2e:null 2f:["$","title",null,{"children":"호기심 해결 in 주식시장: 예측모델로 보는 005930의 주요특성과 투자전략 | Humble Data Miner | Observable"}] 34:T146d,chart = { const dispitems = 10; const svg = d3.create("svg").attr("viewBox", [0, 0, width, height]); const x1 = axis_x1; const x2 = d3.scaleLinear(extent.impact,[width-margin.right-plotdim.width,width-margin.right]); svg.append("g").attr("transform",`translate(0,${margin.top})`).call(d3.axisTop(x1)); svg.append("g").attr("transform",`translate(0,${margin.top})`).call(d3.axisTop(x2)); const y = axis_y; const g_yl = svg.append("g").attr("transform",`translate(${margin.left+plotdim.width},0)`); const g_yr = svg.append("g").attr("transform",`translate(${width-margin.right-plotdim.width},0)`); const beeswarm = beeswarmForce() .x(d => x1(d.m[0])) .y(y(0)+y.bandwidth()/2) .r(d=>Math.max(1,Math.sqrt(d.c)/16)); const label_fname = (fi,data) => `${(fi==0?"":"평균: ")+((data.m[1]/100000000)>1?`${Math.floor(data.m[1]/100000000)}억원`:data.m[1])}\n표본수: ${data.c}`; const dtitle = svg.append("g"); dtitle.selectAll("text") .data(series) .join("text") .attr("x",(d,i)=>i*width+width/2).attr("y",0).attr("dy",3) .attr("dominant-baseline","hanging").attr("text-anchor","middle") .attr("font-weight","bold").attr("font-size","x-large") .text((seq,i)=>seq.label); svg.append("text").attr("text-anchor","middle").attr("dominant-baseline","hanging") .attr("x",margin.left+plotdim.width/2).attr("y",40).text("SHAP value (impact on model output)"); svg.append("text").attr("text-anchor","middle").attr("dominant-baseline","hanging") .attr("x",width-margin.right-plotdim.width/2).attr("y",40).text("mean(|SHAP value|) (average impact on model output magnitude)"); const g_bar = svg.append("g"); const g_swarms = d3.range(series.length) .map(si=>d3.range(fields.length) .map(fi=> svg.append("g").attr("visibility","hidden").attr("transform",`translate(0,${y(series[si].stat[fi].fi)})`) )); g_swarms.forEach((g_swarms_per_seq,si)=>{ g_swarms_per_seq.forEach((g,fi)=>{ const stat = series[si].stat[fi]; g.selectAll("circle") .data(stat.swarm) .join("circle") .attr("cx",d=>d.x).attr("cy",d=>d.y).attr("r",d=>Math.max(2,d.r)) .attr("fill",d=>d3.interpolateRdYlBu(1-d.data.fnorm)) .append("title").text((d,i)=>`${label_fname(stat.fi,d.data)}`); }); }); function ramp(color, n) { const canvas = DOM.canvas(n, 1); const context = canvas.getContext("2d"); canvas.style.margin = "0 -14px"; canvas.style.width = "calc(100% + 28px)"; canvas.style.height = "40px"; canvas.style.imageRendering = "-moz-crisp-edges"; canvas.style.imageRendering = "pixelated"; for (let i = 0; i < n; ++i) { context.fillStyle = color(i / (n - 1)); context.fillRect(i, 0, 1, 1); } return canvas; } svg.append("image") .attr("transform",`translate(${margin.left+20},${height/2}) rotate(90)`) .attr("width", height/2-20) .attr("height", 20) .attr("preserveAspectRatio", "none") .attr("xlink:href", ramp(d3.interpolateRdYlBu,300).toDataURL()); svg.append("text") .attr("text-anchor","start").attr("dominant-baseline","hanging").attr("font-size",14).attr("font-weight","bold") .attr("fill",d3.interpolateRdYlBu(0)) .attr("x",margin.left+20).attr("y",height/2).text("높은특성값"); svg.append("text") .attr("text-anchor","start").attr("dominant-baseline","auto").attr("font-size",14).attr("font-weight","bold") .attr("fill",d3.interpolateRdYlBu(1)) .attr("x",margin.left+20).attr("y",height-20).text("낮은특성값"); const name_field = i=>fields[i][1]; function _update(ns) { const tr1 = svg.transition().duration(anim_dur).ease(d3.easeLinear); const tr2 = svg.transition().duration(anim_dur).ease(d3.easeCubic); y.domain(ns.stat.map(d=>d.fi)); g_yl.selectAll("text").attr("text-anchor","middle").attr("dx",31); g_yl.transition(tr2).call(d3.axisRight(y).tickFormat(name_field)); g_yr.transition(tr2).call(d3.axisLeft(y).tickFormat(x=>"")); g_bar.selectAll("rect") .data(ns.stat,d=>d.fi) .join( enter => enter.append("rect") .attr("fill",d=>fields[d.fi][2]).attr("opacity",d=>d.impact/extent.impact[1]) .attr("x",x2(0)).attr("y",d=>y(d.fi)) .attr("width",d=>x2(d.impact)-x2(0)).attr("height",y.bandwidth()), update => update.transition(tr2) .attr("opacity",d=>d.impact/extent.impact[1]) .attr("y",d=>y(d.fi)) .attr("width",d=>x2(d.impact)-x2(0)).attr("height",y.bandwidth()), ); const next_si = series.findIndex(s=>s==ns); g_swarms.forEach((g_swarms_per_seq,si)=>{ g_swarms_per_seq.forEach((g,fi)=>{ const stat = series[si].stat[fi]; const show = next_si==si; if(show) g.attr("visibility","visible"); else g.transition().delay(anim_dur).attr("visibility","hidden"); g.transition(tr2).attr("transform",`translate(0,${y(stat.fi)})`).attr("opacity",show?1:0); }); }); dtitle.transition(tr2).attr("transform",`translate(${-next_si*width},0)`); } _update(series[0]); return Object.assign(svg.node(), { update(nextseq) { _update(nextseq); } }); }35:Tabc,arrow = { const svg = d3.create('svg').attr('viewBox', [0, 0, width, 10]); const markerBoxWidth=6; const markerBoxHeight=6; const refX = markerBoxWidth; const refY = markerBoxHeight/2; // See https://developer.mozilla.org/en-US/docs/Web/SVG/Element/marker const defs = svg.append('defs'); defs.append('marker') .attr('id', `arrowinc`) .attr('viewBox', [0, 0, markerBoxWidth, markerBoxHeight]) .attr('refX', refX/2) .attr('refY', refY) .attr('markerWidth', markerBoxWidth) .attr('markerHeight', markerBoxHeight) .attr('orient', 'auto-start-reverse') .append('path') .attr('d', d3.line()([[0,0],[0,markerBoxHeight],[markerBoxWidth,markerBoxHeight/2]])) .attr('stroke', 'red').attr('fill', 'red'); defs.append('marker') .attr('id', `arrowdec`) .attr('viewBox', [0, 0, markerBoxWidth, markerBoxHeight]) .attr('refX', refX/2) .attr('refY', refY) .attr('markerWidth', markerBoxWidth) .attr('markerHeight', markerBoxHeight) .attr('orient', 'auto-start-reverse') .append('path') .attr('d', d3.line()([[0,0],[0,markerBoxHeight],[markerBoxWidth,markerBoxHeight/2]])) .attr('stroke', 'blue').attr('fill', 'blue'); defs.append('marker') .attr('id', `circle`) .attr('viewBox', [0, 0, markerBoxWidth, markerBoxHeight]) .attr('refX', refX) .attr('refY', refY) .attr('markerWidth', markerBoxWidth) .attr('markerHeight', markerBoxHeight) .attr('orient', 'auto-start-reverse') .append('path') .attr('d', d3.line()([[0,0],[0,markerBoxHeight],[markerBoxWidth,markerBoxHeight/2]])) .attr('stroke', 'blue').attr('fill', 'blue'); defs.append('marker') .attr('id', `arrow`) .attr('viewBox', [0, 0, markerBoxWidth, markerBoxHeight]) .attr('refX', refX) .attr('refY', refY) .attr('markerWidth', markerBoxWidth) .attr('markerHeight', markerBoxHeight) .attr('orient', 'auto-start-reverse') .append('path') .attr('d', d3.line()([[0,0],[0,markerBoxHeight],[markerBoxWidth,markerBoxHeight/2]])) .attr('stroke', 'black').attr('fill', 'black'); ccodes.forEach((color,ci)=>{ defs.append('marker') .attr('id', `arrow${ci}`) .attr('viewBox', [0, 