2d:null 2e:["$","title",null,{"children":"호기심 해결 in 주식시장: CNN Fear & Greed Index는 과연 유익한 지표일까? | Humble Data Miner | Observable"}] 33:T68f,viewof threshold = { const height = 80; const fearslider = d3_slider.sliderBottom() .min(d3.min(fears)) .max(d3.max(fears)) .marks(fears) .width(300) .value(25) .fill("#eee") .on("onchange", () => svg.dispatch("input")); const greedslider = d3_slider.sliderBottom() .min(d3.min(greeds)) .max(d3.max(greeds)) .marks(greeds) .width(300) .value(75) .fill("#eee") .on("onchange", () => svg.dispatch("input")); const svg = d3.create("svg") .attr("viewBox", [0, 0, width, height]) .attr("width", width) .attr("height", height); const thresfear = svg.append("g") .attr("class","thresfear") .attr("transform",`translate(40,40)`) .call(fearslider); const fcolor = d3.color(d3.schemeCategory10[2]); fcolor.opacity = 0.7; thresfear.selectAll("line.track-fill").attr("stroke",fcolor); const thresgreed = svg.append("g") .attr("class","thresgreed") .attr("transform",`translate(380,40)`) .call(greedslider); const gcolor = d3.color(d3.schemeCategory10[3]); gcolor.opacity = 0.7; thresgreed.selectAll("line.track-inset").attr("stroke",gcolor); svg.append("text") .attr("text-anchor","middle").attr("dominant-baseline","hanging") .attr("x","190").attr("y",10).attr("dy",4).text("(공포상태)FGI 매수기준값"); svg.append("text") .attr("text-anchor","middle").attr("dominant-baseline","hanging") .attr("x","530").attr("y",10).attr("dy",4).text("(탐욕상태)FGI 매도기준값"); return Object.defineProperty( svg.node(), "value", {get: () => [fearslider.value(),greedslider.value()]} ); }34:T1268,{ const height = 300; const margin = {left:48, right:40, top:20, bottom: 20}; const aheight = height-margin.top-margin.bottom; // split area into two; line-graph and scatter-plot const dates = sp500_data.map(x=>x.date); const fgi = fgi_data.map(x=>x.fgiscore); const sp500 = sp500_data.map(x=>x.value); const ps = height; const gw = width-ps; // graph-width const svg = d3.create("svg").attr("viewBox", [0, 0, width, height]); const x = d3.scaleTime().domain(d3.extent(dates)).range([margin.left, gw-margin.right]); //x-axis of date-time const y1 = d3.scaleLinear().domain([0,100]).range([height-margin.bottom,margin.top]); // y-axis of fgi score const y2 = d3.scaleLinear().domain(d3.extent(sp500)).range([height-margin.bottom, margin.top]); // y-axis of sp500 index const data = processed_data[threshold]; const rectheight = height-margin.bottom-margin.top; const frect = svg.append("g").attr("fill",d3.schemeCategory10[2]).attr("opacity",0.4) .selectAll("rect") .data(data.frs) .join("rect") .attr("x",d=>x(d[0])).attr("y",margin.top) .attr("width",d=>x(d[1])-x(d[0])+1).attr("height",rectheight); const grect = svg.append("g").attr("fill",d3.schemeCategory10[3]).attr("opacity",0.4) .selectAll("rect") .data(data.grs) .join("rect").attr("x",d=>x(d[0])).attr("y",margin.top) .attr("width",d=>x(d[1])-x(d[0])+1).attr("height",rectheight); const line_fgi = d3.line().defined(d=>!isNaN(d.fgiscore)).x(d=>x(d.date)).y(d=>y1(d.fgiscore)); const p_fgi = svg.append("g").append("path") .attr("stroke",d3.schemeCategory10[0]).attr("stroke-opacity",0.7).attr("fill","none") .attr("d",line_fgi(fgi_data)); const line_sp500 = d3.line().x(d=>x(d.date)).y(d=>y2(d.value)); const p_sp500 = svg.append("g").append("path") .attr("stroke",d3.schemeCategory10[1]).attr("stroke-opacity",0.75).attr("fill","none") .attr("d",line_sp500(sp500_data)); svg.append("g").attr("transform", `translate(0,${height-margin.bottom})`).call(d3.axisBottom(x)); svg.append("g").attr("transform", `translate(${margin.left},0)`).call(d3.axisLeft(y1).tickFormat(x=>`${x.toFixed(0)}`)); svg.append("g").attr("transform", `translate(${gw-margin.right},0)`).call(d3.axisRight(y2).tickFormat(x=>`${x.toFixed(0)}`)); svg.append("text") .attr("x",x(dates.slice(-1)[0])).attr("y",y2(sp500.slice(-1)[0])).attr("dx",2) .attr("dominant-baseline","middle").attr("text-anchor","start") .attr("font-size","small") .text(`◀x${(sp500.slice(-1)[0]/sp500[0]).toFixed(2)}`); svg.append("text") .attr("x",x(dates[0])).attr("y",y2(sp500[0])).attr("dx",-2) .attr("dominant-baseline","middle").attr("text-anchor","end") .attr("font-size","small") .text(`x${(1.0).toFixed(2)}▶`); const