Hands-On Python for Finance

Hands-On Python for Finance Front Cover
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378 pages

Book Description

Learn and implement quantitative finance using popular Python libraries like NumPy, pandas, and Keras

Key Features

  • Understand Python data structure and work with time series data
  • Use popular Python libraries including TensorFlow, Keras, and SciPy to deploy key concepts in quantitative finance
  • Explore various Python programs and learn finance paradigms

Book Description

Python is one of the most popular languages used for quantitative finance. With this book, you'll explore the key characteristics of Python for finance, solve problems in finance, and understand risk management.

The book starts with major concepts and techniques related to quantitative finance, and an introduction to some key Python libraries. Next, you'll implement time series using pandas and DataFrames. The following chapters will help you gain an understanding of how to measure the diversifiable and non-diversifiable security risk of a portfolio and optimize your portfolio by implementing Markowitz Portfolio Optimization. Sections on regression methodology will help you to value assets and understand the relationship between commodity prices and stocks. In addition to this, you'll be able to forecast stock prices using Monte Carlo simulation. The book will also highlight forecast that will show you how to determine the price of a call option by analyzing price variation. You'll also use deep learning for financial data analysis and forecasting. In the concluding chapters, you will create neural networks with TensorFlow and Keras for forecasting and prediction.

By the end of this book, you will be equipped with the skills you need to perform different financial analysis tasks using Python

What you will learn

  • Clean financial data with data preprocessing
  • Visualize financial data using histograms, color plots, and graphs
  • Perform time series analysis with pandas for forecasting
  • Estimate covariance and the correlation between securities and stocks
  • Optimize your portfolio to understand risks when there is a possibility of higher returns
  • Calculate expected returns of a stock to measure the performance of a portfolio manager
  • Create a prediction model using recurrent neural networks (RNN) with Keras and TensorFlow

Who this book is for

This book is ideal for aspiring data scientists, Python developers and anyone who wants to start performing quantitative finance using Python. You can also make this beginner-level guide your first choice if you're looking to pursue a career as a financial analyst or a data analyst. Working knowledge of Python is necessary.

Table of Contents

  1. Python for Finance 101
  2. Getting started with Numpy, Pandas and Matplotlib
  3. Time Series Analysis and Forecasting
  4. Measuring Investment Risks
  5. Portfolio Allocation and Markowitz Portfolio Optimization
  6. The Capital Asset Pricing Model
  7. Regression Analysis in Finance
  8. Monte Carlo simulations for decision-making
  9. Option Pricing-Black Scholes Model
  10. Introduction to Deep Learning with TensorFlow and Keras
  11. Stock Market Analysis and Forecasting Case study
  12. What Is Next

Book Details

  • Title: Hands-On Python for Finance
  • Author:
  • Length: 378 pages
  • Edition: 1
  • Language: English
  • Publisher:
  • Publication Date: 2019-03-30
  • ISBN-10: 1789346371
  • ISBN-13: 9781789346374

Book Link

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Download from NitroFlareEPUB1205/03/2019
Download from UsersCloudEPUB1205/03/2019
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