0, markerBoxWidth, markerBoxHeight]) .attr('refX', refX) .attr('refY', refY) .attr('markerWidth', markerBoxWidth) .attr('markerHeight', markerBoxHeight) .attr('orient', 'auto-start-reverse') .append('path') .attr('d', d3.line()([[0,0],[0,markerBoxHeight],[markerBoxWidth,markerBoxHeight/2]])) .attr('stroke', color).attr('fill', color); }); //svg.append("path").attr("marker-end","url(#arrow)").attr("fill","none").attr("stroke","black").attr("d",`M 0 5 h ${width}`); return svg.node(); }36:T41f,mktshr_chart = { const mktshr = mktshr_data.map((d,i)=>({cd:d[0],cn:d[1],pct:d[2]*100,r:Math.floor(d[2]*100)*4})); mktshr.forEach((d,i)=>{ mktshr[i].offset=(i>0?(mktshr[i-1].offset+2*mktshr[i-1].r)+5:0) }); const _height = d3.max(mktshr,d=>d.r)*2; const svg = d3.create("svg").attr("width",500).attr("height",_height); svg.append("g").selectAll("circle") .data(mktshr) .join("circle") .attr("cx",(d,i)=>d.offset+d.r).attr("cy",_=>_height/2).attr("r",d=>d.r) .attr("fill",d=>d.cd=="005930"||d.cd=="005935"?"#11228C":"none").attr("stroke","grey"); svg.append("g").selectAll("text") .data(mktshr) .join("text") .attr("transform",(d,i)=>`translate(${i==0?d.offset+d.r:d.offset+(1+4/5)*d.r},${i==0?_height/2:_height/2-d.r*4/5}) rotate(${i==0?0:-45})`) .attr("text-anchor",(_,i)=>i==0?"middle":"start").attr("dominant-baseline","middle").attr("font-size",d=>Math.max(11,d.r/3.5)) .attr("fill",(_,i)=>i==0?"white":"black") .text(d=>`${d.cd}(${d.pct.toFixed(1)}%)`); return svg.node(); }37:Tee9,single_sample_shap_values = { const ps = {w:400,h:300+90}; const svg = d3.create("svg").attr("width",ps.w).attr("height",ps.h); const samples = raw_data.samples; const rng = d3.randomInt(samples.length); const nr = ps.w/3; const g = svg.append("g"); function resample(){ g.selectAll("*").remove(); const sample = samples[rng()]; const bias = sample.slice(-1)[0]; const ordered = sample.slice(0,-1).map((v,fi)=>[v,fi]).toSorted((a,b)=>b[0]-a[0]).filter(v=>v[0]!=0); //console.log(ordered) const acc = ordered.reduce((acc,cur)=>{acc.push(acc[acc.length-1]+cur[0]); return acc;},[bias]); const xlines = d3.range(acc.length-1).map((_,i)=>({x1:acc[i],x2:acc[i+1]})); const x = d3.scaleLinear(d3.extent(acc),[10,ps.w-20]); const y = d3.scaleBand(d3.range(xlines.length),[40,ps.h-60]) g.selectAll("path") .data(xlines) .join("path") .attr("marker-end",d=>d.x2>d.x1?"url(#arrowinc)":d.x2d.x2>d.x1?"red":d.x2`M ${x(d.x1)} ${y(i)+y.bandwidth()/2} H ${x(d.x2)}`); g.selectAll("circle") .data(xlines) .join("circle") .attr("visibility",d=>d.x1==d.x2?"visible":"hidden") .attr("cx",d=>x(d.x2)).attr("cy",(d,i)=>y(i)+y.bandwidth()/2).attr("r",3).attr("fill","grey"); g.selectAll("text.textvalue") .data(xlines) .join("text") .attr("font-size",10).attr("class","textvalue") .attr("text-anchor","middle").attr("dominant-baseline","hanging") .attr("x",d=>x((d.x1+d.x2)/2)).attr("y",(d,i)=>y(i)+y.bandwidth()/2) .text((d,i)=>`${ordered[i][0]>0?"+":""}${ordered[i][0].toFixed(3)}`); g.selectAll("text.textfield") .data(xlines) .join("text") .attr("font-size",10).attr("class","textfield") .attr("text-anchor","middle").attr("dominant-baseline","auto") .attr("x",d=>x((d.x1+d.x2)/2)).attr("y",(d,i)=>y(i)+y.bandwidth()/2) .text((d,i)=>`${fields[ordered[i][1]][1]}`); const iv_right = x(acc[0]) 예측모델로 보는 ${html_005930()}의 주요특성과 투자전략","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":130,"value":"---\n\n## 예측모델이란?\n\n

