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项目作者： business-science

项目描述 ：
Bringing financial analysis to the tidyverse
高级语言： R
项目地址: git://github.com/business-science/tidyquant.git
创建时间： 2016-12-30T19:12:30Z
项目社区：https://github.com/business-science/tidyquant
开源协议：Other
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Bringing financial and business analysis to the tidyverse

2-Minutes To tidyquant

Our short introduction to tidyquant on
YouTube.

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Features of tidyquant

tidyquant integrates the best resources for collecting and analyzing
financial data, zoo, xts, quantmod, TTR, and
PerformanceAnalytics, with the tidy data infrastructure of the
tidyverse allowing for seamless interaction between each. You can now
perform complete financial analyses in the tidyverse.

  • A few core functions with a lot of power
  • Integrates the quantitative analysis functionality of zoo, xts,
    quantmod, TTR, and now PerformanceAnalytics
  • Designed for modeling and scaling analyses using the tidyverse
    tools in R for Data Science
  • Implements ggplot2 functionality for beautiful and meaningful
    financial visualizations
  • User-friendly documentation to get you up to speed quickly!

New Excel Functionality in tidyquant

One-Stop Shop for Serious Financial Analysis

With tidyquant all the benefits add up to one thing: a one-stop shop
for serious financial analysis!

Core Functions

  • Getting Financial Data from the web: tq_get(). This is a
    one-stop shop for getting web-based financial data in a “tidy” data
    frame format. Get data for daily stock prices (historical), key
    statistics (real-time), key ratios (historical), financial statements,
    dividends, splits, economic data from the FRED, FOREX rates from
    Oanda.

  • Manipulating Financial Data: tq_transmute() and tq_mutate().
    Integration for many financial functions from xts, zoo,
    quantmod,TTR and PerformanceAnalytics packages. tq_mutate() is
    used to add a column to the data frame, and tq_transmute() is used
    to return a new data frame which is necessary for periodicity changes.

  • Performance Analysis and Portfolio Analysis: tq_performance() and
    tq_portfolio()    . The newest additions to the tidyquant family
    integrate PerformanceAnalytics functions. tq_performance()
    converts investment returns into performance metrics. tq_portfolio()
    aggregates a group (or multiple groups) of asset returns into one or
    more portfolios.

Comparing Stock Prices

Visualizing the stock price volatility of four stocks side-by-side is
quick and easy…

Evaluating Stock Performance

What about stock performance? Quickly visualize how a \$10,000
investment in various stocks would perform.

Evaluating Portfolio Performance

Ok, stocks are too easy. What about portfolios? With the
PerformanceAnalytics integration, visualizing blended portfolios are
easy too!

  • Portfolio 1: 50% FB, 25% AMZN, 25% NFLX, 0% GOOG
  • Portfolio 2: 0% FB, 50% AMZN, 25% NFLX, 25% GOOG
  • Portfolio 3: 25% FB, 0% AMZN, 50% NFLX, 25% GOOG
  • Portfolio 4: 25% FB, 25% AMZN, 0% NFLX, 50% GOOG

This just scratches the surface of tidyquant. Here’s how to install to
get started.

Installation

Development Version with Latest Features:

  1. # install.packages("devtools")
  2. devtools::install_github("business-science/tidyquant")

CRAN Approved Version:

  1. install.packages("tidyquant")

Further Information

The tidyquant package includes several vignettes to help users get up
to speed quickly:

Want to Learn tidyquant?

  • Learning Lab
    #9:

    • Performance Analysis & Portfolio Optimization with tidyquant -
      A 1-hour course on tidyquant in Learning Labs PRO

  • Learning Lab
    #10:

    • Building an API with plumber - Build a stock optimization API
      with plumber and tidyquant

  • Learning Lab
    #16:

    • Stock Portfolio Optimization and Nonlinear Programming - Use the
      ROI package with tidyquant to calculate optimal minimum variance
      portfolios and develop an efficient frontier.

  • Learning Lab
    #30:

    • Shiny Financial Analysis with Tidyquant API & Excel Pivot
      Tables       - Learn how to use the new Excel Functionality to make
      Pivot Tables, VLOOKUPs, Sum-If’s, and more!