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OHLC aggregation example.notebook
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{
"version": 1,
"sheet": {
"id": "e7609d50-4d28-11e9-aecb-e12689d681c1",
"language": "javascript",
"cells": [
{
"id": "3a576070-4d2e-11e9-aecb-e12689d681c1",
"cellType": "markdown",
"code": "# Example of OHLC aggregation\r\n\r\n## Load and preview the data",
"lastEvaluationDate": "2019-03-23T15:58:22.975+10:00",
"output": [],
"errors": []
},
{
"id": "e7609d51-4d28-11e9-aecb-e12689d681c1",
"cellType": "code",
"cellScope": "global",
"code": "const dataForge = require(\"data-forge\");\r\nconst axios = require(\"axios\");\r\nconst response = await axios.get(\"https://pastebin.com/raw/6PGbw5MG\"); // Load data via HTTP get.\r\nlet df = new dataForge.DataFrame(response.data); // Load data into a dataframe.\r\ndisplay(df.head(5)); // Preview the data.",
"lastEvaluationDate": "2019-03-23T15:54:28.770+10:00",
"output": [
{
"values": [
{
"data": {
"columnOrder": [
"symbol",
"sector",
"securityType",
"bidPrice",
"bidSize",
"askPrice",
"askSize",
"lastUpdated",
"lastSalePrice",
"lastSaleSize",
"lastSaleTime",
"volume",
"ts"
],
"columns": {
"symbol": "string",
"sector": "string",
"securityType": "string",
"bidPrice": "number",
"bidSize": "number",
"askPrice": "number",
"askSize": "number",
"lastUpdated": "number",
"lastSalePrice": "number",
"lastSaleSize": "number",
"lastSaleTime": "number",
"volume": "number",
"ts": "number"
},
"index": {
"type": "number",
"values": [
0,
1,
2,
3,
4
]
},
"values": [
{
"symbol": "AAPL",
"sector": "electronictechnology",
"securityType": "cs",
"bidPrice": 194.37,
"bidSize": 100,
"askPrice": 195,
"askSize": 100,
"lastUpdated": 1553188361686,
"lastSalePrice": 194.59,
"lastSaleSize": 50,
"lastSaleTime": 1553188350343,
"volume": 705106,
"ts": 1553188362.345773,
"__index__": 0
},
{
"symbol": "AAPL",
"sector": "electronictechnology",
"securityType": "cs",
"bidPrice": 175,
"bidSize": 100,
"askPrice": 194.81,
"askSize": 100,
"lastUpdated": 1553184717909,
"lastSalePrice": 194.6,
"lastSaleSize": 100,
"lastSaleTime": 1553184696547,
"volume": 605389,
"ts": 1553184718.096802,
"__index__": 1
},
{
"symbol": "AAPL",
"sector": "electronictechnology",
"securityType": "cs",
"bidPrice": 194.74,
"bidSize": 100,
"askPrice": 195.52,
"askSize": 136,
"lastUpdated": 1553180999484,
"lastSalePrice": 194.73,
"lastSaleSize": 100,
"lastSaleTime": 1553180996131,
"volume": 455249,
"ts": 1553180999.94565,
"__index__": 2
},
{
"symbol": "AAPL",
"sector": "electronictechnology",
"securityType": "cs",
"bidPrice": 176.48,
"bidSize": 2000,
"askPrice": 192.98,
"askSize": 800,
"lastUpdated": 1553177407202,
"lastSalePrice": 192.96,
"lastSaleSize": 7,
"lastSaleTime": 1553177406620,
"volume": 223647,
"ts": 1553177406.926868,
"__index__": 3
},
{
"symbol": "AAPL",
"sector": "electronictechnology",
"securityType": "cs",
"bidPrice": 188.65,
"bidSize": 200,
"askPrice": 188.66,
"askSize": 100,
"lastUpdated": 1553111181042,
"lastSalePrice": 188.65,
"lastSaleSize": 100,
"lastSaleTime": 1553111180653,
"volume": 694498,
"ts": 1553111185.513574,
"__index__": 4
}
]
},
"displayType": "dataframe"
}
]
}
],
"errors": []
},
{
"id": "47e587d0-4d2e-11e9-aecb-e12689d681c1",
