AionVanguard - Autonomous Trading Agent

This project is an autonomous trading agent designed to execute trades based on a combination of technical analysis strategies. It features a modular architecture and a user interface built with Streamlit for easy monitoring and control.

Table of Contents

• Overview
• Features
• Deployment to Streamlit Cloud
• Local Setup
• Configuration
• Code Structure
• Testing

Overview

AionVanguard is a Python-based autonomous trading agent. It uses a combination of strategies to identify trading opportunities and includes modules for risk management, broker integration, and dynamic adaptation to market conditions.

Features

• Core Strategy: Combines PVG (Price-Volume-Gradient), SMC (Smart Money Concepts), and TPR (Trend-Pullback-Reversal) analysis.
• Risk Management: Implements position sizing, stop loss, take profit, and daily risk limits.
• Broker Flexibility: Modular design allows for integration with brokers like Alpaca, Binance, etc.
• Live Dashboard: A Streamlit-based UI for real-time monitoring, control, and performance tracking.

Deployment to Streamlit Cloud

The easiest way to deploy this application is using Streamlit Cloud, which integrates directly with your GitHub repository.

1. Click the Deploy Button: Click the "Deploy to Streamlit Cloud" button at the top of this README.
2. Connect Your Account: If you haven't already, you'll be prompted to connect your GitHub account to Streamlit Cloud.
3. Deploy: Follow the on-screen instructions. Streamlit Cloud will automatically detect the repository and the streamlit_app.py file and deploy the application.
4. Add Secrets: Once deployed, you will need to add your broker API keys as secrets in the Streamlit Cloud settings for your app. Go to your app's settings (... -> Settings -> Secrets) and add your keys (e.g., ALPACA_API_KEY_ID, ALPACA_API_SECRET_KEY).

Local Setup

1. Clone the repository:

   git clone https://github.com/Hali-creater/AionVanguard.git
   cd AionVanguard

2. Install dependencies:

   pip install -r requirements.txt

3. Launch the Dashboard:

   streamlit run streamlit_app.py

Configuration

The agent is configured using environment variables. For local development, you can create a .env file in the project root. For Streamlit Cloud deployment, use the built-in Secrets management.

Example .env file:

# --- Broker Configuration ---
BROKER=Alpaca
ALPACA_API_KEY_ID=YOUR_ALPACA_API_KEY_ID
ALPACA_API_SECRET_KEY=YOUR_ALPACA_API_SECRET_KEY
ALPACA_BASE_URL=https://paper-api.alpaca.markets

Code Structure

.
├── README.md
├── requirements.txt
├── .env.example
├── streamlit_app.py
└── autonomous_trading_agent/
    ├── __init__.py
    ├── adaptability/
    ├── broker_integration/
    ├── data_fetching/
    ├── execution/
    ├── risk_management/
    ├── strategy/
    └── tests/

Testing

The project includes a tests/ directory with unit tests. Run tests using pytest from the project root:

pytest