Stock Prices
Not much explanation here (see the source code for comments) just a proof-of-concept that one can use R for data import and manipulation, then turn to Observable for interactive graphs.
Not much explanation here (see the source code for comments) just a proof-of-concept that one can use R for data import and manipulation, then turn to Observable for interactive graphs.
/*****************************
* We should transpose from an
* array of variable-values to
* an array of trading days.
* Also, the dates conme into
* Observables as strings, so we
* must convert them back to dates:
******************************/
allStocks = {
const df2 = transpose(df);
const parseTime = d3.utcParse("%Y-%m-%d")
df2.forEach(function(d) {
d.date = parseTime(d.date)
});
return df2;
}/****************************
* Create an array of names of the stocks:
*****************************/
stockNames = {
let names = [];
allStocks.forEach(function(d) {
if (!names.includes(d.name)) {
names.push(d.name)
}
});
return names;
}---
title: "Stock Prices"
format:
html:
echo: false
code-tools: true
---
Not much explanation here (see the source code for comments) just a proof-of-concept that one can use R for data import and manipulation, then turn to Observable for interactive graphs.
```{r warning=FALSE, message=FALSE}
library(tidyverse)
library(quantmod)
```
```{r}
## year to date:
sdate <- as.Date("2023-01-01")
edate <- as.Date("2023-11-01")
## found a few from
## https://stockanalysis.com/stocks/
stocks <- list(
c(code ="AAPL", name = "Apple Inc."),
c(code = "ABNB", name = "Airbnb, Inc."),
c(code = "CAT", name = "Caterpillar Inc.")
)
## helper-function to
## get get info for one stock:
get_stock <- function(stock) {
ss_stock <-
getSymbols(
stock["code"],
from = sdate,
to = edate,
auto.assign = FALSE
)
## get the price time series:
df <- fortify(ss_stock[, 6])
names(df) <- c("date", "close")
df$name <- rep(stock["name"], times = nrow(df))
df
}
## get all stock info into one data frame:
all_stocks <- map_dfr(stocks, get_stock)
## pass the data frame to Observable:
ojs_define(df = all_stocks)
```
```{ojs}
// Now we begin work with Observable:
Plot = import("https://esm.run/@observablehq/plot@0.6.11")
```
```{ojs}
/*****************************
* We should transpose from an
* array of variable-values to
* an array of trading days.
* Also, the dates conme into
* Observables as strings, so we
* must convert them back to dates:
******************************/
allStocks = {
const df2 = transpose(df);
const parseTime = d3.utcParse("%Y-%m-%d")
df2.forEach(function(d) {
d.date = parseTime(d.date)
});
return df2;
}
```
```{ojs}
/****************************
* Create an array of names of the stocks:
*****************************/
stockNames = {
let names = [];
allStocks.forEach(function(d) {
if (!names.includes(d.name)) {
names.push(d.name)
}
});
return names;
}
```
```{ojs}
//| panel: input
viewof stockName = Inputs.select(stockNames, {label: "Select a stock: "})
viewof n = Inputs.range([1, 30], {label:"Window size: ", step: 1, value: 10})
viewof r = Inputs.range([1, 5], {label: "Radius: ", step: 0.1, value: 2})
```
```{ojs}
Plot.plot({
caption: `Closing prices for ${stockName}`,
marks: [
Plot.frame(),
Plot.bollingerY(
allStocks.filter(d => d.name == stockName),
{x: "date", y: "close", n: n, k: r}
)
]
})
```