\n* 특성(경제지표,재무정보,...)을 통해 1년후 수익률을 예측하는 모델\n* 이전 [개미투자공학 2장 확장판](https://observablehq.com/@humble-data-miner/mu-sigma-prediction-model-by-xgboost)에서 간단한 예측모델을 소개\n* 예측모델=(특성으로 표현해본)현실의 축소판\n * 입력(36)\n * 경제지표(11): 현물(유가,금값),금리(콜,CD,국채,회사채),환율(USD/EUR)\n * 공시정보(2): 보고서순번, 보고후거래일\n * 재무정보(18): (연결C,개별I)×(분기Q,연간Y)×(iPBR,iPLR,iPTR,iPSR,iPER,iPCR)\n * 접두사 i는 inverse를 의미, e.g. PBR=시총/자산, iPBR=자산/시총\n * iPCR=현금흐름/시총, iPLR=부채/시총, iPTR=잉여이익금/시총\n * 가격정보(5): 시가총액, 합산기간(주간W,월간M,분기Q,반기H)\n * 출력\n * 1년후 로그수익률(1)\n

\n ${htl.html.fragment`

${svg_model}

`}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":204,"value":"---\n## ${html_005930()}의 시장 지배력\n\n

\n* ${html_005930()}의 국내주식시장 시총비 = 18.5%\n * 해당기업의 우선주인 005935 포함시 20.5%\n* ${html_005930()}의 가격변동 ≈ KOSPI지수변동의 1/5\n * KOSPI지수의 변동은 결국 ${html_005930()}의 가격변동에 크게 의존\n* 투자주체(개인,기관,외국인,...)와 상관없이 활발하고 큰 액수의 거래\n * 국장에서 가장 완전경쟁시장에 가까운 종목 → 실적이 수익률에 중요요소?\n

\n${htl.html.fragment`

${mktshr_chart}

`}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":244,"value":"---\n## 예측모델을 이용한 특성의 중요도,연관관계 분석\n\n

\n\n\n

\n \n${md`\n * 각 데이터포인트마다 특성별 출력에 대한 기여도를 측정할수 있다면?\n * SHAP value: 한 데이터포인트에서 한 특성이 출력에 기여한 정도\n * 오른쪽 예시를 참고, 최종출력값=(특성별 출력기여도의 누적합)\n \n * 모든 데이터포인트에 대해서 특성별 SHAP value를 종합하면?\n * 주요한 특성과 무관한 특성을 구분 가능\n * 특성값과 출력과의 연관 관계를 유추 가능\n`}\n

