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You're reading from Mastering Python for Finance Implement advanced state-of-the-art financial statistical applications using Python

Product type Paperback

Published in Apr 2019

Publisher Packt

ISBN-13 9781789346466

Length 426 pages

Edition 2nd Edition

Languages

Python

Tools

TensorFlow

Concepts

Financial Technology

Author (1):

James Ma Weiming

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Table of Contents (18) Chapters

Title Page

Dedication

Contributors

About Packt

Preface

1. Overview of Financial Analysis with Python FREE CHAPTER

Getting Python
Introduction to Quandl
Plotting a time series chart
Performing financial analytics on time series data
Summary

2. The Importance of Linearity in Finance

The Capital Asset Pricing Model and the security market line
The Arbitrage Pricing Theory model
Multivariate linear regression of factor models
Linear optimization
Solving linear equations using matrices
The LU decomposition
The Cholesky decomposition
The QR decomposition
Solving with other matrix algebra methods
Summary

3. Nonlinearity in Finance

Nonlinearity modeling
Root-finding algorithms
SciPy implementations in root-finding
Summary

4. Numerical Methods for Pricing Options

Introduction to options
Binomial trees in option pricing
Pricing European options
Writing the StockOption base class
The Greeks for free
Trinomial trees in option pricing
Lattices in option pricing
Finite differences in option pricing
Putting it all together – implied volatility modeling
Summary

5. Modeling Interest Rates and Derivatives

Fixed-income securities
Yield curves
Valuing a zero-coupon bond
Bootstrapping a yield curve
Forward rates
Calculating the yield to maturity
Calculating the price of a bond
Bond duration
Bond convexity
Short–rate modeling
Bond options
Pricing a callable bond option
Summary

6. Statistical Analysis of Time Series Data

The Dow Jones industrial average and its 30 components
Applying a kernel PCA
Stationary and non-stationary time series
The Augmented Dickey-Fuller Test
Analyzing a time series with trends
Making a time series stationary
Forecasting and predicting a time series
Summary

7. Interactive Financial Analytics with the VIX

Volatility derivatives
Financial analytics of the S&P 500 and the VIX
Calculating the VIX Index
Summary

8. Building an Algorithmic Trading Platform

Introducing algorithmic trading
Building an algorithmic trading platform
Building a mean-reverting algorithmic trading system
Building a trend-following trading platform
VaR for risk management
Summary

9. Implementing a Backtesting System

Introducing backtesting
Designing and implementing a backtesting system
Ten considerations for a backtesting model
Discussion of algorithms in backtesting
Summary

10. Machine Learning for Finance

Introduction to machine learning
Predicting prices with a single-asset regression model
Predicting returns with a cross-asset momentum model
Predicting trends with classification-based machine learning
Conclusion on the use of machine learning algorithms
Summary

11. Deep Learning for Finance

A brief introduction to deep learning
A deep learning price prediction model with TensorFlow
Credit card payment default prediction with Keras
Summary

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You're reading from Mastering Python for Finance Implement advanced state-of-the-art financial statistical applications using Python

Table of Contents (18) Chapters

Authors (1)

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