Want to say goodbye to the blind operations of traditional manual stock trading and master the core capabilities of intelligent quantitative trading? Geek Time's AI Quantitative Trading Training Camp offers a 12-week systematic advanced course that takes you from zero into AI quantitative trading and helps you build your own automated trading system. The course is taught step by step, starting from financial fundamentals, trading indicators, and fundamental and technical analysis to build a solid foundation, then providing hands-on instruction in practical skills such as AI programming tools, market and research report data collection, and database setup. It deeply breaks down classic strategies such as MACD, Chan Theory, grid trading, turtle trading, and multi-factor stock selection, and verifies strategy feasibility through a professional backtesting engine. In the advanced stage, you will unlock high-level technologies such as RAG vector databases, machine learning, and reinforcement learning, with practical exercises in core topics including building AI trading agents, event-driven analysis, live trading risk control, and sector rotation assessment. The course includes a full set of source code, industry research reports, exclusive learning materials, and post-class Q&A, balancing zero-foundation entry with advanced capability improvement. It is fully practical and avoids empty theory, helping learners thoroughly master the entire AI quantitative trading chain and use technology to empower investment decisions.

The course emphasizes reusable research workflows and engineering implementation capabilities. You will learn how to connect data such as market data, financials, news, and research reports into a unified analysis framework, how to screen strategies using backtesting metrics, how to improve signal quality through machine learning and reinforcement learning, and how to organize investment research workflows using agents, RAG, and Langgraph. After learning, you will be able to independently complete the full chain from data collection, factor construction, strategy backtesting, and performance attribution to risk control and simulated trading, and also understand the key details of live trading integration, order splitting, circuit-breaker retries, and mobile collaboration. Suitable for learners who want to systematically transition to quantitative trading, improve investment research efficiency, or develop AI trading tools.

Friendly reminder: This course is for knowledge learning only and does not constitute investment advice. Investing involves risk, and caution is required when entering the market.

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• 1.Kickoff livestream replay
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• 2.What is quantitative trading
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• 3.Understanding financial instruments
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• 4.Getting started with quantitative strategies
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• 5.Basic financial concepts
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• 6.Technical analysis vs fundamental analysis
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• 7.The core logic of technical indicators
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• 8.AI programming and corresponding tools
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• 9.QMT operation instructions
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• 10.Data acquisition
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• 11.How financial indicators guide quantitative trading
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• 12.Building a database
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• 13.Market and financial data collection
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• 14.Macroeconomic data and news event collection
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• 15.Institutional research reports and financial calendar collection
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• 16.Large model-driven catalyst events
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• 17.What is a backtesting engine
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• 18.Backtesting evaluation metrics
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• 19.Case analysis: MACD strategy, RSI strategy, Bollinger Bands strategy
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• 20.Case analysis: bias strategy, momentum strategy, etc.
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• 21.Post-class Q&A
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• 22.What is TA-Lib
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• 23.Limitations of classic indicators
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• 24.Enhancement of traditional indicators
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• 25.Installing OpenClaw
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• 26.Configuring quantitative tools for OpenClaw
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• 27.How to create a skill
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• 28.Core principles of turtle trading rules
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• 29.Turtle trading strategy case
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• 30.Reflection and Q&A
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• 31.Classic Chan Theory Quantitative Strategy
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• 32.Chan Theory Buy/Sell Points in Practice with Case Studies
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• 33.Chan Theory Enhanced Strategy
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• 34.Post-class Q&A
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• 35.Classic Grid Trading Strategy
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• 36.Multi-factor stock selection
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• 37.Factor Classification System
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• 38.Huatai Securities Quantitative Research Framework
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• 39.Machine Learning Fundamentals
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• 40.Case Studies of Machine Learning Applications in the Stock Market
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• 41.Five-Step Paper Reproduction Method
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• 42.Master's Thesis Interpretation
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• 43.Case Analysis
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• 44.QuantStats Metric System
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• 45.Matrix Decomposition
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• 46.Applications of Matrix Decomposition
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• 47.Live Trading Performance Analysis
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• 48.What Is RAG
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• 49.Embedding
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• 50.Vector Database
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• 51.Guotai Junan Five-Step Method
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• 52.Writing AI Investment Research Reports
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• 53.DeepAgent Framework
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• 54.Sentiment Acquisition and Financial Sentiment Analysis
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• 55.Event-Driven and Panic Index Analysis
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• 56.Multi-Dimensional Decision Making
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• 57.Post-class Q&A
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• 58.Xtquant Live Trading
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• 59.Nanobot in Practice
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• 60.Nanobot Programming Interface
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• 61.What Is Reinforcement Learning
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• 62.From Q-learning to DQN
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• 63.Intelligent Order Splitting
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• 64.DQN's Student-Teacher Dual Network Mechanism
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• 65.High-Frequency Market Making and Main Force Behavior Identification
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• 66.Risk Control System
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• 67.ATR Risk Control
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• 68.Event-Driven Risk Control in Practice
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• 69.Langgraph Core Five-Piece Set
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• 70.Trading Team Workflow
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• 71.Risk Control Interception and Circuit-Breaker Retry
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• 72.Investment Morning Meeting Scenarios
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• 73.Multi-factor stock selection
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• 74.Sector Rotation Analysis
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• 75.Investment Morning Meeting Workflow
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• 76.Live Trading Operations
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• 77.CEO Console and Leader Strategy
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• 78.BHB Attribution Model
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• 79.Forward Validation
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• 80.Student Sharing
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• 81.Quantitative Trading System
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• Weekly resource packages, industry research report collections, Active Portfolio Management, Factor Investing: Methods and Practice, A Step-by-Step Guide to Reading Financial Statements, Quantitative Trading, Systematic Learning Notes on Quantitative Investment, AI-generated research report PDFs, course code, AI Quantitative Trading Learning and QA, and related software usage.
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