Public Economic Data and Quantitative Analysis

Provides an interface to access public economic and financial data for economic research and quantitative analysis. The data sources including NBS, FRED, Yahoo Finance, 163 Finance and etc.

CRANstatus Travis buildstatus

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.


  • Install the release version of pedquant from CRAN with:
  • Install the developing version of pedquant from github with:


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

## 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.

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  • Added functions of pq_portfolio and pq_backtest
  • Fixed multiple bugs in pq_plot.

Fixed a bug for functions to query data from NBS, which cant load JS data.

pedquant 0.1.0

  • Added a file to track changes to the package.

Reference manual

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0.1.7 by Shichen Xie, a month ago

Report a bug at

Browse source code at

Authors: Shichen Xie [aut, cre]

Documentation:   PDF Manual  

GPL-3 license

Imports data.table, TTR, zoo, curl, xml2, httr, rvest, jsonlite, stringi, readxl, readr, ggplot2, scales, gridExtra, plotly

Suggests knitr, rmarkdown

See at CRAN