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Mastering Python for Finance: Implement advanced state-of-the-art financial statistical applications using Python , Second Edition

James Ma Weiming

NZ$14.99 ~~NZ$51.99~~

2.9 (8 Ratings)

eBook Apr 2019 426 pages 2nd Edition

eBook

NZ$14.99 ~~NZ$51.99~~

James Ma Weiming

NZ$14.99 ~~NZ$51.99~~

2.9 (8 Ratings)

eBook Apr 2019 426 pages 2nd Edition

eBook

NZ$14.99 ~~NZ$51.99~~

NZ$14.99 ~~NZ$51.99~~

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Key benefits

Explore advanced financial models used by the industry and ways of solving them using Python
Build state-of-the-art infrastructure for modeling, visualization, trading, and more
Empower your financial applications by applying machine learning and deep learning

Description

The second edition of Mastering Python for Finance will guide you through carrying out complex financial calculations practiced in the industry of finance by using next-generation methodologies. You will master the Python ecosystem by leveraging publicly available tools to successfully perform research studies and modeling, and learn to manage risks with the help of advanced examples. You will start by setting up your Jupyter notebook to implement the tasks throughout the book. You will learn to make efficient and powerful data-driven financial decisions using popular libraries such as TensorFlow, Keras, Numpy, SciPy, and scikit-learn. You will also learn how to build financial applications by mastering concepts such as stocks, options, interest rates and their derivatives, and risk analytics using computational methods. With these foundations, you will learn to apply statistical analysis to time series data, and understand how time series data is useful for implementing an event-driven backtesting system and for working with high-frequency data in building an algorithmic trading platform. Finally, you will explore machine learning and deep learning techniques that are applied in finance. By the end of this book, you will be able to apply Python to different paradigms in the financial industry and perform efficient data analysis.

Who is this book for?

If you are a financial or data analyst or a software developer in the financial industry who is interested in using advanced Python techniques for quantitative methods in finance, this is the book you need! You will also find this book useful if you want to extend the functionalities of your existing financial applications by using smart machine learning techniques. Prior experience in Python is required.

What you will learn

Solve linear and nonlinear models representing various financial problems
Perform principal component analysis on the DOW index and its components
Analyze, predict, and forecast stationary and non-stationary time series processes
Create an event-driven backtesting tool and measure your strategies
Build a high-frequency algorithmic trading platform with Python
Replicate the CBOT VIX index with SPX options for studying VIX-based strategies
Perform regression-based and classification-based machine learning tasks for prediction
Use TensorFlow and Keras in deep learning neural network architecture

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Product Details

Publication date : Apr 30, 2019

Length: 426 pages

Edition : 2nd

Language : English

ISBN-13 : 9781789345278

Category :

Data

Languages :

Python

Concepts :

Financial Technology

Tools :

TensorFlow

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15 Chapters

Section 1: Getting Started with Python

Overview of Financial Analysis with Python

Getting Python

Introduction to Quandl

Plotting a time series chart

Performing financial analytics on time series data

Summary

Section 2: Financial Concepts

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

Nonlinearity in Finance

Nonlinearity modeling

Root-finding algorithms

SciPy implementations in root-finding

Summary

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

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

Pricing a callable bond option

Summary

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

Section 3: A Hands-On Approach

Interactive Financial Analytics with the VIX

Volatility derivatives

Financial analytics of the S&P 500 and the VIX

Calculating the VIX Index

Summary

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

Implementing a Backtesting System

Introducing backtesting

Designing and implementing a backtesting system

Ten considerations for a backtesting model

Discussion of algorithms in backtesting

Summary

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

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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About the author

James Ma Weiming

James Ma Weiming is a software engineer based in Singapore. His studies and research are focused on financial technology, machine learning, data sciences, and computational finance. James started his career in financial services working with treasury fixed income and foreign exchange products, and fund distribution. His interests in derivatives led him to Chicago, where he worked with veteran traders of the Chicago Board of Trade to devise high-frequency, low-latency strategies to game the market. He holds an MS degree in finance from Illinois Tech's Stuart School of Business in the United States and a bachelor's degree in computer engineering from Nanyang Technological University.

See other products by James Ma Weiming

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Mastering Python for Finance: Implement advanced state-of-the-art financial statistical applications using Python , Second Edition

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