Project: AI Stock Trading Assistant
The "ai_quant_trade" project is a comprehensive platform designed to assist individuals and institutions in trading stocks using artificial intelligence (AI) technologies. It provides everything from learning resources to tools for live trading, making it suitable for both professionals and beginners with or without programming skills.
Key Features
- All-in-One Solution: A guided path from learning to simulation and real-time trading.
- Trading Strategies: Includes strategies like factor exploration, traditional rules, machine learning, deep learning, reinforcement learning, graph networks, and high-frequency trading.
- Tools for Better Trading: Offers tools for tracking stocks and recommending investments.
- Live Trading Deployment: Supports deployment using various technologies, including C++, CPU, and GPU.
Recent Updates
- Emotions analysis using StructBERT.
- Multi-stock trading using reinforcement learning, achieving up to 53% annual return.
- Automated factor mining and stock trend prediction using machine learning.
- Customized market monitoring software.
- Local deep reinforcement learning strategies.
- Local real trading simulation with Wind.
- Basic backtesting framework with dual moving average strategy.
Who Can Benefit?
- Institutions: Can leverage powerful AI tools to enhance trading strategies.
- Retail Investors: Whether they have a background in programming or not, the platform provides scalable solutions for everyone.
Project Structure
- Educational Resources: Comprehensive teaching materials ranging from OS basics to advanced AI strategies.
- Documentation: User guides and instructional content.
- Auxiliary Tools: Software for market monitoring and stock tracking.
- Factor Libraries: A library of thousands of factors for deeper analysis.
- Data Processing: Unified interface for handling various data sources.
- Text Analysis: Tools for analyzing market sentiment and other textual data.
How to Use?
- Install Required Libraries: With a single command using Python's package manager.
- Explore Strategy Examples: Explore different strategy folders for practical examples.
- Local Quantitative Platform: Build a quantitative trading system right on your local system with AI-driven strategies.
Local Quantitative Trading
- Reinforcement Learning: Emulates interactive goal-directed strategies, using lessons learned from games such as AlphaGo.
- Graph Network Strategies: Promises to link various stocks for better relationship mapping.
- Deep Learning and Machine Learning: Utilizes popular techniques for price and trend predictions.
- High-Frequency Trading: For those interested in minute-by-minute market actions.
- Traditional Strategies: Although more static, these rules-based tactics can still provide value.
Real Trading Tools
- Live Simulation: Features like Wind real-market simulation for strategy testing.
Support and Add-ons
- Customizable Monitoring Tools: Tailor tools according to personal or institutional needs.
- Factor Mining: Assistance in extracting trading factors with machine learning techniques.
- Text Analysis: Use AI for sentiment analysis to gauge market mood effectively.
AI Practice Guide
A comprehensive guide on AI applications includes topics like:
- Systems and platforms
- Programming practices
- Mathematics for programmers
- Algorithm principles
Online Research Platform
- Uqer and JoinQuant: For strategies on online research platforms, with integrated toolkits.
Connect and Collaborate
For more engagement and support, they encourage participation in online communities and offer ample technical support and common FAQ resolutions.
Overall, the "ai_quant_trade" project aims to provide an end-to-end, AI-driven trading experience to streamline the process and maximize trading outcome efficiency.