**Autor: James Ma Weiming**

Broj strana: 426

ISBN broj: 9781789346466

Izdavač:
PACKT PUBLISHING

Godina izdanja: 2019.

Pregleda (30 dana / ukupno): **30 / 234**

Predlog za prevod

- 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

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 sklearn. 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.

- 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

**Table of contents**

**1 Overview of Financial Analysis with Python **

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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