Python for Finance. Mastering Data-Driven Finance. 2nd Edition

Spis treści ebooka

• Preface
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Safari
  • How to Contact Us
  • Acknowledgments
• I. Python and Finance
• 1. Why Python for Finance
  • The Python Programming Language
    • A Brief History of Python
    • The Python Ecosystem
    • The Python User Spectrum
    • The Scientific Stack
  • Technology in Finance
    • Technology Spending
    • Technology as Enabler
    • Technology and Talent as Barriers to Entry
    • Ever-Increasing Speeds, Frequencies, and Data Volumes
    • The Rise of Real-Time Analytics
  • Python for Finance
    • Finance and Python Syntax
    • Efficiency and Productivity Through Python
      • Shorter time-to-results
      • Ensuring high performance
    • From Prototyping to Production
  • Data-Driven and AI-First Finance
    • Data-Driven Finance
    • AI-First Finance
  • Conclusion
  • Further Resources
• 2. Python Infrastructure
  • conda as a Package Manager
    • Installing Miniconda
    • Basic Operations with conda
  • conda as a Virtual Environment Manager
  • Using Docker Containers
    • Docker Images and Containers
    • Building an Ubuntu and Python Docker Image
  • Using Cloud Instances
    • RSA Public and Private Keys
    • Jupyter Notebook Configuration File
    • Installation Script for Python and Jupyter Notebook
    • Script to Orchestrate the Droplet Setup
  • Conclusion
  • Further Resources
• II. Mastering the Basics
• 3. Data Types and Structures
  • Basic Data Types
    • Integers
    • Floats
    • Booleans
    • Strings
    • Excursion: Printing and String Replacements
    • Excursion: Regular Expressions
  • Basic Data Structures
    • Tuples
    • Lists
    • Excursion: Control Structures
    • Excursion: Functional Programming
    • Dicts
    • Sets
  • Conclusion
  • Further Resources
• 4. Numerical Computing with NumPy
  • Arrays of Data
    • Arrays with Python Lists
    • The Python array Class
  • Regular NumPy Arrays
    • The Basics
    • Multiple Dimensions
    • Metainformation
    • Reshaping and Resizing
    • Boolean Arrays
    • Speed Comparison
  • Structured NumPy Arrays
  • Vectorization of Code
    • Basic Vectorization
    • Memory Layout
  • Conclusion
  • Further Resources
• 5. Data Analysis with pandas
  • The DataFrame Class
    • First Steps with the DataFrame Class
    • Second Steps with the DataFrame Class
  • Basic Analytics
  • Basic Visualization
  • The Series Class
  • GroupBy Operations
  • Complex Selection
  • Concatenation, Joining, and Merging
    • Concatenation
    • Joining
    • Merging
  • Performance Aspects
  • Conclusion
  • Further Reading
• 6. Object-Oriented Programming
  • A Look at Python Objects
    • int
    • list
    • ndarray
    • DataFrame
  • Basics of Python Classes
  • Python Data Model
  • The Vector Class
  • Conclusion
  • Further Resources
• III. Financial Data Science
• 7. Data Visualization
  • Static 2D Plotting
    • One-Dimensional Data Sets
    • Two-Dimensional Data Sets
    • Other Plot Styles
  • Static 3D Plotting
  • Interactive 2D Plotting
    • Basic Plots
    • Financial Plots
  • Conclusion
  • Further Resources
• 8. Financial Time Series
  • Financial Data
    • Data Import
    • Summary Statistics
    • Changes over Time
    • Resampling
  • Rolling Statistics
    • An Overview
    • A Technical Analysis Example
  • Correlation Analysis
    • The Data
    • Logarithmic Returns
    • OLS Regression
    • Correlation
  • High-Frequency Data
  • Conclusion
  • Further Resources
• 9. Input/Output Operations
  • Basic I/O with Python
    • Writing Objects to Disk
    • Reading and Writing Text Files
    • Working with SQL Databases
    • Writing and Reading NumPy Arrays
  • I/O with pandas
    • Working with SQL Databases
    • From SQL to pandas
    • Working with CSV Files
    • Working with Excel Files
  • I/O with PyTables
    • Working with Tables
    • Working with Compressed Tables
    • Working with Arrays
    • Out-of-Memory Computations
  • I/O with TsTables
    • Sample Data
    • Data Storage
    • Data Retrieval
  • Conclusion
  • Further Resources
• 10. Performance Python
  • Loops
    • Python
    • NumPy
    • Numba
    • Cython
  • Algorithms
    • Prime Numbers
      • Python
      • Numba
      • Cython
      • Multiprocessing
    • Fibonacci Numbers
      • Recursive algorithm
      • Iterative algorithm
    • The Number Pi
  • Binomial Trees
    • Python
    • NumPy
    • Numba
    • Cython
  • Monte Carlo Simulation
    • Python
    • NumPy
    • Numba
    • Cython
    • Multiprocessing
  • Recursive pandas Algorithm
    • Python
    • Numba
    • Cython
