Books & Videos

Table of Contents

  1. Python and Finance

    1. Chapter 1 Why Python for Finance?

      1. What Is Python?
      2. Technology in Finance
      3. Python for Finance
      4. Conclusions
      5. Further Reading
    2. Chapter 2 Infrastructure and Tools

      1. Python Deployment
      2. Tools
      3. Conclusions
      4. Further Reading
    3. Chapter 3 Introductory Examples

      1. Implied Volatilities
      2. Monte Carlo Simulation
      3. Technical Analysis
      4. Conclusions
      5. Further Reading
  2. Financial Analytics and Development

    1. Chapter 4 Data Types and Structures

      1. Basic Data Types
      2. Basic Data Structures
      3. NumPy Data Structures
      4. Vectorization of Code
      5. Conclusions
      6. Further Reading
    2. Chapter 5 Data Visualization

      1. Two-Dimensional Plotting
      2. Financial Plots
      3. 3D Plotting
      4. Conclusions
      5. Further Reading
    3. Chapter 6 Financial Time Series

      1. pandas Basics
      2. Financial Data
      3. Regression Analysis
      4. High-Frequency Data
      5. Conclusions
      6. Further Reading
    4. Chapter 7 Input/Output Operations

      1. Basic I/O with Python
      2. I/O with pandas
      3. Fast I/O with PyTables
      4. Conclusions
      5. Further Reading
    5. Chapter 8 Performance Python

      1. Python Paradigms and Performance
      2. Memory Layout and Performance
      3. Parallel Computing
      4. multiprocessing
      5. Dynamic Compiling
      6. Static Compiling with Cython
      7. Generation of Random Numbers on GPUs
      8. Conclusions
      9. Further Reading
    6. Chapter 9 Mathematical Tools

      1. Approximation
      2. Convex Optimization
      3. Integration
      4. Symbolic Computation
      5. Conclusions
      6. Further Reading
    7. Chapter 10 Stochastics

      1. Random Numbers
      2. Simulation
      3. Valuation
      4. Risk Measures
      5. Conclusions
      6. Further Reading
    8. Chapter 11 Statistics

      1. Normality Tests
      2. Portfolio Optimization
      3. Principal Component Analysis
      4. Bayesian Regression
      5. Conclusions
      6. Further Reading
    9. Chapter 12 Excel Integration

      1. Basic Spreadsheet Interaction
      2. Scripting Excel with Python
      3. xlwings
      4. Conclusions
      5. Further Reading
    10. Chapter 13 Object Orientation and Graphical User Interfaces

      1. Object Orientation
      2. Graphical User Interfaces
      3. Conclusions
      4. Further Reading
    11. Chapter 14 Web Integration

      1. Web Basics
      2. Web Plotting
      3. Rapid Web Applications
      4. Web Services
      5. Conclusions
      6. Further Reading
  3. Derivatives Analytics Library

    1. Chapter 15 Valuation Framework

      1. Fundamental Theorem of Asset Pricing
      2. Risk-Neutral Discounting
      3. Market Environments
      4. Conclusions
      5. Further Reading
    2. Chapter 16 Simulation of Financial Models

      1. Random Number Generation
      2. Generic Simulation Class
      3. Geometric Brownian Motion
      4. Jump Diffusion
      5. Square-Root Diffusion
      6. Conclusions
      7. Further Reading
    3. Chapter 17 Derivatives Valuation

      1. Generic Valuation Class
      2. European Exercise
      3. American Exercise
      4. Conclusions
      5. Further Reading
    4. Chapter 18 Portfolio Valuation

      1. Derivatives Positions
      2. Derivatives Portfolios
      3. Conclusions
      4. Further Reading
    5. Chapter 19 Volatility Options

      1. The VSTOXX Data
      2. Model Calibration
      3. American Options on the VSTOXX
      4. Conclusions
      5. Further Reading
    6. Appendix Selected Best Practices

    7. Appendix Call Option Class

    8. Appendix Dates and Times