Python for Finance

Book Description

Build real-life Python applications for quantitative finance and financial engineering with this book and ebook

Overview

• Estimate market risk, form various portfolios, and estimate their variance-covariance matrixes using real-world data
• Explains many financial concepts and trading strategies with the help of graphs
• A step-by-step tutorial with many Python programs that will help you learn how to apply Python to finance

In Detail

Python is a free and powerful tool that can be used to build a financial calculator and price options, and can also explain many trading strategies and test various hypotheses. This book details the steps needed to retrieve time series data from different public data sources.

Python for Finance explores the basics of programming in Python. It is a step-by-step tutorial that will teach you, with the help of concise, practical programs, how to run various statistic tests. This book introduces you to the basic concepts and operations related to Python. You will also learn how to estimate illiquidity, Amihud (2002), liquidity measure, Pastor and Stambaugh (2003), Roll spread (1984), spread based on high-frequency data, beta (rolling beta), draw volatility smile and skewness, and construct a binomial tree to price American options.

This book is a hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python.

What you will learn from this book

• Build a financial calculator based on Python
• Learn how to price various types of options such as European, American, average, lookback, and barrier options
• Write Python programs to download data from Yahoo! Finance
• Estimate returns and convert daily returns into monthly or annual returns
• Form an n-stock portfolio and estimate its variance-covariance matrix
• Estimate VaR (Value at Risk) for a stock or portfolio
• Run CAPM (Capital Asset Pricing Model) and the Fama-French 3-factor model
• Learn how to optimize a portfolio and draw an efficient frontier
• Conduct various statistic tests such as T-tests, F-tests, and normality tests

Approach

A hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python.

Table of Contents

Chapter 1: Introduction and Installation of Python
Chapter 2: Using Python as an Ordinary Calculator
Chapter 3: Using Python as a Financial Calculator
Chapter 4: 13 Lines of Python to Price a Call Option
Chapter 5: Introduction to Modules
Chapter 6: Introduction to NumPy and SciPy
Chapter 7: Visual Finance via Matplotlib
Chapter 8: Statistical Analysis of Time Series
Chapter 9: The Black-Scholes-Merton Option Model
Chapter 10: Python Loops and Implied Volatility
Chapter 11: Monte Carlo Simulation and Options
Chapter 12: Volatility Measures and GARCH

中文：

书名：金融界的巨蟒

使用这本书和电子书为量化金融和金融工程构建现实生活中的Python应用程序

概述

• 估计市场风险，形成各种投资组合，并使用真实世界的数据估计它们的方差-协方差矩阵
• 借助图表解释许多金融概念和交易策略
• 包含许多Python程序的分步教程，将帮助您学习如何将Python应用于金融

详细地说

Python是一个免费而强大的工具，可以用来构建金融计算器和价格期权，还可以解释许多交易策略，测试各种假设。本书详细介绍了从不同公共数据源检索时间序列数据所需的步骤。

适用于金融的Python探索了用Python编程的基础知识。这是一个循序渐进的教程，将教你，在简洁，实用程序的帮助下，如何运行各种统计测试。本书向您介绍了与Python相关的基本概念和操作。你还将学习如何估计非流动性，Amihud(2002)，流动性度量，Pastor and Stambaugh(2003)，Roll Send(1984)，基于高频数据的价差，Beta(滚动贝塔)，绘制波动率微笑和偏度，并构建一棵二叉树来为美式期权定价。

这本书是一本实践指南，带有易于操作的示例，帮助您学习期权理论、量化金融、金融建模和使用Python的时间序列。

你将从这本书中学到什么

• Build a financial calculator based on Python
• 了解如何为各种类型的期权定价，如欧洲期权、美国期权、平均期权、回望期权和障碍期权
• 编写从Yahoo！下载数据的Python程序金融
• 估算回报并将每日回报转换为月报或年报
• Form an n-stock portfolio and estimate its variance-covariance matrix
• 估计股票或投资组合的VaR(风险价值)
• 运行CAPM(资本资产定价模型)和Fama-France三因素模型
• 了解如何优化投资组合并绘制有效边界
• 进行各种统计检验，如T检验、F检验和正态检验

方法

这是一个动手指南，带有易于操作的示例，帮助您学习期权理论、量化金融、金融建模和使用Python的时间序列。

目录表

第1章：Python的介绍和安装
Chapter 2: Using Python as an Ordinary Calculator
第3章：使用Python作为财务计算器
第4章：为看涨期权定价的13行Python代码
第5章：模块简介
Chapter 6: Introduction to NumPy and SciPy
第7章：通过Matplotlib实现可视化金融
第八章：时间序列的统计分析
第9章：布莱克-斯科尔斯-默顿期权模型
第10章：巨蟒循环和隐含波动率
第11章：蒙特卡洛模拟和选择
Chapter 12: Volatility Measures and GARCH