LLM进化到现在，GPT4o独树一帜，在利用LLM极大提高工作中的效率，创造更多产品的同时，我希望能够GPT4o变成我某门课的老师，让我一步步地对一门课有一个比较健全且深入的认识。那这就涉及到如何设计一门我们想学的课。对于理工科大部分的学科来说，一门课的设计无非就是一个合适的学习路径，科学的教授方法和适当的练习。我们在用GPT4o来设计课程时，要同时注意输入和输出（费曼学习法）。于是，我将尝试用GPT4o来进行课程大纲规划、课程内容、课后阅读材料和homework。

课程大纲

Junior Level

Outline:

1. Introduction to Stock Markets:
• Overview of stock market basics (types of stocks, exchanges, market participants).
• Key concepts: stock prices, volume, market capitalization, dividends.
• Understanding financial data (OHLC data, moving averages, etc.).

2. Programming Basics for Quantitative Analysis:
• Introduction to Python for financial analysis:
• Data types, loops, conditionals.
• Python libraries: pandas, numpy, matplotlib.
• Loading and handling financial data:
• Fetching stock data from APIs (e.g., Yahoo Finance, Alpha Vantage).
• Cleaning and preprocessing data.

3. Basic Indicators and Analysis:
• Calculating moving averages (SMA, EMA).
• Introduction to basic indicators like RSI and MACD.
• Visualization of stock trends using matplotlib.

4. Mini Project:
• Develop a script to fetch stock data, compute simple indicators, and plot price movements with annotations.

Intermediate Level

Outline:

1. Intermediate Stock Market Concepts:
• Understanding risk and return (Sharpe ratio, drawdowns).
• Overview of sectors and indices.
• Basics of portfolio diversification.

2. Advanced Data Handling and Visualization:
• Handling multiple stocks data simultaneously.
• Introduction to time series analysis:
• Autocorrelation, stationarity tests.
• Visualizing performance metrics and comparisons.

3. Strategy Implementation Basics:
• Introduction to backtesting frameworks (e.g., backtrader, pyalgotrade).
• Implementing crossover strategies (e.g., SMA crossover).
• Introduction to stop-loss and take-profit mechanisms.

4. Mini Project:
• Design and backtest a simple moving average crossover strategy for a given stock.

Senior Level

Outline:

1. Advanced Quantitative Concepts:
• Mean reversion and momentum strategies.
• Factor models (Fama-French, multi-factor models).
• Understanding volatility (GARCH models).

2. Machine Learning for Stock Prediction:
• Introduction to supervised learning:
• Linear regression, decision trees, random forests.
• Feature engineering:
• Creating lagged features, technical indicators as inputs.
• Evaluation metrics: MSE, R-squared.

3. Advanced Backtesting Techniques:
• Walk-forward analysis.
• Handling transaction costs and slippage.
• Risk management: position sizing and portfolio optimization.

4. Mini Project:
• Build a predictive model to forecast stock price movement based on historical data and backtest its performance.

Expert Level

Outline:

1. Advanced Topics in Quantitative Finance:
• Options pricing models (Black-Scholes, binomial models).
• Statistical arbitrage and pairs trading.
• Deep learning applications in finance (LSTMs, transformers for time series).

2. Algorithm Design and Live Trading:
• Implementing live trading systems using APIs (e.g., Interactive Brokers, Alpaca).
• Building and deploying automated trading bots.
• Monitoring and maintaining trading systems in real-time.

3. Optimization and Performance Tuning:
• Hyperparameter optimization for machine learning models.
• Parallel processing for backtesting large datasets.
• Cloud deployment for scalable trading systems.

4. Capstone Project:
• Design, implement, and deploy an end-to-end quantitative trading strategy.
• Demonstrate live trading simulation with a comprehensive performance report.

Training Approach:

• Workshops: Weekly interactive sessions for theory and code walkthroughs.

• Assignments: Real-world problem-solving tasks aligned with the training content.

• Mentorship: Pair junior-level trainees with senior colleagues for guidance.

• Progress Assessment: Regular quizzes, coding challenges, and project presentations.