Skip to content
quant, financial data, economic data
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
R 0.1.2 May 25, 2019
docs Create CNAME Mar 26, 2019
man 0.1.2 May 25, 2019
.DS_Store addti Feb 1, 2019
.Rbuildignore
.gitignore init Jan 21, 2019
.travis.yml 0.1.0 Mar 9, 2019
CRAN-RELEASE fixed a bug in ed_nbs May 13, 2019
DESCRIPTION 0.1.2 May 25, 2019
LICENSE.md '0.1.0' Mar 10, 2019
NAMESPACE 0.1.2 May 25, 2019
NEWS.md 0.1.2 May 25, 2019
README.Rmd '0.1.0' Mar 9, 2019
README.md '0.1.0' Mar 9, 2019
_pkgdown.yml 0.1.0 Mar 9, 2019
cran-comments.md init Jan 21, 2019
pedquant.Rproj pq_plot Mar 5, 2019

README.md

pedquant

CRAN status Travis build status

pedquant (Public Economic Data and QUANTitative analysis) provides an interface to access public economic and financial data for economic research and quantitative analysis. The functions are grouped into three main categories,

  • ed_* (economic data) functions load economic data from NBS and FRED;
  • md_* (market data) functions load stock prices from Yahoo finance, stock prices and financial statements of SSE and SZSE shares from 163 Finance, and future prices from Sina Finance etc.
  • pq_* (quantitative analysis) functions create technical indicators, visualization charts and industrial index etc for time series data.

The functions in this package are designed to write minimum codes for some common tasks in quantitative analysis process. Since the parameters to get data can be interactively specify, it’s very easy to start. The loaded data have been carefully cleansed and provided in a unified format. More public data sources are still under cleansing and developing.

pedquant package has advantages on multiple aspects, such as the format of loaded data is a list of data frames, which can be easily manipulated in data.table or tidyverse packages; high performance on speed by use data.table and TTR; and modern graphics by using ggplot2. At this moment, pedquant can only handle EOD (end of date) data. Similar works including tidyquant or quantmod, which are much mature for financial analysis.

Installation

  • Install the release version of pedquant from CRAN with:
install.packages("pedquant")
  • Install the developing version of pedquant from github with:
devtools::install_github("shichenxie/pedquant")

Example

The following examples show you how to import data and create charts.

library(pedquant)
## import eocnomic data
dat1 = ed_fred('GDPCA')
#> 1/1 GDPCA
dat2 = ed_nbs(geo_type='national', freq='quarterly', symbol='A010101')

## import market data
FAAG = md_stock(c('FB', 'AMZN', 'AAPL', 'GOOG'), date_range = 'max') # from yahoo
#> 1/4 FB
#> 2/4 AMZN
#> 3/4 AAPL
#> 4/4 GOOG
INDX = md_stock(c('^000001','^399001'), date_range = 'max', source = '163')
#> 1/2 ^000001
#> 2/2 ^399001
# candlestick chart with technical indicators
pq_plot(INDX$`^000001`, chart_type = 'candle', date_range = '1y', addti = list(
    sma = list(n=50), macd=list()
))

#> $`000001.SS`
#> TableGrob (2 x 1) "arrange": 2 grobs
#>    z     cells    name           grob
#> p0 1 (1-1,1-1) arrange gtable[layout]
#> p1 2 (2-2,1-1) arrange gtable[layout]
# comparing prices
pq_plot(FAAG, multi_series = list(nrow=2, scales = 'free_y'), date_range = '3y')
#> $multi_series

Issues and Contributions

This package still on the developing stage. If you have any issue when using this package, please update to the latest version from github. If the issue still exists, report it at github page. Contributions in any forms to this project are welcome.

If you like this package, you can buy me a coffee.

alipay wechat

You can’t perform that action at this time.