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Python for Finance

Mastering Data-Driven Finance

Paperback Engels 2019 9781492024330
Verkooppositie 2726
Verwachte levertijd ongeveer 8 werkdagen

Samenvatting

The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics.

Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks

Specificaties

ISBN13:9781492024330
Taal:Engels
Bindwijze:paperback
Aantal pagina's:685
Uitgever:O'Reilly
Druk:2
Verschijningsdatum:15-1-2019

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Inhoudsopgave

Preface
Conventions Used in This Book
Using Code Examples
O’Reilly 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
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
Fibonacci Numbers
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
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
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
Supervised Learning
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

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