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A local-first multi-agent investment research system for stocks and cryptocurrencies, supporting structured analyst workflows, QLoRA model fine-tuning, and bilingual CLI report generation.
Section 01
This post introduces Investment Research Desk, an open-source multi-agent system for stock and crypto investment research. It aims to democratize investment research by addressing tool barriers, high data costs, and model opacity. Key features include structured analyst workflows, QLoRA fine-tuning, bilingual CLI reports, and local-first design ensuring data sovereignty and offline usability.
Section 02
In the era of quantitative investment and algorithmic trading, individual investors and small teams face challenges like high tool thresholds, expensive data costs, and black-box models. The Investment Research Desk project vision is to democratize investment research capabilities, enabling users with basic technical skills to build professional-grade analysis workflows via open-source multi-agent architecture.
Section 03
Multi-agent Collaboration: The system decomposes research into specialized roles: data collection, fundamental analysis, technical analysis, sentiment analysis, risk assessment agents, which coordinate via structured workflows. Local-first Design: Ensures data sovereignty (local storage), cost control (open-source models + QLoRA for consumer hardware), and offline availability. QLoRA Support: Allows fine-tuning of large models on consumer GPUs/CPUs, enabling custom models tailored to specific strategies or markets.
Section 04
Bilingual CLI Reports: Generates Chinese/English reports in Markdown/PDF via CLI. Application Scenarios:
Section 05
Open Source Contribution: Welcomes community input (data adapters, agents, UI improvements) with permissive licensing for commercial use. Future Roadmap:
Section 06
Investment Research Desk represents AI's latest effort to empower individual investors. Its multi-agent architecture, local-first design, and open-source nature bring new possibilities to investment research. For users seeking autonomous, controllable investment analysis capabilities, this project is worth following and contributing to.
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