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Deploying Open-Source Investment Research Agents for Private Equity: Generating Financial Briefings via Monthly 17k RMB Outsourced Operations

Workflow: At 8:00 AM every morning, the system automatically scrapes the latest financial disclosure documents of Shanghai, Shenzh

AGENT

Key Fields

FIELD STAMPS

IndustryFintech

RegionChina

ScaleSME

ChannelOnline

🔧 Workflow

At 8:00 AM every morning, the system automatically scrapes the latest financial disclosure documents of Shanghai, Shenzhen, Hong Kong, and US-listed Chinese concept stocks from the previous night via public data interfaces. These are simultaneously fed into pre-trained LLM agents to extract anomalies in core financial metrics such as revenue, profit, and cash flow, automatically generating standardized briefings with risk warnings. Human operators only need to cross-check key data and supplement industry comparison perspectives, and finally push the briefings to the clients' Lark/Feishu groups or internal research knowledge bases. The entire process requires no manual reading of financial reports one by one.

🛠 Setup Requirements

Setup requires Python programming and containerized deployment skills. Pull the open-source investment research agent repository code, configure the LLM API interface and exchange announcement scraping rules, and fine-tune prompt words and filtering logics based on the client's holding industries and tracking dimensions. The first-client full-process deployment and debugging can be delivered within 1 to 2 weeks.

🧰 Toolchain

🔧 Code hosting platform GitHub
🔧 LLM DeepSeek API
🔧 Containerization tool Docker
🔧 Lark/Feishu bot interface

💰 Revenue

Adopting a build-plus-operations pricing model, the one-time setup fee per client is 6,000 RMB, with a monthly system maintenance, rule optimization, and human review service fee of 2,000 RMB. Currently stably serving 3 small private equity firms and 1 small quantitative team, the fixed monthly service revenue is about 8,000 RMB. Coupled with ad-hoc requests from clients for specialized financial analysis and custom data interfaces, the average monthly total revenue can reach 17,000 RMB.

💸 Cost

Inference costs for financial report parsing and Q&A are about 500 to 800 RMB per month, cloud server and on-premise deployment storage costs are about 100 RMB per month, with no other additional fixed costs.

⏱ Time Investment

Dedicate 2 hours daily to maintain system operational stability, manually review key data anomalies in briefings, and respond to ad-hoc client requests. Invest an extra 1 hour weekly to follow up on client feedback and optimize tracking rules.

🚀 Getting Started

The first step for beginners is to search code hosting platforms, clone open-source investment research-related codebases, complete LLM interface configuration and financial report scraping rule debugging in a local environment, and successfully generate a quarterly financial report analysis report of a listed company as a demo case. This can be prioritized for presentation to potential clients such as local small private equity firms, investment communities, or small quantitative teams, entering the market with a low-priced first order.

🔑 Keys to Success

✅ Skillfully conduct secondary development based on open-source investment research agent code, avoiding the high time and capital costs of developing from scratch
✅ Accurately target small and medium-sized private equity firms and small quantitative teams who lack the ability to self-develop automated research systems but urgently need to improve financial report monitoring efficiency
✅ Strictly distinguish the boundaries between research information aggregation and investment advice to avoid financial compliance risks
✅ Establish a client-exclusive financial report tracking rule base to improve service repurchase and client referral rates

⚠️ 风险

⚠️ Open-source data scraping interfaces are prone to failure due to exchange website redesigns and announcement rule adjustments, requiring frequent maintenance patches
⚠️ Investment research briefings involve the dissemination of listed companies' financial data, posing implicit risks of data source copyright and financial compliance
⚠️ LLMs suffer from hallucination issues, which may lead to key financial data extraction errors and omission of risk warnings, triggering client complaints
⚠️ Risks of core research data leakage such as client holdings, necessitating on-premise deployment and permission isolation

📌 Real Cases

📌 A Shenzhen independent developer conducted secondary development based on the open-source Dayu Investment Research Agent codebase, providing financial anomaly monitoring outsourcing services for 3 small hot-money trading teams, with a monthly net profit of about 15,000 RMB
📌 A Hangzhou fintech enthusiast conducted secondary development based on the open-source Lobster-Research project, providing customized financial analysis services for 2 quantitative private equity firms, with monthly revenue exceeding 8,000 RMB
📌 A Chengdu freelancer packaged open-source financial analysis tools into a paid subscription service, providing weekly financial report briefing services to individual investors, with 120 paying users and a monthly revenue of about 18,000 RMB

Sources