AAPL.csv
Type Table, then Shift-Enter. Ctrl-space for more options.
data
X
Date
Y
Close
Color
#b33a52
Size
—
Facet X
—
Facet Y
—
Mark
Auto
Type Chart, then Shift-Enter. Ctrl-space for more options.
data
X
Date
Y
Close
Color
#b33a52
Size
—
Facet X
—
Facet Y
—
Mark
area
Type Chart, then Shift-Enter. Ctrl-space for more options.
Plot.auto(data, {
x: "Date",
y: "Close",
color: "#b33a52",
mark: "area"
}).plot()
Plot.auto(data, {
x: "Date",
y: "Close",
color: "#b33a52",
mark: "area"
}).plot()
Plot.plot({
marks: [
Plot.ruleY([0]),
Plot.lineY(data, {x: "Date", y: "Close"})
]
})
// ... your code here
Plot.plot({
y: {
grid: true
},
x: {
grid: true
},
marks: [
Plot.ruleY([0]),
Plot.lineY(data, {x: "Date", y: "Close", stroke: "red"})
]
})
Plot.plot({
marginLeft: 100,
y: {
grid: true
},
x: {
grid: true
},
marks: [
Plot.ruleY([0]),
Plot.lineY(data, {
x: "Date",
y: "Close",
stroke: "red"}),
Plot.ruleY([0]),
Plot.lineY(data, {
x: "Date",
y: d => d["Volume"]/1_000_000_000,
stroke: "blue"})
],
})
// Uncomment to activate
// `Close` is standardized for you.
// You should do the `Volume` yourself
data_standardized = aq.from(data)
.derive({CloseStandardized: d => ( d.Close - op.mean(d.Close) ) / op.stdev(d.Close) || 0})
.derive({VolumeStandardized: d => ( d.Volume - op.mean(d.Volume) ) / op.stdev(d.Volume) || 0})
.derive({CloseStandardized: d => ( d.Close - op.mean(d.Close) ) / op.stdev(d.Close) || 0})
.objects()
Inputs.table(data_standardized)
Plot.plot({
marginLeft: 100,
y: {
grid: true,
label: "⬆︎Z-score"
},
x: {
grid: true
},
marks: [
Plot.ruleY([0]),
Plot.lineY(data_standardized, {
x: "Date",
y: "CloseStandardized",
stroke: "red"}),
Plot.ruleY([0]),
Plot.lineY(data_standardized, {
x: "Date",
y: "VolumeStandardized",
stroke: "blue"})
],
})
Plot.plot({
marginLeft: 100,
y: {
grid: true,
label: "⬆︎Z-score"
},
x: {
grid: true
},
marks: [
Plot.ruleY([0]),
Plot.lineY(data_standardized, {
x: "Date",
y: "CloseStandardized",
stroke: "red"}),
Plot.ruleY([0]),
Plot.lineY(data_standardized, {
x: "Date",
y: "VolumeStandardized",
stroke: "blue"})
],
})
startYear_slider
aq.from(data_standardized)
.filter(aq.escape (d => d["Date"] > 1185877562903))
.object()
data_standardized[0].Date.constructor.name
data_standardized[0].Date.getTime()
startYear_slider_asTimeObject = new Date(startYearFixed_slider, 1).getTime()
data_standardized_filtered= aq.from(data_standardized)
.filter(aq.escape(d => d["Date"]>startYear_slider_asTimeObject))
.objects()
// Uncomment to activate
import {Plot} from "@mkfreeman/plot-tooltip"
Plot.plot({
color: {
legend: true
},
y:{
grid: true,
nice: true,
label: "Z-Score"
},
x:{
grid: true,
nice: true
},
marks: [
Plot.lineY(data_standardized_filtered, {
x: "Date",
y: "CloseStandardized",
stroke:"salmon",
title: "CloseStandardized"
}),
Plot.lineY(data_standardized_filtered, {
x: "Date",
y: "VolumeStandardized",
stroke:"dodgerblue",
curve: "step",
title: d=> d3.format(".4s")(d["Volume"])
}
)]
})
Plot.plot({
color: {
legend: true
},
y:{
grid: true,
nice: true,
label: "Z-Score"
},
x:{
grid: true,
nice: true
},
marks: [
Plot.lineY(data_standardized_filtered, {
x: "Date",
y: "CloseStandardized",
stroke:"salmon",
title: d =>
` Close: ${ d3.format('.4s')(d["Close"]) }
Open: ${ d3.format('.4s')(d["Open"]) }
High: ${ d3.format('.4s')(d["High"]) }
Low: ${ d3.format('.4s')(d["Low"]) }`
}),
Plot.lineY(data_standardized_filtered, {
x: "Date",
y: "VolumeStandardized",
stroke:"dodgerblue",
curve: "step",
title: d =>
`Close: ${ d3.format('.4s')(d["Close"]) }
Open: ${ d3.format('.4s')(d["Open"]) }
High: ${ d3.format('.4s')(d["High"]) }
Low: ${ d3.format('.4s')(d["Low"]) }`
}
)]
})
import {Wrangler, op} from "@observablehq/data-wrangler"
import {toc} from "@nebrius/indented-toc"
import {imageToDo} from "@clokman/student-blocks"
imageToDo
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