README.md

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

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()

##   code       date close change perc_change  open   low  high
## 1  JFC 2020-04-30 143.1   -3.9       -2.65 148.0 143.0 149.0
## 2  JFC 2020-04-29 147.0    5.8        4.11 141.2 139.1 147.0
## 3  JFC 2020-04-28 141.2    6.7        4.98 136.0 135.6 141.4
## 4  JFC 2020-04-27 134.5    0.2        0.15 135.0 134.3 136.5
## 5  JFC 2020-04-24 134.3   -1.7       -1.25 137.8 133.7 137.8
## 6  JFC 2020-04-23 136.0    2.0        1.49 137.0 135.5 140.0

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
  )

nfrimando/pseR documentation built on Jan. 10, 2022, 4:27 p.m.