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
- 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 and interactive graphics by using plotly. Similar works
including tidyquant or
- Install the release version of
pedquantfrom CRAN with:
- Install the developing version of
pedquantfrom github with:
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='nation', 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.