sigma-terminal 3.6.1

Sigma - Finance Research Agent

σ SIGMA

The Elite Financial Research Terminal

A high-performance, terminal-based research platform for the modern quantitative analyst.

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Overview

Sigma v3.6.1 is not just a chatbot—it is a comprehensive financial operating system. Designed for speed, privacy, and precision, Sigma unifies state-of-the-art LLMs with institutional-grade data tools in a minimalist, keyboard-centric interface.

Inspired by the aesthetics of Claude Code and the power of Bloomberg Terminal, Sigma provides a distraction-free environment where natural language commands translate instantly into rigorous financial analysis.

Key Features

• Elite TUI Experience: A "Tokyonight" themed interface with zero latency, generative autocomplete, and real-time ticker recognition.
• Multi-Model Intelligence: Seamlessly route queries to OpenAI o1, Claude 3.5 Sonnet, Gemini 1.5 Pro, or run locally with Ollama (Qwen 2.5).
• Institutional Data: Direct integration with Polygon.io, Alpha Vantage, and Exa Search for hallucination-free market data.
• Quantitative Backtesting: Built-in LEAN Engine support allows you to generate, test, and refine algorithmic strategies in seconds.
• Privacy by Design: All API keys and strategies are stored locally. No cloud dependencies. No data logging.

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Quick Start

Prerequisites

• Python 3.11+
• Ollama (Recommended for local intelligence)
• LEAN CLI (Auto-installed for backtesting)

Installation

Sigma features an autonomous Setup Agent that configures your environment automatically.

# 1. Clone the repository
git clone https://github.com/sigma/terminal.git
cd sigma

# 2. Install dependencies
pip install -r requirements.txt

# 3. Launch Sigma (Triggers Setup Agent)
python -m sigma

The Setup Agent will:

1. Detect your hardware acceleration (Metal/CUDA).
2. Install and pull the optimized local model (qwen2.5:1.5b).
3. Configure your API keys securely in .env.
4. Initialize the LEAN backtesting engine.

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Usage

Sigma is controlled via natural language or slash commands. The interface is designed for flow.

Market Intelligence

"Analyze the correlation between NVDA and SMCI over the last 6 months." "What are the key risk factors in Apple's latest 10-K?" "Visualize the volatility surface for SPY options."

Strategy Development

"Backtest a mean-reversion strategy on BTC-USD using 15-minute bars." "Optimize parameters for a dual-moving average crossover on TSLA."

System Control

• /model <name> - Switch active LLM (e.g., /model gpt-4o)
• /clear - Reset context window
• /quit - Exit session

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Architecture

Sigma is built on a modular, event-driven architecture designed for extensibility.

Layer	Component	Description
Interface	Textual TUI	Async, reactive terminal UI with custom widgets and syntax highlighting.
Intelligence	LLM Router	Dynamically routes queries based on complexity and cost (Local vs. Cloud).
Data	Tool Registry	Type-safe bridge connecting LLMs to 30+ financial data endpoints.
Execution	LEAN Engine	Dockerized backtesting environment for institutional-grade strategy validation.

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Configuration

Configuration is managed via the .env file in your home directory (~/.sigma/config.env) or project root.

# Example .env
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
POLYGON_API_KEY=...
DEFAULT_PROVIDER=ollama
OLLAMA_HOST=http://localhost:11434

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Contributing

We welcome contributions from the community! Please see our Contributing Guidelines for details on how to get started.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Contact

For support or inquiries, please open an issue on GitHub or contact the maintainers at [e-mail].

_{Built by the Sigma Team}