openmozi

openmozi / openfr

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OpenFR：A lightweight agent for financial research

agent fiancial financial-research llm
30
4
100% credibility
Found Feb 12, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

OpenFR is a simple chat-based assistant that uses AI to fetch and analyze financial data on Chinese and Hong Kong stocks, funds, futures, indices, and economic indicators.

How It Works

1
🔍 Discover OpenFR

You find a friendly chat tool that helps everyday people research stocks and markets by simply asking questions.

2
📥 Set it up quickly

Download and prepare the tool on your computer in just a couple of minutes.

3
🔗 Connect a smart helper

Link it to an AI service so your tool can understand questions and pull real market facts.

4
🚀 Start your chat

Open the conversation window and type something like 'Is Guizhou Moutai a good buy?' – it feels like talking to a stock expert.

5
📊 See it analyze

Watch as it plans smart steps, grabs current prices, news, and trends, then explains everything step by step.

💡 Get clear advice

You receive a complete, easy-to-read report with insights, charts, and buy/sell thoughts to guide your decisions.

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AI-Generated Review

What is openfr?

Openfr is a lightweight Python agent for financial research on Chinese markets, letting you query stock prices, fund rankings, futures trends, or macro data like CPI via natural language in a terminal. Fire up `openfr chat` or `openfr query "Is Guizhou Moutai a buy?"` and it plans steps, fetches live data from sources like East Money and Sina, then delivers analysis with pretty tables. Built on AKShare for data and any of 15+ LLMs (local or cloud), it skips heavy frameworks for quick setup.

Why is it gaining traction?

Unlike bloated quant platforms or generic LLM agents, openfr nails Chinese A-shares, HK stocks, and macros with smart tool selection, parallel fetches, caching, and fallback retries—handling flaky APIs without babysitting. The Rich-powered CLI shows plans, live results, and final insights in real-time, making research feel instant. Multi-LLM support (Zhipu default, but swap to Ollama or GPT) keeps costs low and works offline.

Who should use this?

Quant traders scanning hot sectors or validating buys on 600519/00700. Financial analysts prototyping ideas without Excel imports. Python devs building research bots who want AKShare wrapped in an agent, not raw API calls.

Verdict

Grab it for fast Chinese market queries if you're okay tinkering with .env LLMs—docs are solid, install is pip-simple, tests cover integration. At 16 stars and 1.0% credibility, it's early but MIT-licensed and battle-tested on real data; watch for stability as openfront-style tools evolve.

(198 words)

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