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.packages("pedquant")
- Install the developing version of
pedquant
from github with:
devtools::install_github("shichenxie/pedquant")
The following examples show you how to import data and create charts.
library(pedquant)
#> Registered S3 method overwritten by 'xts':
#> method from
#> as.zoo.xts zoo
## 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
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.