Quantitative Finance With Python: A Practical Guide to Investment Management, Trading, and Financial Engineering 1st Edition by Chris Kelliher ISBN 1032014431 9781032014432

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ISBN 10: 1032014431 
ISBN 13: 9781032014432
Author: Chris Kelliher

Quantitative Finance with Python: A Practical Guide to Investment Management, Trading and Financial Engineering bridges the gap between the theory of mathematical finance and the practical applications of these concepts for derivative pricing and portfolio management. The book provides students with a very hands-on, rigorous introduction to foundational topics in quant finance, such as options pricing, portfolio optimization and machine learning. Simultaneously, the reader benefits from a strong emphasis on the practical applications of these concepts for institutional investors. Features Useful as both a teaching resource and as a practical tool for professional investors. Ideal textbook for first year graduate students in quantitative finance programs, such as those in master’s programs in Mathematical Finance, Quant Finance or Financial Engineering. Includes a perspective on the future of quant finance techniques, and in particular covers some introductory concepts of Machine Learning. 

Quantitative Finance With Python: A Practical Guide to Investment Management, Trading, and Financial Engineering 1st Table of contents:

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Quantitative Finance with Python: A Practical Guide to Investment Management

Table of Contents

Part I: Python Fundamentals for Finance

1. Introduction to Python for Financial Applications

   • Why Python for Finance?
   • Setting Up Your Python Environment (Anaconda, virtual environments)
   • Basic Python Syntax and Data Structures (lists, dictionaries, tuples, sets)
   • Control Flow (if/else, for loops, while loops)
   • Functions and Modules
   • Object-Oriented Programming (OOP) Basics

2. Essential Libraries for Data Analysis

   • NumPy for Numerical Computing (arrays, vectorized operations)
   • Pandas for Data Manipulation (DataFrames, Series)
   • Matplotlib and Seaborn for Data Visualization

3. Financial Data Acquisition and Handling

   • Sources of Financial Data (Yahoo Finance, Alpha Vantage, Quandl, etc.)
   • Downloading Historical Data
   • Cleaning and Preprocessing Financial Time Series Data
   • Handling Missing Values and Outliers
   • Resampling and Aggregating Data

Part II: Core Quantitative Finance Concepts

4. Time Value of Money and Basic Financial Calculations

   • Present Value, Future Value
   • Annuities, Perpetuities
   • Loan Amortization
   • Bond Valuation

5. Descriptive Statistics for Financial Returns

   • Calculating Returns (Simple vs. Log Returns)
   • Mean, Median, Mode, Standard Deviation, Variance
   • Skewness and Kurtosis
   • Histograms and Distribution Analysis

6. Portfolio Theory and Optimization

   • Modern Portfolio Theory (MPT) and Diversification
   • Expected Return and Risk of a Portfolio
   • Efficient Frontier and Optimal Portfolios
   • Capital Asset Pricing Model (CAPM)
   • Sharpe Ratio, Sortino Ratio

7. Risk Management

   • Value at Risk (VaR)
   • Conditional Value at Risk (CVaR) / Expected Shortfall
   • Stress Testing
   • Backtesting Risk Models

Part III: Advanced Topics and Applications

8. Option Pricing and Derivatives

   • Introduction to Options (Calls, Puts)
   • Binomial Option Pricing Model
   • Black-Scholes-Merton Model
   • Implied Volatility
   • The Greeks (Delta, Gamma, Vega, Theta, Rho)

9. Time Series Analysis in Finance

   • Stationarity, Autocorrelation, Partial Autocorrelation
   • ARIMA, GARCH Models
   • Cointegration
   • Volatility Modeling

10. Algorithmic Trading and Backtesting

    • Designing Trading Strategies (e.g., Mean Reversion, Momentum)
    • Backtesting Frameworks and Methodologies
    • Performance Metrics (CAGR, Max Drawdown, Sharpe, Alpha, Beta)
    • Pitfalls of Backtesting (Look-ahead bias, overfitting)

11. Machine Learning in Finance

    • Supervised Learning (Regression, Classification for prediction)
    • Unsupervised Learning (Clustering for market segmentation)
    • Feature Engineering for Financial Data
    • Applications in Algorithmic Trading and Risk Prediction

12. Fixed Income and Interest Rate Modeling

    • Bond Duration and Convexity
    • Yield Curve Analysis
    • Interest Rate Models (e.g., Vasicek, Cox-Ingersoll-Ross)

13. Monte Carlo Simulation in Finance

    • Generating Random Numbers
    • Simulating Stock Prices (Geometric Brownian Motion)
    • Monte Carlo Option Pricing
    • Portfolio Value Simulation

Part IV: Practical Considerations and Case Studies

14. Putting It All Together: Building a Comprehensive Investment Strategy

    • Case Studies of Quantitative Strategies
    • From Idea to Implementation
    • Data Pipelines and Automation

15. Best Practices and Future Trends

    • Code Optimization and Performance
    • Ethical Considerations in Quantitative Finance
    • Emerging Technologies (Blockchain, AI in Finance)
    • Career Paths in Quantitative Finance

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Tags: Chris Kelliher, Quantitative, Finance, Python