\n\n${htl.html.fragment`

${single_sample_shap_values}

`}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":70,"value":"---\n## ${html_005930()}의 수익률 모델\n* 매수기간(입력): ${extent.date.train[0]}~${extent.date.train[1]}\n* 매도기간(출력): ${extent.date.result[0]}~${extent.date.result[1]}\n* 수익률 계산 기준이 다른 **2개의 모델**\n 1. (상대수익률) KOSPI 지수의 변동만큼 매수가격을 보정후 수익률 측정\n 2. (절대수익률) 명목가격을 기준으로 수익률 측정\n\n## ${html_005930()}의 SHAP value 분석 결과\n * (공통) SHAP value에 영향력이 큰 순서대로 10개의 특성을 위에서부터 표시\n * (왼쪽) 특성별 SHAP value의 분포를 표시\n * 원의 크기: 비슷한 특성값과 SHAP value를 가진 데이터들의 군집 크기\n * 원의 색깔: 높은 특성값은 빨강색, 낮은 특성값은 파란색\n * (오른쪽) 특성별 SHAP value의 퍼지는 정도(절대값의 평균)를 표시","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":91,"value":"viewof sel_seq = Inputs.radio(series, {label:\"수익률 기준\",format:d=>d.label, value:series[0]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":299,"value":"## 결과 해석\n* 절대수익률 **\"전체적인 시장의 흐름을 포함\"**\n * 주요특성: \\[개별,분기\\]자본/시총, \\[개별,분기\\]잉여/시총, \\[연결,분기\\]자본/시총, 원유가격\n * 분기별 자본관련 지표(분기별 PBR,PTR)와 강한 상관관계\n * 일반론: 대표기업의 곳간이 가격대비 풍족할때 국장도 함께 성장\n * 직전 1분기 반영: 최근의 실적이 가장 유효한 정보\n * 유가와 수익률의 반비례 관계\n * 일반론: 원자재 가격에 민감한 산업구조\n* KOSPI 지수대비 상대수익률 **\"시장보다 잘하거나 못하거나\"**\n * 주요특성: \\[개별,연간\\]매출/시총\n * 연간 매출(연간 PSR)이 차이를 만든다?\n * 특수론: 시장의 흐름과 무관하게 매출이 투자의 차별화 포인트\n * 직전 1년 반영: 1년간 정기보고서를 종합할때 다른 기업과의 비교가 유의\n\n## 제안드리는 ${html_005930()} 투자전략\n* 지난 5년의 결과를 요약하면\n * 기본적으로 분기별 자본/시총 대비 투자금액 주 조정 → 시장의 흐름을 추종\n * 부가적으로 연간 매출/시총 대비 투자금액 미세 조정 → 시장을 이기기 위함","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":335,"value":"---\n## 참고자료\n\n* Shapley value 이론 설명: [Wikipedia - Shapley value](https://en.wikipedia.org/wiki/Shapley_value)\n* python SHAP 라이브러리, SHAP value 시각화에 참고: [SHAP(SHapley Additive exPlanations) Documentation](https://shap.readthedocs.io/en/latest/index.html)\n* 트리모델 학습과 SHAP value 계산에 사용한 라이브러리: [XGBoost Documentation](https://xgboost.readthedocs.io/en/stable/)\n* 트리모델 학습과정의 소개, 특히 단일모델편을 참고: [개미투자공학 2장 확장판](https://observablehq.com/@humble-data-miner/mu-sigma-prediction-model-by-xgboost)\n* 로그수익률 참고: [개미투자공학 6장](https://observablehq.com/@humble-data-miner/personal-investment-engineering-ch-6?collection=@humble-data-miner/personal-investment-engineering)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":368,"value":"---\n## 부연설명 동영상\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":6,"value":"---\n## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":12,"value":"raw_data = FileAttachment(\"KRX_005930_TreeSHAP_2018_1_2023_1@2.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":146,"value":"mktshr_data = {\n return [\n ['005930', '삼성전자', 0.1851],\n ['000660', 'SK하이닉스', 0.0544],\n ['373220', 'LG에너지솔루션', 0.0294],\n ['207940', '삼성바이오로직스', 0.0255],\n ['005380', '현대차', 0.0204],\n ['005935', '삼성전자우', 0.0200],\n ['000270', '기아', 0.0174],\n ['068270', '셀트리온', 0.0173],\n ['105560', 'KB금융', 0.0140],\n ['055550', '신한지주', 0.0118],\n ['005490', 'POSCO홀딩스', 0.0117],\n ['035420', 'NAVER', 0.0109],\n ['028260', '삼성물산', 0.0103]\n ]\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"series = {\n const beeswarm = beeswarmForce()\n .x(d => axis_x1(d.m[0]))\n .y(axis_y.bandwidth()/2)\n .r(d=>Math.max(1,Math.sqrt(d.c)/2));\n\n const normalized_clip = (v,min,max) => Math.min(1,Math.max(0,(v-min)/(max-min)));\n const series = [raw_data.series.kospi,raw_data.series.abs];\n series.forEach((seq,si)=>{\n seq.stat.forEach((stat,fi)=>{\n stat.clusts.forEach((clust,ci)=>{\n const fext = extent.feats[stat.fi];\n clust.fnorm = normalized_clip(clust.m[1],fext[0],fext[1]);\n });\n stat.swarm = beeswarm(stat.clusts);\n });\n seq.label = si==0?