https://github.com/0xzee/financial-stock-analysis-agent
The Financial Analysis Crew is a Streamlit app that simplifies financial stock analysis. With the power of LLM-driven agents, users can seamlessly gather and analyze stock market data to generate comprehensive financial insights. Perfect for investors, analysts, and anyone interested in making data-driven financial decisions.
https://github.com/0xzee/financial-stock-analysis-agent
agent crewai groq llama3 llm mixtral-8x7b plotly task tool-calling tools yfinance
Last synced: 3 months ago
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The Financial Analysis Crew is a Streamlit app that simplifies financial stock analysis. With the power of LLM-driven agents, users can seamlessly gather and analyze stock market data to generate comprehensive financial insights. Perfect for investors, analysts, and anyone interested in making data-driven financial decisions.
- Host: GitHub
- URL: https://github.com/0xzee/financial-stock-analysis-agent
- Owner: 0xZee
- Created: 2025-01-17T14:09:27.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-17T15:07:11.000Z (9 months ago)
- Last Synced: 2025-01-17T15:31:37.370Z (9 months ago)
- Topics: agent, crewai, groq, llama3, llm, mixtral-8x7b, plotly, task, tool-calling, tools, yfinance
- Language: Python
- Homepage:
- Size: 168 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🌆 Financial Analysis Crew
The Financial Analysis Crew is a Streamlit application designed to assist in comprehensive financial stock analysis. It leverages the power of cutting-edge LLMs and tools to collect, analyze, and report on financial data efficiently.
---
## Interface :
## Features
- **Financial Data Collection:** Uses advanced agents to gather data for specified stock tickers.
- **Detailed Analysis Reports:** Generates in-depth financial reports using AI-driven analysis.
- **Interactive Interface:** Simple and intuitive interface powered by Streamlit.
---
## Requirements
- Python 3.8+
- Streamlit
- `yfinance`
- `crewai`
- Your API key for the `groq` models.
---
## How it works :
- Agent-Based Design:
The app employs two agents:
- Collector: Gathers stock data using YFinance and other tools.
- Reporter: Analyzes the collected data to produce actionable insights.
LLM Integration:
Utilizes groq/gemma2-9b-it and groq/mixtral-8x7b-32768 models for natural language understanding and response generation.
Interactive Display:
Users can input stock tickers and view real-time data and analysis results.
---
## Installation
1. Clone the repository:
```bash
git clone https://github.com/your-username/financial-analysis-crew.git
cd financial-analysis-crew
```
2. Installation :
```bash
pip install -r requirements.txt
```
3. Setup secrets :
Create a .streamlit/secrets.toml file and add
```bash
[GROQ_API]
GROQ_API = "your_api_key"
```
4. Run :
```bash
streamlit run app.py
```