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Learn and implement quantitative finance using popular Python libraries like NumPy, pandas, and KerasKey FeaturesUnderstand Python data structure fundamentals and work with time series dataUse popular Python libraries including TensorFlow, Keras, and SciPy to deploy key concepts in quantitative financeExplore various Python programs and learn finance paradigmsBook DescriptionPython 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 analysis 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 analysis methodology will help you to value assets and understand the relationship between commodity prices and business stocks. In addition to this, you'll be able to forecast stock prices using Monte Carlo simulation. The book will also highlight forecast models 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 PythonWhat you will learnClean financial data with data preprocessingVisualize financial data using histograms, color plots, and graphsPerform time series analysis with pandas for forecastingEstimate covariance and the correlation between securities and stocksOptimize your portfolio to understand risks when there is a possibility of higher returnsCalculate expected returns of a stock to measure the performance of a portfolio managerCreate a prediction model using recurrent neural networks (RNN) with Keras and TensorFlowWho this book is forThis 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 programming language is necessary.

 

Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Introduction to Python for Finance -- Chapter 1: Python for Finance 101 -- Concepts related to the financial market -- Public finance -- Corporate finance -- Personal finance -- Understanding the stock market -- The primary market -- The secondary market -- Comparing the primary market and the secondary market -- Bonds -- Types of funds -- ETFs -- Mutual funds -- Hedge funds -- An introduction to derivatives -- Forward contracts -- Future contracts -- Option contracts -- Swap contracts -- Stock splits and dividends -- What are stock splits? -- How do stock splits work? -- Why would a company split a stock? -- What are reverse stock splits? -- The advantages of stock splits on dividends -- A summary of stock splits -- Order books and short selling -- Short selling -- Why Python for finance? -- What is Python? -- Python for finance -- The Anaconda Python distribution -- Why Anaconda? -- Summary -- Chapter 2: Getting Started with NumPy, pandas, and Matplotlib -- Technical requirements -- A brief introduction to NumPy -- NumPy arrays -- NumPy's arange function -- NumPy's zeros and ones functions -- NumPy's linspace function -- NumPy's eye function -- NumPy's rand function -- NumPy indexing -- NumPy operations -- A brief introduction to pandas -- Series -- DataFrames -- Selection and indexing in DataFrames -- Different operations in pandas DataFrames -- Data input and output operations with pandas -- Reading CSV files -- Reading Excel files -- Reading from HTML files -- Matplotlib -- The plot function -- The xlabel function -- The ylabel function -- Creating multiple subplots using matplotlib -- The pie function -- The histogram function -- Summary -- Further reading -- Section 2: Advanced Analysis in Python for Finance.