pdim = {rows:fears.length+1,cols:greeds.length+1}; const psize = {width:ps/pdim.cols,height:ps/pdim.rows}; const matplot = svg.append("g").attr("transform",`translate(${gw-2},-2)`); const capext = [1.0, d3.max(Object.values(processed_data),d=>d.cap)]; for(let r = 0; r < pdim.rows; r++) { for(let c = 0; c < pdim.cols; c++) { if(r == 0 && c == 0) continue; if(r == 0) { matplot.append("text").attr("x",psize.width*(c+0.5)).attr("y",psize.height*(r+0.5)) .attr("dominant-baseline","middle").attr("text-anchor","middle").text(greeds[c-1]); } else if(c == 0) { matplot.append("text").attr("x",psize.width*(c+0.5)).attr("y",psize.height*(r+0.5)) .attr("dominant-baseline","middle").attr("text-anchor","middle").text(fears[fears.length-r]); } else { const f = fears[fears.length-r]; const g = greeds[c-1]; const key = [f,g]; const cap = processed_data[key].cap; const colorratio = (cap-capext[0])/(capext[1]-capext[0]); const fillcolor = d3.interpolateGreens(colorratio); matplot.append("rect") .attr("x",psize.width*c).attr("y",psize.height*r).attr("width",psize.width).attr("height",psize.height) .attr("fill",fillcolor).attr("stroke","black"); matplot.append("text").attr("class","capital") .attr("x",psize.width*(c+0.5)).attr("y",psize.height*(r+0.5)) .attr("dominant-baseline","middle").attr("text-anchor","middle") .attr("font-size","small").attr("fill","white").text(cap.toFixed(2)); } } } matplot.append("rect") .attr("x",psize.width*(greeds.findIndex(d=>d==threshold[1])+1)) .attr("y",psize.height*(fears.length-fears.findIndex(d=>d==threshold[0]))) .attr("width",psize.width).attr("height",psize.height) .attr("fill","none").attr("stroke","red").attr("stroke-width",4); return svg.node(); }35:T570,processed_data = { const processed = {}; for(let i=0; i < fears.length; i++) { for(let j=0; j < greeds.length; j++) { const f = fears[i]; const g = greeds[j]; const fis = fgi_data.map((d,i)=>[d,i]).filter(d=>d[0].fgiscore<=f); const gis = fgi_data.map((d,i)=>[d,i]).filter(d=>d[0].fgiscore>=g); function partition(arr) { return arr.reduce((acc, cur) => { if (acc.length == 0 || acc.slice(-1)[0].slice(-1)[0][1] != cur[1]-1) { acc.push([]); } acc.slice(-1)[0].push(cur); return acc; }, []); } function calculate(fis,gis,initcap=1.0) { let cap = initcap; if(fis.length == 0) return cap; const fidx = fis.map(d=>d[1]); const gidx = gis.map(d=>d[1]); let fi = 0; let gi = 0; while((gi = d3.bisect(gidx,fidx[fi])) < gidx.length) { cap *= fgi_data[gidx[gi]].sp500/fgi_data[fidx[fi]].sp500 fi = d3.bisect(fidx, gidx[gi]) if(fi >= fidx.length) break; } return cap; } processed[[f,g]] = { frs:partition(fis).map(arr=>[arr[0][0].date,arr.slice(-1)[0][0].date]), grs:partition(gis).map(arr=>[arr[0][0].date,arr.slice(-1)[0][0].date]), cap:calculate(fis,gis) }; } } return processed; 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31:{"id":"75e69923b40fb3bc","slug":"cnn-fear-and-greed-index-review","title":"호기심 해결 in 주식시장: CNN Fear & Greed Index는 과연 유익한 지표일까?","thumbnail":"1cbd0aa7d6836fff75b0b1ebe5f88aac7f2e2abb50908bc8b3136d5e52769a80","access_level":"public","create_time":"$D2023-05-09T19:40:25.102Z","update_time":"$D2024-08-05T17:47:55.529Z","view_time":null,"first_public_time":"$D2023-05-09T19:40:25.102Z","has_unpublished_edits":null,"fork":false,"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":467,"publish_version":null,"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":"ffed4a92f8068ba7","slug":"myth-buster-in-person","title":"Myth Buster in Person","owner":{"id":"50faa4da52a9ff9b","login":"humble-data-miner","name":"Humble Data Miner"}},"type":"notebook","body":{"title":"호기심 해결 in 주식시장: CNN Fear & Greed Index는 과연 유익한 지표일까?","cells":[{"id":0,"value":"# 호기심 해결 in 주식시장: CNN Fear & Greed Index는 과연 유익한 지표일까?