2e:null 2f:["$","title",null,{"children":"Stock Investment in a Greenhouse Part1 | Humble Data Miner | Observable"}] 34:Tef8,{ replay; const n = 2400/period.value; const radius = 1*Math.sqrt(period.value); const dodge = dodger(radius * 2 + Math.sqrt(period.value)/2); const margin = {top: 0, right: 30, bottom: 20, left: 30}; const asset = assets[assetindex]; const mu = asset.mu; const sigma = asset.sigma; const color = asset.Color; const values1 = Float64Array.from({length: n}, d3.randomNormal(mu, sigma)); const values2 = values1.map(x => Math.exp(x - sigma**2/2)); let productSum = 1.0; let values3 = [productSum]; for (let i = 0; i < n ; ++i) values3.push(productSum *= values2[i]); const extent1 = d3.extent(values1); const extent2 = d3.extent(values2); const extent3x = [0,10]; const extent3y = [0,d3.max(values3)]; const x1 = d3.scaleLinear(extent1, [margin.left/2, width/3 - margin.right/2]).nice(); const x2 = d3.scaleLinear(extent2, [margin.left/2 + width/3, width*2/3 - margin.right/2]); const x3 = d3.scaleLinear(extent3x, [margin.left/2 + width*2/3, width - margin.right*1.1]); const y3 = d3.scaleLinear(extent3y, [height - margin.bottom, margin.top]); const svg = d3.select(DOM.svg(width, height)).style("overflow", "visible"); yield svg.node(); let axis_x3 = d3.axisBottom(x3).ticks(10).tickFormat((x,i) => `${i}Y`); svg.append("g") .attr("transform", `translate(0,${height - margin.bottom})`) .call(d3.axisBottom(x1)); svg.append("g") .attr("transform", `translate(0,${height - margin.bottom})`) .call(d3.axisBottom(x2).tickFormat(x => `x${x}`)); svg.append("g") .attr("transform", `translate(0,${height - margin.bottom})`) .call(d3.axisBottom(x3).tickFormat(x => `${x}Y`)); svg.append("g") .attr("transform", `translate(${width - margin.right},0)`) .call(d3.axisRight(y3).tickFormat(x => `x${x}`)); for (let i = 0; i < n; ++i) { if (i % 20 == 0) yield svg.node(); const cx1 = x1(values1[i]); const cy1 = height - margin.bottom - dodge(cx1) - radius - 1; const cx2 = x2(values2[i]); const cy2 = height - margin.bottom - dodge(cx2) - radius - 1; const lx1 = x3(i*10/n); const lx2 = x3((i+1)*10/n); const ly1 = y3(values3[i]); const ly2 = y3(values3[i+1]); //if (cy1 < margin.top || cy2 < margin.top) break; svg.append("circle") .attr("cx", cx1) .attr("cy", -400) .attr("r", radius) .attr("cy", cy1); svg.append("circle") .attr("cx", cx2) .attr("cy", -400) .attr("r", radius) .attr("cy", cy2); svg.append("line") .style("stroke", color) .style("stroke-width", 1) .attr("x1", lx1) .attr("y1", ly1) .attr("x2", lx2) .attr("y2", ly2); } yield svg.node(); let p1 = []; let p2 = []; let quantileticks = 40; for (let i = 0; i <= quantileticks ; i++) { let q = extent1[0] + i*(extent1[1] - extent1[0])/quantileticks; p1.push({x: q, y: prob_normal(q, mu, sigma)}); p2.push({x: Math.exp(q-sigma**2/2), y: prob_lognormal(q, mu, sigma)}); } const py1 = d3.scaleLinear([0, d3.max(p1, d=>d.y)*1.1], [height - margin.bottom, margin.top]); const py2 = d3.scaleLinear([0, d3.max(p2, d=>d.y)*1.2], [height - margin.bottom, margin.top]); let line1 = d3.line().x(d => x1(d.x)).y(d => py1(d.y)); let line2 = d3.line().x(d => x2(d.x)).y(d => py2(d.y)); svg.append("path") .datum(p1) .attr("fill", "none") .attr("stroke", color) .attr("stroke-width", 2) .attr("stroke-dasharray", "4,4") .attr("stroke-linejoin", "round") .attr("stroke-linecap", "round") .attr("d", line1); svg.append("path") .datum(p2) .attr("fill", "none") .attr("stroke", color) .attr("stroke-width", 2) .attr("stroke-dasharray", "4,4") .attr("stroke-linejoin", "round") .attr("stroke-linecap", "round") .attr("d", line2); yield svg.node(); }35:[] 32:{"id":"8cc9221e66414c4e","slug":"stock-investment-in-a-greenhouse-part1","title":"Stock Investment in a Greenhouse Part1","thumbnail":"c974013679cd849590b18919cd30c85daea7552d5f51f42065987fe8785a55fe","access_level":"public","create_time":"$D2021-06-29T17:32:49.222Z","update_time":"$D2021-06-29T20:16:57.541Z","view_time":null,"first_public_time":"$D2021-06-29T20:07:27.758Z","has_unpublished_edits":null,"fork":{"id":"8c0d267b44b5bee7","slug":"stock-investment-in-a-greenhouse-part1-korean","title":"Stock Investment in a Greenhouse Part1 (Korean)","owner":{"id":"50faa4da52a9ff9b","login":"humble-data-miner","name":"Humble Data Miner"}},"stars":0,"starred":false,"owner":{"type":"team","id":"50faa4da52a9ff9b","login":"humble-data-miner","name":"Humble Data Miner","avatar_url":"https://avatars-next.observableusercontent.com/3046b1bea86c5e825baea7c0c403d90ad9690b5396b11b5d02b3a0839fbcb946","tier":"starter_2024"},"version":1272,"publish_version":1272,"role":null,"authored":false,"authors":[{"type":"individual","id":"4e2fc41e06361cc3","login":"humble-data-miner","name":"Humble Data Miner","avatar_url":"https://avatars-next.observableusercontent.com/3046b1bea86c5e825baea7c0c403d90ad9690b5396b11b5d02b3a0839fbcb946","tier":null}],"members":[{"type":"individual","id":"4e2fc41e06361cc3","login":"humble-data-miner","name":"Humble Data Miner","avatar_url":"https://avatars-next.observableusercontent.com/3046b1bea86c5e825baea7c0c403d90ad9690b5396b11b5d02b3a0839fbcb946","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":{"id":"693a1556761fc3fc","slug":"stock-investment-in-a-greenhouse","title":"Stock Investment in a Greenhouse","owner":{"id":"50faa4da52a9ff9b","login":"humble-data-miner","name":"Humble Data Miner"}},"type":"notebook","body":{"title":"Stock Investment in a Greenhouse Part1","cells":[{"id":786,"value":"md`# Stock Investment in a Greenhouse Part1`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":812,"value":"md`---\n## What is this \"Stock Investment in a Greenhouse\"?\n
\nStock market runs in a quite complex manner and we cannot predict the future of stock prices almost everytime. Some people say that diversified investment for a long term is the best way we can do but this sounds conventional without firm grounds. What should we do? To bet all in one looking promising or not to bet for no loss?\n\nTo simplify the characteristics of stock market, hereafter we're standing on the simple supposition that\n1. Economic growth leads to corporations' higher value\n1. Eventually price of stocks represent corporations' value\n1. Consistent economic growth increases price of stocks in the long run\n\nSuppose we can accept this statement \"stock prices goes upward\", it is still questionable that blue chip and long term investment always win this game. That's why I'm trying to have an experiment focusing only on volatiliy without concern of other external factors, \"in a greenhouse\" in other words.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":826,"value":"md`---\n## Stock Investment in a Greenhouse Part1: volatility representation of stock price\n
\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":839,"value":"md`\n### only ${data.length} stocks left in the world\n
\nFor brievity, let's assume that there left ${data.length} stocks. Besides, here defines 'gross return' as the ratio of the new price after a holding period over the original price so gross return 1.0 indicates nothing changed in the price after a holding period. This stocks has two volatility statistics for a holding period as follows:\n\n* inclination: slope of increment or decrement of gross return after a holding period\n* variance: variability of gross return for a holding period\n\nIn the following table, these two volatility statistics for each stock is given computed in advance. You can choose the holding period and the statistics numbers change according to the period you picked\n`\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":678,"value":"viewof period = {\n const options = [\n { label: \"one day (1 day)\", value: 1 },\n { label: \"one month (20 days)\", value: 20 },\n { label: \"one quarter (60 days)\", value: 60 },\n { label: \"one year (240 days)\", value: 240 },\n ];\n return Inputs.select(options, {label: \"holding period\", format: x => x.label })\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":281,"value":"table(assets, {\n sortable: true,\n columns: {\n \"Color\": { title: \"color\", formatter(val) { return html``; } },\n \"Name\" : { title: \"name\"},\n \"mu\": { title: \"μ(inclination)\", formatter(val) { return d3.format(\".4f\")(val); } },\n \"sigma\": { title: \"σ(variance)\", formatter(val) { return d3.format(\".3f\")(val); } },\n \"MultiplicativeMean\": { title: \"multiplicative mean\", formatter(val) { return d3.format(\".5f\")(val); } }\n }\n})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":891,"value":"md`\n