  • Conclusion
  • Further Resources
• 11. Mathematical Tools
  • Approximation
    • Regression
      • Monomials as basis functions
      • Individual basis functions
      • Noisy data
      • Unsorted data
      • Multiple dimensions
    • Interpolation
  • Convex Optimization
    • Global Optimization
    • Local Optimization
    • Constrained Optimization
  • Integration
    • Numerical Integration
    • Integration by Simulation
  • Symbolic Computation
    • Basics
    • Equations
    • Integration and Differentiation
    • Differentiation
  • Conclusion
  • Further Resources
• 12. Stochastics
  • Random Numbers
  • Simulation
    • Random Variables
    • Stochastic Processes
      • Geometric Brownian motion
      • Square-root diffusion
      • Stochastic volatility
      • Jump diffusion
    • Variance Reduction
  • Valuation
    • European Options
    • American Options
  • Risk Measures
    • Value-at-Risk
    • Credit Valuation Adjustments
  • Python Script
  • Conclusion
  • Further Resources
• 13. Statistics
  • Normality Tests
    • Benchmark Case
    • Real-World Data
  • Portfolio Optimization
    • The Data
    • The Basic Theory
    • Optimal Portfolios
    • Efficient Frontier
    • Capital Market Line
  • Bayesian Statistics
    • Bayes Formula
    • Bayesian Regression
    • Two Financial Instruments
    • Updating Estimates over Time
  • Machine Learning
    • Unsupervised Learning
      • The data
      • k-means clustering
      • Gaussian mixture
    • Supervised Learning
      • The data
      • Gaussian Naive Bayes
      • Logistic regression
      • Decision trees
      • Deep neural networks
        • DNNs with scikit-learn
        • DNNs with TensorFlow
      • Feature transforms
      • Train-test splits: Support vector machines
  • Conclusion
  • Further Resources
• IV. Algorithmic Trading
• 14. The FXCM Trading Platform
  • Getting Started
  • Retrieving Data
    • Retrieving Tick Data
    • Retrieving Candles Data
  • Working with the API
    • Retrieving Historical Data
    • Retrieving Streaming Data
    • Placing Orders
    • Account Information
  • Conclusion
  • Further Resources
• 15. Trading Strategies
  • Simple Moving Averages
    • Data Import
    • Trading Strategy
    • Vectorized Backtesting
    • Optimization
  • Random Walk Hypothesis
  • Linear OLS Regression
    • The Data
    • Regression
  • Clustering
  • Frequency Approach
  • Classification
    • Two Binary Features
    • Five Binary Features
    • Five Digitized Features
    • Sequential Train-Test Split
    • Randomized Train-Test Split
  • Deep Neural Networks
    • DNNs with scikit-learn
    • DNNs with TensorFlow
  • Conclusion
  • Further Resources
• 16. Automated Trading
  • Capital Management
    • The Kelly Criterion in a Binomial Setting
    • The Kelly Criterion for Stocks and Indices
  • ML-Based Trading Strategy
    • Vectorized Backtesting
    • Optimal Leverage
    • Risk Analysis
    • Persisting the Model Object
  • Online Algorithm
  • Infrastructure and Deployment
  • Logging and Monitoring
  • Conclusion
  • Python Scripts
    • Automated Trading Strategy
    • Strategy Monitoring
  • Further Resources
• V. Derivatives Analytics
• 17. Valuation Framework
  • Fundamental Theorem of Asset Pricing
    • A Simple Example
    • The General Results
  • Risk-Neutral Discounting
    • Modeling and Handling Dates
    • Constant Short Rate
  • Market Environments
  • Conclusion
  • Further Resources
• 18. Simulation of Financial Models
  • Random Number Generation
  • Generic Simulation Class
  • Geometric Brownian Motion
    • The Simulation Class
    • A Use Case
  • Jump Diffusion
    • The Simulation Class
    • A Use Case
  • Square-Root Diffusion
    • The Simulation Class
    • A Use Case
  • Conclusion
  • Further Resources
• 19. Derivatives Valuation
  • Generic Valuation Class
  • European Exercise
    • The Valuation Class
    • A Use Case
  • American Exercise
    • Least-Squares Monte Carlo
    • The Valuation Class
    • A Use Case
  • Conclusion
  • Further Resources
• 20. Portfolio Valuation
  • Derivatives Positions
    • The Class
    • A Use Case
  • Derivatives Portfolios
    • The Class
    • A Use Case
  • Conclusion
  • Further Resources
• 21. Market-Based Valuation
  • Options Data
  • Model Calibration
    • Relevant Market Data
    • Option Modeling
    • Calibration Procedure
  • Portfolio Valuation
    • Modeling Option Positions
    • The Options Portfolio
  • Python Code
  • Conclusion
  • Further Resources
• A. Dates and Times
  • Python
  • NumPy
  • pandas
• B. BSM Option Class
  • Class Definition
  • Class Usage
• Index

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