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":32,"value":"* 확인할 질문\n * CNN Fear & Greed Index는 투자에 도움이 될까?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":35,"value":"---\n## [CNN Fear & Greed Index](https://edition.cnn.com/markets/fear-and-greed) (이하 FGI)\n* CNN Business에서 발표하는 **시장의 감정상태에 대한 지표**\n * 0~25(심한공포),25~45(공포),45~55(중립),55~75(탐욕),75~100(심한탐욕)\n* **7가지 서로다른 세부지표들**을 같은비중으로 합산\n * Market Momentum, Stock Price Strength, Stock Price Breadth, Put & Call Options, Market Volatility, Safe Haven Demand, Junk Bond Demand\n* 시장의 감정상태와 관련된 일반론\n * **과도한 공포**는 가격을 낮추고 **과도한 탐욕**은 가격을 올리는 경향\n * 공포에 사고 탐욕에 팔아라? 주식시장 **가격변동의 변곡점 탐지**를 목표로함\n> “Be fearful when others are greedy, and greedy when others are fearful.” - Warren Buffett","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":66,"value":"---\n## BackTesting 설정\n* 데이터셋\n * 2011-01-03부터 2023-05-09까지의 ${md_fgi()}와 ${md_sp500()} 지수값\n * FGI의 경우 2020-09-20에서 2021-01-20까지의 값이 누락됨\n* 거래대상은 ${md_sp500()}지수로 한정\n * 미국에 상장된 시가총액 상위 500개 기업의 주식들을 모아만든 지수\n * 미국 주식시장 전체에 대한 투자성과 평가에 일반적으로 사용된다\n * 실제 ${md_fgi()}의 7개 세부지표중 2개 세부지표의 산정기준\n* 단순한 매매체계를 가정해 수익배율을 계산\n * (공포상태)매도후 ${md_fgi()}가 일정값 이하로 내려간 첫 시점에 전액 매수\n * (탐욕상태)매수후 ${md_fgi()}가 일정값 이상으로 올라간 첫 시점에 전액 매도","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":69,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":133,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":325,"value":"## 결과 해석\n* 기준점=3.25배 자산(첫날매수후 마지막날 매도)\n* FGI를 이용한 어떤결과도 기준점을 넘지못함, 따라서 FGI는 무쓸모?\n * **FGI 매도기준값이 85이상인 경우** 2020년 이후에 매도가능한 경우없음\n * (매수,매도기준값)이 각각 (5,95)인 경우를 예로들면\n * 2020년에 마지막 매도시점의 자산이 2.61배\n * 2020년 3월경에 마지막 매수후 수익실현을 안한상태\n * 최근에 매도해 수익실현을 했다고 가정하면 자산이 대략 3.7배\n * **FGI 매도기준값 85이상인 경우**는 기준점과 공정비교불가\n* 수익배율 결과표로 살펴본 FGI의 특성\n * 매수기준값이 높고(↑) 매도기준값이 높을수록(→) 좋은결과\n * 다시말해 다소간 하락요인이 있을때 쉽게 사고, 까다롭게 파는것이 유리\n * 지수가 결과적으로 우상향한다면 가능한 투자휴지기를 덜 갖는게 중요\n* 2020년 이후의 FGI의 경향\n * FGI의 심한공포상태는 이전과 비슷한 추세로 나타남\n * FGI의 심한탐욕상태는 찾아보기 힘듦\n * 높은 FGI매도기준값으로는 영원히 못팔수도?\n * 2022년말에 S&P500지수가 최고조에 달했을때 FGI가 최대75.6에 불과\n * 바뀐 시장환경에 맞도록 FGI의 수정보완이 필요","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":387,"value":"## 답변의 시간\n\n* CNN Fear & Greed Index는 투자에 도움이 될까?\n * 보조지표로 참고할만하다(주지표로는 부족하다)\n * 목표지수의 하락/상승시, 그 변동의 특성을 분석할때\n * 극단적인 FGI값(5이하, 95이상)은 활용성이 있다\n * 하지만 무척 빈도가 낮아, 여러 좋은기회를 놓칠수 있다","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":23,"value":"---\n## 참고자료\n* CNN 공식 웹페이지: [Fear and Greed Index - Investor Sentiment | CNN](https://edition.cnn.com/markets/fear-and-greed)\n* YouTube 동영상 FGI 설명 및 개발 참고자료: [Historical Fear and Greed Index Data, Jupyter Notebooks - YouTube](https://www.youtube.com/watch?v=6r3Ecf8KkTI)\n---\n## 부연설명 동영상\n\n\n---\n## 부록","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":164,"value":"fgi_data = await FileAttachment(\"cnn_fgi@2.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"fgi_data","database":"$undefined"},{"id":172,"value":"sp500_data = await FileAttachment(\"sp500.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"sp500_data","database":"$undefined"},{"id":202,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":255,"value":"fears = d3.range(5,50,5)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":258,"value":"greeds = d3.range(55,100,5)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":451,"value":"md_fgi = _=> html`FGI`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":454,"value":"md_sp500 = _=> html`S&P500`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":245,"value":"{\n const date_sp500_map = sp500_data.reduce((acc,cur,i)=>{ acc[cur.date]=cur.value; return acc; },{});\n fgi_data.forEach((d,i)=>{ d[\"sp500\"]=date_sp500_map[d.date]; });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"html`\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"d3_slider= require(\"d3-simple-slider\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":433,"value":"import {Swatches} from \"@humble-data-miner/inputsmore\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W36","stdlib":"1","titling":"auto"}} 32:"$31:body"