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Python for Quant Finance Building Financial Models and Trading Algorithms (Johns.epub
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内容非常精彩，1000多页的大型资料，实用，翔实，全部矢量文字适合投喂给大模型等工具，翻译也极为方便。
Contents
1 Introduction to Quantitative Finance with Python1.1 What is Quantitative Finance?1.2 Essential Concepts in Quantitative Finance1.3 Basic Financial Instruments1.4 Overview of Python for Finance1.5 Setting Up the Python Environment1.6 First Steps with Python in Finance1.7 Numerical Computing with NumPy1.8 Data Analysis with Pandas1.9 Visualization of Financial Data 1.10 Case Study: Simple Financial Calculations with Python2 Basic Python Programming for Finance2.1 Python Basics: Variables and Data Types2.2 Control Structures: Conditionals and Loops2.3 Functions and Modules2.4 Working with Lists and Dictionaries2.5 Handling Strings in Python2.6 File Input and Output2.7 Understanding Errors and Exceptions2.8 Using Libraries for Financial Computations2.9 Introduction to Python Classes and Objects 2.10 Practical Exercises: Financial Calculations and Scripts3 Handling Financial Data with Pandas3.1 Overview of Pandas Library3.2 Data Structures in Pandas: Series and DataFrame3.3 Importing Financial Data with Pandas3.4 Data Cleaning and Preprocessing3.5 Handling Missing Data in Financial Time Series3.6 Data Indexing and Slicing3.7 Data Aggregation and Group Operations3.8 Time Series Manipulation with Pandas3.9 Merging, Joining, and Concatenating DataFrames3.10 Visualization of Financial Data with Pandas4 Time Series Analysis4.1 Understanding Time Series Data4.2 Statistical Properties of Time Series4.3 Data Visualization Techniques for Time Series4.4 Decomposing Time Series into Components4.5 Stationarity and Its Importance4.6 Autocorrelation and Partial Autocorrelation4.7 Time Series Smoothing Techniques4.8 Moving Averages and Exponential Smoothing4.9 Seasonal Decomposition of Time Series 4.10 Modeling Time Series with ARIMA4.11 Advanced Models: GARCH and VAR4.12 Case Study: Analyzing Financial Time Series Data5 Mathematical Foundations for Quantitative Finance5.1 Importance of Mathematics in Quantitative Finance5.2 Basic Concepts in Probability and Statistics5.3 Random Variables and Probability Distributions5.4 Stochastic Processes and Brownian Motion5.5 Time Series and Forecasting Models5.6 Linear Algebra for Financial Models5.7 Calculus in Finance: Optimization Techniques5.8 Monte Carlo Simulations5.9 Numerical Methods for Finance5.10 Introduction to Differential Equations5.11 Case Study: Mathematical Modeling in Finance6 Portfolio Optimization6.1 Basic Concepts of Portfolio Theory6.2 Risk and Return Metrics6.3 Diversification and Portfolio Risk6.4 Mean-Variance Optimization6.5 Efficient Frontier and Capital Market Line6.6 Covariance and Correlation in Portfolio Construction 6.7 Constraint Handling in Portfolio Optimization6.8 Sharpe Ratio and Other Performance Metrics6.9 Advanced Optimization Techniques6.10 Practical Implementation of Portfolio Optimization6.11 Case Study: Real-world Portfolio Optimization7 Risk Management and Metrics7.1 Fundamentals of Risk Management7.2 Types of Financial Risks7.3 Risk Metrics and Measurements7.4 Value at Risk (VaR)7.5 Expected Shortfall (ES)7.6 Stress Testing and Scenario Analysis7.7 Credit Risk Management7.8 Liquidity Risk Management7.9 Market Risk Models7.10 Operational Risk Management7.11 Regulatory Frameworks and Compliance7.12 Case Study: Risk Management in Practice8 Valuation of Financial Instruments8.1 Principles of Valuation8.2 Valuation of Equities8.3 Valuation of Bonds 8.4 Valuation of Derivatives8.5 Options Pricing Models: Black-Scholes and Binomial8.6 Valuing Futures and Forwards8.7 Interest Rate Models8.8 Credit Derivatives Valuation8.9 Valuing Swaps and Other Exotic Instruments8.10 Arbitrage Pricing Theory8.11 Practical Applications in Valuation9 Algorithmic Trading Strategies9.1 Overview of Algorithmic Trading9.2 Types of Algorithmic Trading Strategies9.3 Momentum Trading Strategies9.4 Mean Reversion Trading Strategies9.5 Arbitrage Strategies9.6 High-Frequency Trading (HFT)9.7 Machine Learning in Algorithmic Trading9.8 Risk Management in Algorithmic Trading9.9 Implementing Algorithmic Strategies in Python9.10 Case Study: Developing a Simple Trading Algorithm9.11 Ethical Considerations in Algorithmic Trading10 Backtesting Trading Strategies10.1 Concept of Backtesting 10.2 Setting Up the Backtesting Environment10.3 Historical Data Collection10.4 Financial Metrics for Backtesting10.5 Developing a Backtesting Framework10.6 Handling Transaction Costs and Slippage10.7 Evaluating Strategy Performance10.8 Walk-Forward Optimization10.9 Common Pitfalls in Backtesting10.10 Case Study: Backtesting a Momentum Strategy10.11 Monte Carlo Simulation in Backtesting11 Machine Learning in Quantitative Finance11.1 Overview of Machine Learning in Finance11.2 Supervised Learning Algorithms11.3 Unsupervised Learning Algorithms11.4 Machine Learning Model Evaluation11.5 Data Preprocessing for Machine Learning11.6 Feature Engineering and Selection11.7 Time Series Forecasting with Machine Learning11.8 Natural Language Processing in Finance11.9 Reinforcement Learning for Trading11.10 Implementing Machine Learning Models in Python11.11 Case Study: Predicting Stock Prices with Machine Learning 11.12 Challenges and Future Directions in Machine Learning for Finance12 Advanced Topics in Quantitative Finance12.1 Advanced Derivative Models12.2 Quantitative Asset Allocation12.3 Stochastic Volatility Models12.4 Credit Risk Modeling12.5 Algorithmic Execution and Market Microstructure12.6 Bayesian Methods in Finance12.7 Financial Econometrics12.8 Optimal Control in Finance12.9 Blockchain and Cryptocurrency Modeling12.10 Quantitative Approaches to ESG Investing12.11 Case Studies: Complex Financial Instruments12.12 Frontiers of Research in Quantitative Finance
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