"cellType": "markdown",
"code": "## Parse dates into JavaScript date objects",
"lastEvaluationDate": "2019-03-23T15:58:22.976+10:00",
"output": [],
"errors": []
},
{
"id": "2099de60-4d2e-11e9-aecb-e12689d681c1",
"cellType": "code",
"cellScope": "global",
"code": "df = df.transformSeries({ lastSaleTime: d => new Date(d) }) // Convert time to JS date object.\r\n .bringToFront(\"lastSaleTime\"); // Bring this column to the front so I can see it in the preview.\r\ndisplay(df.head(5)); // Preview again to make sure it worked.",
"lastEvaluationDate": "2019-03-23T15:54:28.775+10:00",
"output": [
{
"values": [
{
"data": {
"columnOrder": [
"lastSaleTime",
"symbol",
"sector",
"securityType",
"bidPrice",
"bidSize",
"askPrice",
"askSize",
"lastUpdated",
"lastSalePrice",
"lastSaleSize",
"volume",
"ts"
],
"columns": {
"lastSaleTime": "date",
"symbol": "string",
"sector": "string",
"securityType": "string",
"bidPrice": "number",
"bidSize": "number",
"askPrice": "number",
"askSize": "number",
"lastUpdated": "number",
"lastSalePrice": "number",
"lastSaleSize": "number",
"volume": "number",
"ts": "number"
},
"index": {
"type": "number",
"values": [
0,
1,
2,
3,
4
]
},
"values": [
{
"symbol": "AAPL",
"sector": "electronictechnology",
"securityType": "cs",
"bidPrice": 194.37,
"bidSize": 100,
"askPrice": 195,
"askSize": 100,
"lastUpdated": 1553188361686,
"lastSalePrice": 194.59,
"lastSaleSize": 50,
"lastSaleTime": "2019-03-22T03:12:30.343+10:00",
"volume": 705106,
"ts": 1553188362.345773
},
{
"symbol": "AAPL",
"sector": "electronictechnology",
"securityType": "cs",
"bidPrice": 175,
"bidSize": 100,
"askPrice": 194.81,
"askSize": 100,
"lastUpdated": 1553184717909,
"lastSalePrice": 194.6,
"lastSaleSize": 100,
"lastSaleTime": "2019-03-22T02:11:36.547+10:00",
"volume": 605389,
"ts": 1553184718.096802
},
{
"symbol": "AAPL",
"sector": "electronictechnology",
"securityType": "cs",
"bidPrice": 194.74,
"bidSize": 100,
"askPrice": 195.52,
"askSize": 136,
"lastUpdated": 1553180999484,
"lastSalePrice": 194.73,
"lastSaleSize": 100,
"lastSaleTime": "2019-03-22T01:09:56.131+10:00",
"volume": 455249,
"ts": 1553180999.94565
},
{
"symbol": "AAPL",
"sector": "electronictechnology",
"securityType": "cs",
"bidPrice": 176.48,
"bidSize": 2000,
"askPrice": 192.98,
"askSize": 800,
"lastUpdated": 1553177407202,
"lastSalePrice": 192.96,
"lastSaleSize": 7,
"lastSaleTime": "2019-03-22T00:10:06.620+10:00",
"volume": 223647,
"ts": 1553177406.926868
},
{
"symbol": "AAPL",
"sector": "electronictechnology",
"securityType": "cs",
"bidPrice": 188.65,
"bidSize": 200,
"askPrice": 188.66,
"askSize": 100,
"lastUpdated": 1553111181042,
"lastSalePrice": 188.65,
"lastSaleSize": 100,
"lastSaleTime": "2019-03-21T05:46:20.653+10:00",
"volume": 694498,
"ts": 1553111185.513574
}
]
},
"displayType": "dataframe"
}
]
}
],
"errors": []
},
{
"id": "ab3d33a0-4d2e-11e9-aecb-e12689d681c1",
"cellType": "markdown",
"code": "## Group by time period and aggregate\r\n\r\nUsing the moment library for date formatting.",
"lastEvaluationDate": "2019-03-23T15:58:22.976+10:00",
"output": [],
"errors": []
},
{
"id": "9158efb0-4d2e-11e9-aecb-e12689d681c1",
"cellType": "code",
"cellScope": "global",
