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To get started with quantitative trading in finance, you need to learn the following topics:

  1. Fundamental Financial Knowledge:
    • Financial markets and products (such as stocks, bonds, futures, options, etc.)
    • Investment theory and portfolio management (e.g., CAPM model, arbitrage pricing theory, etc.)
  2. Quantitative Trading Basics:
    • Concepts and processes of quantitative trading
    • Strategy development and testing (e.g., momentum strategies, mean reversion strategies, etc.)
  3. Programming and Data Analysis:
    • Programming languages (Python, R, C++, etc.)
    • Data analysis and processing (e.g., Pandas, NumPy, and other Python libraries)
    • Data visualization (e.g., Matplotlib, Seaborn, etc.)
  4. Mathematics and Statistics:
    • Basics of probability and statistics
    • Time series analysis
    • Numerical methods and optimization
  5. Financial Engineering and Numerical Methods:
    • Derivative pricing models (e.g., Black-Scholes model, etc.)
    • Risk management and VaR models
    • Quantitative risk models (e.g., GARCH models, etc.)
  6. Machine Learning and Artificial Intelligence:
    • Basics of machine learning (e.g., supervised learning, unsupervised learning, reinforcement learning, etc.)
    • Deep learning and neural networks
    • Applications in finance (e.g., predictive models, trade signal generation, etc.)
  7. Algorithms and High-Frequency Trading:
    • Trading algorithms (e.g., VWAP, TWAP, etc.)
    • Basics of high-frequency trading and technology (e.g., low latency networks, order book, etc.)
  8. Practical Experience and Projects:
    • Participating in quantitative trading competitions or internships
    • Designing, implementing, and testing your own quantitative trading strategies
    • Trading with real or simulated accounts

Recommended Learning Resources:

  1. Books:
    • "Quantitative Trading: How to Build Your Own Algorithmic Trading Business" by Ernest P. Chan
    • "Python for Finance: Analyze Big Financial Data" by Yves Hilpisch
    • "Statistical Arbitrage" by Andrew Pole
    • "Introduction to Machine Learning with Python" by Andreas C. Müller, Sarah Guido
  2. Online Courses and Websites:
    • Financial and data science courses on Coursera and edX
    • Projects and practice on websites like QuantStart and Kaggle
    • Related tutorial videos on YouTube
  3. Practical Tools and Platforms:
    • Quantitative trading platforms: such as QuantConnect, Zipline, Backtrader
    • Data sources: such as Quandl, Yahoo Finance, Alpha Vantage

By systematically learning these topics and combining practical experience and project work, you can gradually master the basic knowledge and skills required for quantitative trading in finance.