\"KOSPI 지수대비 상대수익률\":\"절대수익률\";\n });\n \n return series;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":28,"value":"extent = raw_data.extent","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":42,"value":"fnamemap = {\n const entries = [\n [\"groupcode\",\"종목그룹\"],\n [\"market\",\"거래소\"],\n [\"totalamount\",\"시가총액\"],\n [\"seq\",\"보고서순번\"],\n [\"offset\",\"보고후거래일\"],\n [\"FX_USDKRW\",\"환율(KRW/USD)\"],\n [\"FX_EURKRW\",\"환율(KRW/EUR)\"],\n [\"KRCALLBOKK\",\"콜금리\"],\n [\"KFIA114000\",\"CD금리\"],\n [\"KFIA103009\",\"회사채(3년/AA-)\"],\n [\"KR3YT_RR\",\"한국국채(3년)\"],\n [\"KR10YT_RR\",\"한국국채(10년)\"],\n [\"US3YT_RR\",\"미국국채(3년)\"],\n [\"US10YT_RR\",\"미국국채(10년)\"],\n [\"CLcv1\",\"원유WTI(USD)\"],\n [\"GCcv1\",\"금(USD/OZS)\"],\n [\"w0\",\"[직전]주간변동\"],\n [\"m0\",\"[직전]월간변동\"],\n [\"q0\",\"[직전]분기변동\"],\n [\"h0\",\"[직전]반기변동\"],\n ];\n entries.push(...\n [[\"c\",\"C\"],[\"\",\"I\"]].map(\n cons=>[[\"y\",\"Y\"],[\"q\",\"Q\"]].map(\n period=>[[\"b\",\"자본/시총\"],[\"l\",\"부채/시총\"],[\"t\",\"잉여/시총\"],[\"s\",\"매출/시총\"],[\"e\",\"순익/시총\"],[\"c\",\"현금/시총\"]].map(\n ratio=> [`${cons[0]}p${period[0]}${ratio[0]}r0`,`[${cons[1]},${period[1]}]${ratio[1]}`]\n ))).flat(2));\n return Object.fromEntries(entries);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":46,"value":"ccodemap = {\n const entries = [\n [\"totalamount\",3],\n [\"seq\",1],\n [\"offset\",1],\n [\"FX_USDKRW\",0],\n [\"FX_EURKRW\",0],\n [\"KRCALLBOKK\",0],\n [\"KFIA114000\",0],\n [\"KFIA103009\",0],\n [\"KR3YT_RR\",0],\n [\"KR10YT_RR\",0],\n [\"US3YT_RR\",0],\n [\"US10YT_RR\",0],\n [\"CLcv1\",0],\n [\"GCcv1\",0],\n [\"w0\",3],\n [\"m0\",3],\n [\"q0\",3],\n [\"h0\",3],\n ];\n entries.push(...\n [[\"c\",\"C\"],[\"\",\"I\"]].map(\n cons=>[[\"y\",\"Y\"],[\"q\",\"Q\"]].map(\n period=>[[\"b\",\"자본/시총\"],[\"l\",\"부채/시총\"],[\"t\",\"잉여/시총\"],[\"s\",\"매출/시총\"],[\"e\",\"순익/시총\"],[\"c\",\"현금/시총\"]].map(\n ratio=> [`${cons[0]}p${period[0]}${ratio[0]}r0`,2]\n ))).flat(2));\n return Object.fromEntries(entries);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"ccodes = [d3.schemeCategory10[4],d3.schemeCategory10[5],d3.schemeCategory10[9],d3.schemeCategory10[8]]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":52,"value":"fields = {\n return raw_data.fields.map(f=>[f,fnamemap[f],ccodes[ccodemap[f]]]);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":38,"value":"height = 640","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":32,"value":"plotpad = ({outer:10,inner:80})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":36,"value":"plotdim = ({width:(width-margin.left-margin.right-plotpad.inner)/2, height:height-margin.top-margin.bottom})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":30,"value":"margin = ({left:plotpad.outer,right:plotpad.outer,top:80,bottom:0})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":62,"value":"max_displayitems = 10","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"axis_x1 = d3.scaleLinear(extent.shap,[margin.left,margin.left+plotdim.width]);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"axis_y = d3.scaleBand(d3.range(fields.length),[margin.top, margin.top+(height-margin.top-margin.bottom)*fields.length/max_displayitems]).paddingInner(0.2).paddingOuter(0.3);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":67,"value":"anim_dur = 1000","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":140,"value":"html_005930 = function(){\n return html`005930`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"import { beeswarmForce } from \"@harrystevens/force-directed-beeswarm\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":106,"value":"{\n chart.update(sel_seq);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":115,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":113,"value":"svg_model = svg`\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":150,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":256,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W38","stdlib":"1","titling":"auto"}} 33:"$32:body"