\n### 10-years simulation per stock\n
\n\nTry to select the stock to simulate. One black dot implies prices changes for the holding period and dot is stochastically produced based on the volatility statistics of the chosen stock.\n\n1. [Left Figure] log(gross return) placed between -∞ and +∞\n1. [Middle Figure] gross return between 0 and +∞\n1. [Right Figure] gross return changes for 10 years\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":392,"value":"viewof assetindex = Inputs.select([...Array(data.length).keys()], {label: \"target stock\", format: i => data[i].name});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1206,"value":"viewof replay = button({value: \"simulation once again\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":414,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":921,"value":"md`\n
\n### Limitation on this representation method\n
\n* It's unrealistic that inclination and variance are fixed. Corporation value keeps changing\n* Assuming that gross return follows designated probability distribution (log-normal distribution). Thus this never predicts drastic market collapse like the subprime mortgage crisis in 2008\n* This's inapplicable to the real estate asset, which is not close to perfect competition market\n* This's inapplicable to crypto-currency as an asset, which is unlikely to corrected to economic growth\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":915,"value":"md`\n
\n### Closing part1\n
\n* This post isn't targeting at how to select a good stock\n* In the part2, more plausible simulations would be placed based on the volatility statistics mentioned here\n* In the part2 and part3, the volatility statistics of those example stocks would be represented only by gross return between 0 and +∞ for 1-year holding as below\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1176,"value":"{\n let items = _.cloneDeep(data);\n const dt = 240;\n for (let i = 0; i < items.length; i++)\n items[i].name = data[i].name + \" \" + label_mu_sigma(data[i].mu*dt, data[i].sigma*Math.sqrt(dt));\n return draw_lognormal_pdf(items, 100, 240, margin, width*2/3, height)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1157,"value":"md`---\n## Reference\n
\n* [Geometric Brownian motion - Wikipedia](https://en.wikipedia.org/wiki/Geometric_Brownian_motion)\n* [Log-normal distribution - Wikipedia](https://en.wikipedia.org/wiki/Log-normal_distribution)\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":203,"value":"md`---\n\n## Appendix`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":560,"value":"html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":535,"value":"data = {\n var d = stocks10;\n for (let i = 0; i < d.length; i++)\n d[i]['name'] = \"stock #\" + (i+1);\n return d;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":333,"value":"assets = {\n var assets = [];\n for (var i = 0; i < data.length; i++) {\n let mu = data[i].mu * period.value;\n let sigma = data[i].sigma * Math.sqrt(period.value);\n assets.push(\n {\n \"Color\": d3.schemeCategory10[i],\n \"Name\": data[i].name,\n \"mu\": mu,\n \"sigma\": sigma,\n \"MultiplicativeMean\": Math.exp(mu),\n });\n }\n return assets;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"margin = ({top: 10, right: 10, bottom: 10, left: 10})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":59,"value":"height = 250","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"d3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1166,"value":"import {stocks10, prob_normal, prob_lognormal, draw_lognormal_pdf, label_mu_sigma} from \"@humble-data-miner/finance-library\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":277,"value":"import {table} from \"@humble-data-miner/tables\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":404,"value":"import {dodger} from \"@d3/d3-random\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":608,"value":"import {input, button} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W35","stdlib":"1","titling":"auto"}} 33:"$32:body"