"code": "const moment = require(\"moment\");\r\n\r\n//\r\n// Make a key from the time that we can use to \r\n// group by day (or my minute, hour, week, month, whatever period you need).\r\n//\r\nfunction makeGroupKey(time) {\r\n return moment(time).format(\"YYYY-MM-DD\");\r\n}\r\n\r\n\r\nconst aggregated = df.groupBy(row => makeGroupKey(row.lastSaleTime)) // Group data by lastSaleTime\r\n .select(group => {\r\n return {\r\n date: moment(group.first().lastSaleTime).startOf('day').toDate(), // Extract the day.\r\n open: group.first().bidPrice, // Opening bid.\r\n high: group.deflate(row => row.bidPrice).max(), // Highest bid in the group.\r\n low: group.deflate(row => row.bidPrice).min(), // Lowest bid in the group.\r\n close: group.last().bidPrice, // Closing bid.\r\n };\r\n })\r\n .inflate();\r\ndisplay(aggregated.head(5));",
"lastEvaluationDate": "2019-03-23T15:54:28.779+10:00",
"output": [
{
"values": [
{
"data": {
"columnOrder": [
"date",
"open",
"high",
"low",
"close"
],
"columns": {
"date": "date",
"open": "number",
"high": "number",
"low": "number",
"close": "number"
},
"index": {
"type": "number",
"values": [
0,
1,
2
]
},
"values": [
{
"date": "2019-03-22T00:00:00.000+10:00",
"open": 194.37,
"high": 194.74,
"low": 175,
"close": 176.48
},
{
"date": "2019-03-21T00:00:00.000+10:00",
"open": 188.65,
"high": 188.65,
"low": 188.65,
"close": 188.65
},
{
"date": "2019-03-23T00:00:00.000+10:00",
"open": 192.33,
"high": 192.33,
"low": 192.33,
"close": 192.33
}
]
},
"displayType": "dataframe"
}
]
}
],
"errors": []
},
{
"id": "963ad330-4d2f-11e9-aecb-e12689d681c1",
"cellType": "markdown",
"code": "## Plot the data\r\n\r\n(There's not much data in this example to plot, but add some data and the plot will look more interesting)",
"lastEvaluationDate": "2019-03-23T15:58:22.976+10:00",
"output": [],
"errors": []
},
{
"id": "9ef8add0-4d2f-11e9-aecb-e12689d681c1",
"cellType": "code",
"cellScope": "global",
"code": "require(\"data-forge-plot\");\r\n\r\ndisplay(aggregated.plot({}, { x: \"date\" }));",
"lastEvaluationDate": "2019-03-23T15:54:28.784+10:00",
"output": [
{
"values": [
{
"data": {
"data": {
"columnOrder": [
"date",
"open",
"high",
"low",
"close",
"__index__"
],
"columns": {
"date": "date",
"open": "number",
"high": "number",
"low": "number",
"close": "number",
"__index__": "number"
},
"index": {
"type": "number",
"values": [
0,
1,
2
]
},
"values": [
{
"date": "2019-03-21T14:00:00.000Z",
"open": 194.37,
"high": 194.74,
"low": 175,
"close": 176.48,
"__index__": 0
},
{
"date": "2019-03-20T14:00:00.000Z",
"open": 188.65,
"high": 188.65,
"low": 188.65,
"close": 188.65,
"__index__": 1
},
{
"date": "2019-03-22T14:00:00.000Z",
"open": 192.33,
"high": 192.33,
"low": 192.33,
"close": 192.33,
"__index__": 2
}
]
},
"plotConfig": {
"chartType": "line",
"width": 800,
"height": 600,
"template": "c3",
"x": {
"axisType": "default",
"label": {}
},
"y": {
"axisType": "default",
"label": {}
},
"y2": {
"axisType": "default",
"label": {}
},
"legend": {
"show": true
}
},
"axisMap": {
"x": {
"series": "date"
},
"y": [
{
"series": "open"
},
{
"series": "high"
},
{
"series": "low"
},
{
"series": "close"
},
{
"series": "__index__"
}
],
"y2": []
}
},
"displayType": "chart"
}
]
}
],
"errors": []
}
]
}
}