Applied quantitative finance : using Python for financial analysis

This book provides both conceptual knowledge of quantitative finance and a hands-on approach to using Python. It begins with a description of concepts prior to the application of Python with the purpose of understanding how to compute and interpret results. This book offers practical applications in the field of finance concerning Python, a language that is more and more relevant in the financial arena due to big data. This will lead to a better understanding of finance as it gives a descriptive process for students, academics and practitioners. Introduction Contents About the Author List of Figures List of Tables List of Equations Why Python? Abstract Installing Python in the Computer Using Jupyter Notebooks with Python Understanding Jupyter Notebooks Using Google Colab References Learning to Use Python: The Basic Aspects Abstract Understanding Numbers in Python Understanding Numbers in Python Using Data Structures in Python What Is a List? How to Create a List? Measuring a List Indexing and Cutting a List Appending Lists Arranging Lists From List to Matrices From List to Dictionaries Modifying a Dictionary Other Interesting Functions of a Dictionary The DataFrame Boolean, Loops and Other Features If, Else and Elif in Python Loops For Loop While Loop List Comprehension References Using FRED® API for Economic Indicators and Data (Example) Abstract Installing the FRED® API Using the FRED® API to Retrieve Data First Step Second Step Third Step The Gross Domestic Product The Gross Domestic Product Price Deflator Understanding the Process into the Basics Comparing GDP Using Stock Market Data in Python Abstract API Sources Most Important Libraries for Using Data in Python in the Present Book Other Important Libraries Not Used in This Book Suggestion of Libraries for Other Applications Using Python with Yahoo Finance API Using Python with Quandl API Using f.fn( ) for Retreiving Information Using Python with Excel Conclusion Regarding Using Data in Python Statistical Methods Using Python for Analyzing Stocks Abstract The Central Limit Theorem Creating a Histogram Creating a Histogram with Line Plots Histograms Using f.fn() Histogram (Percent Change) with Two Variables Histogram (Logarithmic Return) with Two Variables Interquartile Range and Boxplots Boxplot with Two Variables Kernel Density Plot and Volatility Kernel Density Plot (Percent Change) with Two Variables Covariance and Correlation Scatterplots and Heatmaps Works Cited Elements for Technical Analysis Using Python Abstract The Linear Plot with One Stock Price (Max & Min Values and the Range) When to Use Linear Plots in Finance The Linear Plot with Two or More Stock Price Linear Plot with Volume Volume of Trade Comparison of Securities with Volume Plots and Closing Prices Candlestick Charts Candlestick Charts and Volume Customizing Candlestick Charts and Volume with **Kwargs OHLC Charts with Volume Line Charts with Volume Moving Average with Matplotlib Moving Average with Mplfinance The Exponential Moving Average (EMA) The Moving Average Convergence Divergence (MACD) with Baseline The Moving Average Convergence Divergence (MACD) with Signal Line Bollinger Bands ® Backtesting Strategies for Trading Parabolic SAR Fast and Slow Stochastic Oscillators References Valuation and Risk Models with Stocks Abstract Creating a Portfolio Calculating Statistical Measures on a Portfolio The Capital Asset Pricing Model The Beta The Beta and the CAPM Sharpe Ratio Traynor Ratio Jensen’s Measure Information Ratio References Value at Risk Abstract Historical VaR(95) Historical VaR(99) VaR for the Next 10 Days Historical Drawdown Wrapping Up the Book—Understanding Performance Portfolio Performance using f.fn() Fund Performance using f.fn() Works Cited Works Cited Index This book provides conceptual knowledge on quantitative finance and a hands-on experience using Python. It begins with a description of concepts prior to the application of Python with the purpose of understanding how to compute and also the interpretation of the results. The book will satisfy the lack of information concerning Python, a language that is more and more relevant in the financial arena due to big data. This will lead to a better understanding of advance finance as it gives a descriptive process for students, academics and practitioners.