Update README.md

README.md

@@ -14,9 +14,100 @@ short_description: Combines cryptocurrency insights with AI-driven analytics.
 
 ---
 
-# Welcome to Streamlit!
+This file should be placed in the root directory of your project. It's written in Markdown.
+Generated markdown
+# 🤖 Sentinel Arbitrage Engine
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+**Sentinel is a high-frequency, AI-powered arbitrage detection engine for cryptocurrency markets. It autonomously monitors real-time price dislocations between major decentralized oracles and provides AI-generated risk analysis and trading strategies.**
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+This application is designed to identify and analyze fleeting arbitrage opportunities that exist between different price-reporting networks in the DeFi space. It uses a robust, multi-asset architecture and leverages Google's Gemini Pro for sophisticated, real-time decision support.
+
+---
+
+## ✨ Core Features
+
+*   **Multi-Asset Monitoring:** Continuously tracks prices for multiple crypto assets (BTC, ETH, SOL, etc.) across different data sources simultaneously.
+*   **Decentralized & Resilient:** Queries globally-accessible, censorship-resistant oracles (Pyth and Chainlink aggregators) to avoid CEX geoblocking and rate-limiting issues.
+*   **AI-Powered Alpha Briefings:** For every detected opportunity, it uses the Gemini Pro API to generate a concise briefing, including:
+    *   **Risk Assessment** (LOW, MEDIUM, HIGH)
+    *   **Execution Strategy** (e.g., "Execute a flash loan arbitrage...")
+    *   **Rationale** (The "why" behind the risk assessment)
+*   **Real-Time WebSocket UI:** The frontend uses a professional, Socket.IO-powered dashboard to display signals with millisecond latency. The UI is clean, data-dense, and built for at-a-glance interpretation.
+*   **Asynchronous Architecture:** Built with Python, FastAPI, and `asyncio`, the entire engine is asynchronous from the ground up, ensuring high performance and concurrency.
+
+## 🛠️ Tech Stack
+
+*   **Backend:** Python 3.9+, FastAPI
+*   **Real-Time Communication:** `python-socketio`
+*   **Data Fetching:** `httpx` (for async HTTP requests)
+*   **AI Engine:** Google Gemini Pro
+*   **Data Sources:**
+    *   Pyth Network (On-chain data)
+    *   CoinGecko (Off-chain aggregated data)
+*   **Frontend:** Vanilla JavaScript with the Socket.IO Client
+*   **Styling:** Pico.css
+
+## 🚀 Getting Started
+
+### 1. Prerequisites
+
+*   Python 3.9+
+*   An account with [Hugging Face](https://huggingface.co/) to deploy as a Space (recommended).
+*   API Keys for:
+    *   **Google Gemini:** Obtain from [Google AI Studio](https://aistudio.google.com/).
+    *   **(Optional but Recommended)** **CoinGecko:** A free or Pro key from [CoinGecko API](https://www.coingecko.com/en/api).
+
+### 2. Project Structure
+
+The project uses a standard package structure for scalability and maintainability.
+Use code with caution.
+Markdown
+/
+├── app/
+│ ├── init.py
+│ ├── arbitrage_analyzer.py
+│ ├── broker.py
+│ ├── main.py
+│ └── price_fetcher.py
+├── static/
+│ └── index.html
+├── .gitignore
+├── Dockerfile
+└── requirements.txt
+Generated code
+### 3. Installation & Setup
+
+1.  **Clone the repository:**
+    ```bash
+    git clone https://huggingface.co/spaces/mgbam/CryptoSentinel_AI
+    cd CryptoSentinel_AI
+    ```
+
+2.  **Install dependencies:**
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+3.  **Configure Environment Secrets:**
+    *   If running locally, create a `.env` file and add your API key:
+        ```
+        GEMINI_API_KEY="your_gemini_api_key_here"
+        ```
+    *   If deploying on Hugging Face Spaces, add `GEMINI_API_KEY` as a repository secret in your Space's **Settings** tab.
+
+### 4. Running the Engine
+
+The application is run using `uvicorn`. From the root directory of the project, execute:
+
+```bash
+uvicorn app.main:app --host 0.0.0.0 --port 7860 --reload
+Use code with caution.
+--reload enables hot-reloading for development. Remove this flag for production.
+Once running, navigate to http://127.0.0.1:7860 in your browser to view the Sentinel Arbitrage Engine dashboard.
+⚙️ How It Works
+Lifespan Management: On startup, the lifespan manager in app/main.py initializes all necessary services (PriceFetcher, ArbitrageAnalyzer) and launches the main run_arbitrage_detector loop as a persistent background task.
+Data Fetching: The PriceFetcher runs in the background loop, making concurrent async calls to the Pyth and CoinGecko APIs to get the latest prices for all configured assets.
+Discrepancy Detection: The loop compares the prices from the two oracles for each asset. If the percentage difference exceeds the OPPORTUNITY_THRESHOLD, it's flagged as a potential arbitrage opportunity.
+AI Analysis: The detected opportunity data is passed to the ArbitrageAnalyzer, which constructs a detailed prompt for the Gemini API.
+Signal Emission: Gemini's structured response (Risk, Strategy, Rationale) is combined with the price data into a final "signal" object. This signal is then broadcast to all connected clients using sio.emit('new_signal', ...).
+Real-Time UI: The static/index.html page connects to the Socket.IO server. A JavaScript listener for the new_signal event receives the data and dynamically constructs a new table row, prepending it to the live signal stream.