    Filings["Filings Intelligence<br/>parse · TF-IDF retrieve · LLM extract · verify"]
    Research["Research pack<br/>KPIs · value chain · themes · results · ideas"]
    Engines["Engines<br/>screener · backtest · risk · alerts · OMS"]
    Fetcher["Unified fetcher<br/>provider waterfall + provenance"]
  end

  subgraph Data["Data & models"]
    Providers["Kite · Yahoo · FMP · Finnhub<br/>NSE · SEC EDGAR · FRED"]
    LLM["LLM gateway<br/>OpenRouter · OpenAI · Gemini<br/>LM Studio / vLLM (local)"]
    Store[("SQLite / PostgreSQL<br/>Redis cache + pub/sub")]
  end

  UI --> Routes
  Pages --> Routes
  AgentUI --> Agent
  Routes --> Engines & Research & Filings
  Agent --> Fetcher & Filings & Engines
  MCP --> Agent
  Engines --> Fetcher
  Research --> Filings & Fetcher
  Fetcher --> Providers
  Filings --> LLM
  Agent --> LLM
  Research --> LLM
  Routes --> Store
  Fetcher --> Store

How a request flows. The React client calls /api/* over REST (and WebSockets for live quotes). Market data goes through the unified fetcher: L1 SQLite cache → L2 Redis → primary provider → fallback provider, with the serving source recorded as provenance on every response. AI features call one LLM gateway, so swapping OpenRouter for a local model is a configuration change, not a code change.

Filings Intelligence pipeline

flowchart LR
  A["Import<br/>SEC · NSE · upload"] --> B["Parse<br/>PDF / HTML / iXBRL<br/>page-aware"]
  B --> C["Chunk<br/>~1.2k chars<br/>+ section heading"]
  C --> D["Index<br/>per-symbol TF-IDF<br/>+ keyword boost"]
  D --> E["Retrieve<br/>top chunks per driver"]
  E --> F["Extract<br/>LLM, strict JSON<br/>(lexical fallback)"]
  F --> G{"Verify<br/>quote in source?"}
  G -- yes --> H["Score<br/>growth vs headwind"]
  G -- no --> X["Dropped"]

A finding survives only if its quote is found in the cited chunk (exact, or ≥85% token overlap with every number matching verbatim), and only if the model marks it as supporting the driver. Without an LLM, a lexical extractor still works, with results flagged as lower-confidence keyword matches.

🚀 Quick start

git clone https://github.com/Hitheshkaranth/OpenTerminalUI.git
cd OpenTerminalUI
./install.sh          # macOS / Linux / WSL   (Windows: ./install.ps1)

The installer detects your OS, creates .env with strong generated secrets, seeds an admin account with a unique password, uses Docker if available, otherwise a local Python + Node setup, and prints your login:

 OpenTerminalUI is ready  ->  http://localhost:8000
   email:    admin@openterminal.local
   password: <generated unique password>

Prerequisites: Docker, or Python 3.11+ and Node 20+. All API keys are optional; the app runs on free fallback sources.

cp .env.example .env
docker compose up --build                       # backend + frontend + Redis (SQLite)
docker compose --profile postgres up --build    # with PostgreSQL

python3.11 -m venv .venv && source .venv/bin/activate
pip install -r backend/requirements.txt
PYTHONPATH=. uvicorn backend.main:app --reload --port 8000

cd frontend && npm ci && npm run dev          # http://127.0.0.1:5173

Adding data keys: run make keys for a guided wizard, or (as an admin) use Settings → Data Providers in the app to set, test and clear keys live.

🤖 AI models

Every AI feature (agent, briefings, filings analysis, Q&A, concall summaries, KPI and value-chain extraction, news emotion) goes through one gateway. Pick a provider:

Reasoning models are supported: structured (JSON) calls disable thinking via chat_template_kwargs on local servers, so the token budget goes to the answer. If no model is reachable, features fall back to deterministic engines (lexical filings extraction, FinBERT / lexical sentiment) and say so in the UI.

📁 Project structure

backend/                FastAPI app
  api/routes/           REST route modules (equity, F&O, backtest, risk, OMS, providers…)
  agent/                AI agent: orchestrator, tool registry, debate, Strategy Lab
  mcp/                  MCP server (stdio / HTTP) over the agent tools
  filings_rag/          Filings Intelligence: sources, parsing, retrieval, analysis, knowledge
  filings_watch/        Background watcher that turns new filings into alerts
  business_metrics/     KPI, revenue-mix and market-share extraction
  value_chain/          Suppliers / customers / competitors / raw materials
  thematic_indices/     Theme baskets and benchmark-relative performance
  ideas/ results_tracker/ raw_materials/ peer_kpis/ valuation/   Research pack
  core/                 Unified fetcher, providers, backtesting, risk, technicals
  pure_jump_vol/        Pure-jump volatility model (fit, filter, signals)
  services/ shared/     LLM gateway, caching, DB session, market classifier
  tests/                pytest suite
frontend/               React + Vite SPA
  src/pages/            Screens
  src/components/       Terminal design system and feature components (incl. ai/AiVisuals)
  src/agent/            Agent console, SSE client, artifact rendering
  src/api/              Typed API clients
  tests/e2e/            Playwright specs
plugins/                Example plugins
scripts/                Installer helpers, screenshot capture, PJV research CLI (scripts/pjv)
packaging/windows/      PyInstaller build for a Windows desktop executable
docs/                   Architecture notes, guides and design docs
assets/                 Logo and README screenshots

✅ Testing

PYTHONPATH=. pytest backend/tests -q            # backend
cd frontend && npx vitest run && npm run build  # frontend unit tests + type-checked build
cd frontend && npm run test:e2e                 # Playwright end-to-end
make gate                                       # backend tests + frontend build

Re-capture the README screenshots from a running instance:

cd frontend && OT_BASE=http://127.0.0.1:8000 OT_TOKEN_FILE=/path/to/jwt.txt node ../scripts/capture_readme.mjs

⌨️ Keyboard shortcuts

🤝 Contributing

Contributions are welcome. See CONTRIBUTING.md. Branch as feat/… or fix/…, add tests with the change, run make gate, and open a PR with a clear description.

📄 License

MIT. Free to use, modify and distribute, including commercially.