Add ReadMe

README.md

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----
-title: Financial Analysis System
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 5.42.0
-app_file: app.py
-pinned: false
-hf_oauth: true
-hf_oauth_scopes:
-- inference-api
-license: mit
-short_description: Financial analysis system
----
-
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+# Multi-Agent Financial Analysis System
+
+## Overview
+
+The **Multi-Agent Financial Analysis System** is an advanced AI-driven platform designed to revolutionize the way financial data is processed and analyzed. The system employs a multi-agent architecture powered by agentic AI, leveraging specialized Large Language Models (LLMs) to analyze market news, earnings reports, and stock data. The ultimate goal is to deliver structured, actionable, and explainable investment insights for traders, investors, and financial analysts.
+
+## Objectives
+
+1. **Multi-Agent Architecture**  
+   Design a robust multi-agent system with distinct specialized roles, including:  
+   - News Analysis  
+   - Earnings Report Analysis  
+   - Market Trends Analysis  
+
+2. **Data Integration**  
+   Integrate real-time financial data sources such as APIs and news feeds to ensure accurate and timely analysis.
+
+3. **Explainable Insights**  
+   Generate transparent and explainable investment briefs and predictions, enabling users to make informed decisions.
+
+4. **Autonomous Workflows**  
+   Demonstrate autonomous capabilities by showcasing features like:  
+   - Planning investment tasks  
+   - Self-critique and evaluations  
+   - Iterative workflow improvements  
+
+## Key Features
+
+- **Agent-Based Processing**: The system utilizes multiple specialized agents that work collaboratively on segmented tasks to provide a holistic analysis.  
+- **Real-Time Data Analysis**: Connects to live financial data sources and processes high volumes of data in real-time.  
+- **Actionable Output**: Provides structured summaries and final investment recommendations tailored to specific investment scenarios.  
+- **Transparency**: Investment insights are backed by clear explanations, improving trust and usability.  
+
+## Technology Stack
+
+The system is built using modern AI technologies centered around Large Language Models (LLMs) and integrates with financial data APIs. The architecture includes:  
+- Python-based agent workflows.  
+- Specialized modules for financial data ingestion and processing.  
+- Autonomous feedback mechanisms for iterative improvement and decision-making.
+
+## Example Workflow
+
+Here is an example of how the system functions:
+1. A user provides a company stock ticker (e.g., `AAPL`).
+2. The system begins processing by splitting tasks across agents to analyze:
+   - News relevant to the stock.
+   - The company’s latest earnings information.
+   - Broader market trends impacting the stock.
+3. The agents collaborate, refine insights, and self-critique results, leading to an actionable investment recommendation.
+4. The final analysis provides:
+   - **News Summary**: Key relevant headlines and sentiment analysis.  
+   - **Earnings Summary**: Insights from the latest earnings reports.  
+   - **Market Summary**: Broader trends and their relationship to the selected stock.  
+   - **Final Recommendation**: A structured, actionable output.
+
+## How to Get Started
+
+To contribute or explore the system in action:
+1. Clone the project repository.
+2. Configure API keys and data sources as necessary.
+3. Run the main workflow function to generate insights.
+
+```python
+from workflow.agents_workflow import build_agents_workflow
+
+app = build_agents_workflow(llm_model_name="gpt-4o-mini")
+
+# Example usage:
+state = app.invoke({
+    "ticker": "AAPL",
+    "query": "Produce investor-ready insights for AAPL",
+    "news_summary": None,
+    "earnings_summary": None,
+    "market_summary": None,
+})
+```
+
+## Roadmap
+
+Future enhancements include:
+- Expanding the agent roles to support alternative asset classes (e.g., cryptocurrencies, bonds).  
+- Integrating more complex sentiment and risk analysis tools.  
+- Implementing an intuitive web-based user interface for broader accessibility.  
+
+## Contributions
+
+We welcome contributions from the community! Please review the contribution guidelines and feel free to submit pull requests or issues with suggestions and improvements.