In nfrimando/pseR: Pull and Analyse PSE Data using R

knitr::opts_chunk$set(echo = TRUE)

pseR

Pull and analyse PSE data. Currently limited only to Investagrams as source which is limited to past year data only.

Installation

Install devtools to easily install pseR.

install.packages("devtools")
devtools::install_github("nfrimando/pseR")

Examples

Extract historical PSE data with pse_get()

suppressPackageStartupMessages({library(pseR); library(dplyr)}) 
stock.dt <- pse_get(c("JFC", "MBT", "FGEN","BPI", "URC",
                            "ALI", "MER", "ABS", "GLO"))
stock.dt %>% head()

Analytics using `tidyquant`

The package tidyquant has convenient functions that allows easy implementation for visualisation and analysis. (It also has functions to pull data for PSE from various sources. The easy-to-access sources though are usually not updated. E.g. Yahoo! Finance).

suppressPackageStartupMessages({library(tidyquant); library(ggplot2)})
stock.dt %>%
  ggplot(aes(x = as.Date(date), y = close)) +
  geom_barchart(aes(open = open, high = high, low = low, close = close)) +
  labs(title = "PSE Stocks Daily Past 1 Year", y = "Closing Price", x = "") + 
  facet_wrap(~code, scales = "free") + 
  scale_x_date(date_breaks = "3 months", date_labels = "%b %d") + 
  theme_tq()

Correlation Visualisation with `ggcorrplot`

suppressPackageStartupMessages({library(tidyr); library(ggcorrplot)})
stock.dt %>%
  group_by(code) %>% 
  arrange(desc(date)) %>% 
  # filter(row_number() <= 150) %>% 
  ungroup() %>% 
  select(code, date, perc_change) %>% 
  spread(key = code, value = perc_change) %>% 
  select(-date) %>% 
  cor() %>% 
  ggcorrplot(
    hc.order = TRUE, 
    type = "lower",
    outline.col = "white",
    ggtheme = ggplot2::theme_gray,
    colors = c("#6D9EC1", "white", "#E46726"),
    insig = "blank",
    lab = TRUE,
    lab_size = 6
  )