Independent Reviewer

FinAlly — AI Trading Workstation

An AI-powered trading workstation that streams live market data, supports simulated portfolio trading, and includes an LLM chat assistant that can analyze positions and execute trades on your behalf. Designed to look and feel like a modern Bloomberg terminal with an AI copilot.

Features

• Live price streaming via Server-Sent Events — prices flash green/red on change
• Sparkline mini-charts per ticker, built from the live price stream
• Simulated portfolio — $10,000 starting cash, instant market order fills
• Portfolio heatmap (treemap) — positions sized by weight, colored by P&L
• P&L chart — total portfolio value over time
• AI chat assistant — powered by Cerebras/OpenRouter; analyzes positions and executes trades via natural language
• Watchlist management — add/remove tickers manually or through the AI

Quick Start

# Copy and configure environment variables
cp .env.example .env
# Edit .env — set OPENROUTER_API_KEY (or set LLM_MOCK=true for development)

# Start the app
./scripts/start_mac.sh        # macOS/Linux
./scripts/start_windows.ps1   # Windows PowerShell

Open http://localhost:8000 in your browser.

Environment Variables

Variable	Required	Description
OPENROUTER_API_KEY	Yes (unless LLM_MOCK=true)	OpenRouter API key for LLM chat
MASSIVE_API_KEY	No	Real market data; omit to use the built-in simulator
LLM_MOCK	No	Set true for deterministic mock LLM responses (no API key needed)

Architecture

Single Docker container on port 8000:

• Frontend: Next.js (TypeScript), built as a static export, served by FastAPI
• Backend: FastAPI (Python/uv) — REST API, SSE streaming, LLM integration
• Database: SQLite at db/finally.db, persisted via Docker named volume
• Market data: Built-in GBM simulator (default) or Massive REST API
• AI: LiteLLM → OpenRouter → Cerebras (openai/gpt-oss-120b)

Development

Backend

cd backend
uv sync
uv run uvicorn app.main:app --reload

Frontend

cd frontend
npm install
npm run dev

Tests

# Backend unit tests
cd backend && uv run pytest

# E2E tests (requires Docker)
cd test && docker compose -f docker-compose.test.yml up

Docker

# Build and run
docker build -t finally .
docker run -v finally-data:/app/db -p 8000:8000 --env-file .env finally

License

MIT