Mastering Pandas for Finance : Master Pandas, an Open Source Python Data Analysis Library, for Financial Data Analysis.

Cover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Getting Started with pandas Using Wakari.io -- What is Wakari? -- Creating a Wakari cloud account -- Updating existing packages -- Installing new packages -- Installing the samples in Wakari -- Summary -- Chapter 2: Introducing the Series and DataFrame -- Notebook setup -- The main pandas data structures - Series and DataFrame -- The Series -- The DataFrame -- The basics of the Series and DataFrame objects -- Creating a Series and accessing elements -- Size, shape, uniqueness, and counts of values -- Alignment via index labels -- Creating a DataFrame -- Example data -- Selecting columns of a DataFrame -- Selecting rows of a DataFrame using the index -- Slicing using the [] operator -- Selecting rows by the index label and location - .loc[] and .iloc[] -- Selecting rows by the index label and/or location - .ix[] -- Scalar lookup by label or location using .at[] and .iat[] -- Selecting rows using the Boolean selection -- Arithmetic on a DataFrame -- Reindexing the Series and DataFrame objects -- Summary -- Chapter 3: Reshaping, Reorganizing, and Aggregating -- Notebook setup -- Loading historical stock data -- Organizing the data for the examples -- Reorganizing and reshaping data -- Concatenating multiple DataFrame objects -- Merging DataFrame objects -- Pivoting -- Stacking and unstacking -- Melting -- Grouping and aggregating -- Splitting -- Aggregating -- Summary -- Chapter 4: Time-series -- Notebook setup -- Time-series data and the DatetimeIndex -- Creating time-series with specific frequencies -- Representing intervals of time using periods -- Shifting and lagging time-series data -- Frequency conversion of time-series data -- Resampling of time-series -- Summary -- Chapter 5: Time-series Stock Data.

Notebook setup -- Obtaining historical stock and index data -- Fetching historical stock data from Yahoo! -- Fetching index data from Yahoo! -- Visualizing financial time-series data -- Plotting closing prices -- Plotting volume-series data -- Combined price and volumes -- Plotting candlesticks -- Fundamental financial calculations -- Calculating simple daily percentage change -- Calculating simple daily cumulative returns -- Analyzing the distribution of returns -- Histograms -- Q-Q plots -- Box-and-whisker plots -- Comparison of daily percentage change between stocks -- Moving windows -- Volatility calculation -- Rolling correlation of returns -- Least-squares regression of returns -- Comparing stocks to the S&amp -- P 500 -- Summary -- Chapter 6: Trading Using Google Trends -- Notebook setup -- A brief on Quantifying Trading Behavior in Financial Markets Using Google Trends -- Data collection -- The data from the paper -- Gathering our own DJIA data from Quandl -- Google Trends data -- Generating order signals -- Computing returns -- Cumulative returns and the result of the strategy -- Summary -- Chapter 7: Algorithmic Trading -- Notebook setup -- The process of algorithmic trading -- Momentum strategies -- Mean-reversion strategies -- Moving averages -- Simple moving average -- Exponentially weighted moving average -- Technical analysis techniques -- Crossovers -- Pairs trading -- Algo trading with Zipline -- Algorithm - buy apple -- Algorithm - dual moving average crossover -- Algorithm - pairs trade -- Summary -- Chapter 8: Working with Options -- Introducing options -- Notebook setup -- Options data from Yahoo! Finance -- Implied volatility -- Volatility smirks -- Calculating payoff on options -- The call option payoff calculation -- The put option payoff calculation -- Profit and loss calculation -- The call option profit and loss for a buyer.

The call option profit and loss for the seller -- Combined payoff charts -- The put option profit and loss for a buyer -- The put option profit and loss for the seller -- The pricing of options -- The pricing of options with Black-Scholes -- Deriving the model -- The formulas -- Black-Scholes using Mibian -- Charting option price change over time -- The Greeks -- Calculation and visualization -- Summary -- Chapter 9: Portfolios and Risk -- Notebook setup -- An overview of modern portfolio theory -- Concept -- Mathematical modeling of a portfolio -- Risk and expected return -- Diversification -- The efficient frontier -- Modeling a portfolio with pandas -- Constructing an efficient portfolio -- Gathering historical returns for a portfolio -- Formulation of portfolio risks -- The Sharpe ratio -- Optimization and minimization -- Constructing an optimal portfolio -- Visualizing the efficient frontier -- Value at Risk -- Summary -- Index.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.