Skip to content

README.md

Latest commit

 

History

History
30 lines (22 loc) · 2.26 KB

README.md

File metadata and controls

30 lines (22 loc) · 2.26 KB
Raw

Autonomous-RL-Trading-Bot

This project is a professional-grade autonomous trading assistant that combines Reinforcement Learning, Technical Analysis, and Market Sentiment Analysis. The system scans market data, calculates advanced technical indicators, and generates AI-driven buy/sell signals.

Key Features

  • PPO-Based Decision Engine: Autonomous decision-making using deep neural networks trained with the Stable Baselines3 PPO algorithm.
  • Market Intelligence: Real-time financial news scanning, Reddit discussion tracking, and sentiment analysis using FinBERT.
  • Advanced Risk Management: Implementation of Kelly Criterion, Volatility Scaling, and dynamic Position Sizing to protect capital.
  • Integrated Alert System: Real-time push notifications of AI decisions and trading signals via Telegram.
  • Live Paper Trading: Strategy testing using live market data within a simulated paper trading environment.

Technical Stack

  • RL Environment: Custom Gymnasium implementation (StockTradingEnv).
  • Data Source: Real-time and historical price data provided via yfinance.
  • Technical Indicators: Calculation of RSI, MACD, Bollinger Bands, ATR, OBV, and other quantitative metrics.
  • UI/UX: Interactive dashboard built with Streamlit and advanced financial charting using Plotly.

Quick Start

  1. Install Dependencies: 
    pip install -r requirements.txt

Technical Notes and Usage

  • Execution Command: The command streamlit run app/app.py initializes a local web server and triggers essential setup functions such as apply_styles() and init_session_state() within the main application file.
  • Dependencies: It is critical that all libraries listed in requirements.txt—specifically stable-baselines3 and torch—are installed correctly to prevent errors during model loading in rl_agent.py.
  • Sidebar Integration: The user interface utilizes sidebar components for core functionalities including model training (train_agent), asset selection, and Telegram notification configuration via the SignalBroadcaster.
  • Directory Structure: The application expects a specific directory structure for saving and loading AI models; specifically, it utilizes the models/trained/ path for storing .zip and .pkl files.