skills/fund-slides/references/charts-matrix.md
wangyitong a65adcc2e5 Initial commit: merged, deduplicated, and vetted skill collection
Sources: extracted from two upstream archives (skill-repo, skills-main),
merged with the following policy:

- 15 broken symlinks (pointing to /Users/jameslee/.cc-switch/skills or
  ../../.agents/skills on a foreign machine) discarded
- 3 real name collisions with identical content (ai-pair, ifind-http-api,
  zhipu-websearch) kept as one copy
- Functional overlaps deduped keeping the strongest variant:
  - docx family: kept docx (official, full toolchain) + docx-cn
    (GB/T 9704 Chinese official-document constants),
    dropped docx_writer (no scripts, name collided with docx)
  - humanizer family: kept humanizer-zh (6 zh reference docs),
    dropped humanizer (en, redundant for CN workflow)
- Skills that only ran in a foreign environment removed:
  ablemind-ops, app-publish, hlb-design-system, openclaw-adj-skill,
  claude-driver
- alphapai excluded from this public repo because its SKILL.md hard-coded
  live credentials

Result: 25 skills, 572 files, ~7.5 MB.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-07-13 14:47:12 +08:00

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# Charts -- Matrix (矩阵与分布类)
热力图、相关性矩阵、雷达图、散点图。基础设施见 [charts-base.md](charts-base.md)。
---
## 热力图NxM 矩阵数据)
两个维度交叉的数值强度,颜色深浅表示大小。
```javascript
const heatmapOption = {
tooltip: { position: 'top', formatter: p => p.data[1] + ' x ' + p.data[0] + ': ' + p.data[2] },
grid: { left: '12%', right: '8%', top: '8%', bottom: '15%' },
xAxis: {
type: 'category',
data: ['Q1', 'Q2', 'Q3', 'Q4'],
splitArea: { show: true },
axisLabel: { fontSize: 11 }
},
yAxis: {
type: 'category',
data: ['游戏', '社交', '广告', '金融科技', '云服务'],
splitArea: { show: true },
axisLabel: { fontSize: 11 }
},
visualMap: {
min: 0, max: 600,
calculable: true,
orient: 'horizontal',
left: 'center', bottom: '0%',
inRange: { color: ['#f0f4ff', '#93c5fd', '#2563eb', '#1a365d'] },
textStyle: { fontSize: 10 }
},
series: [{
type: 'heatmap',
data: [
['Q1', '游戏', 480], ['Q2', '游戏', 520], ['Q3', '游戏', 460], ['Q4', '游戏', 580],
['Q1', '社交', 290], ['Q2', '社交', 310], ['Q3', '社交', 300], ['Q4', '社交', 320],
['Q1', '广告', 250], ['Q2', '广告', 340], ['Q3', '广告', 310], ['Q4', '广告', 380],
['Q1', '金融科技', 490], ['Q2', '金融科技', 530], ['Q3', '金融科技', 510], ['Q4', '金融科技', 560],
['Q1', '云服务', 130], ['Q2', '云服务', 150], ['Q3', '云服务', 155], ['Q4', '云服务', 170]
],
label: { show: true, fontSize: 10, color: '#333', formatter: p => p.data[2] },
emphasis: { itemStyle: { shadowBlur: 10, shadowColor: 'rgba(0,0,0,0.3)' } }
}]
};
```
---
## 相关性热力图(方阵)
对称矩阵,-1 到 +1 色阶。适用于多资产/多因子相关系数。
```javascript
const labels = ['贵州茅台', '五粮液', '泸州老窖', '山西汾酒', '洋河股份'];
const corrData = [
[0,0,1.0],[1,0,0.85],[2,0,0.82],[3,0,0.71],[4,0,0.68],
[0,1,0.85],[1,1,1.0],[2,1,0.91],[3,1,0.78],[4,1,0.74],
[0,2,0.82],[1,2,0.91],[2,2,1.0],[3,2,0.80],[4,2,0.72],
[0,3,0.71],[1,3,0.78],[2,3,0.80],[3,3,1.0],[4,3,0.65],
[0,4,0.68],[1,4,0.74],[2,4,0.72],[3,4,0.65],[4,4,1.0]
];
const corrMatrixOption = {
tooltip: { formatter: p => labels[p.data[0]] + ' vs ' + labels[p.data[1]] + ': ' + p.data[2].toFixed(2) },
grid: { left: '15%', right: '12%', top: '15%', bottom: '5%' },
xAxis: { type: 'category', data: labels, axisLabel: { rotate: 30, fontSize: 10 } },
yAxis: { type: 'category', data: labels, axisLabel: { fontSize: 10 } },
visualMap: {
min: -1, max: 1,
inRange: { color: ['#dc2626', '#fef2f2', '#ffffff', '#eff6ff', '#2563eb'] },
orient: 'horizontal', left: 'center', bottom: '0%',
text: ['+1.0', '-1.0']
},
series: [{
type: 'heatmap',
data: corrData,
label: { show: true, fontSize: 10, formatter: p => p.data[2].toFixed(2) }
}]
};
```
---
## 雷达图(多维评分对比)
多个维度的综合评分。2 个主体对比最佳。
```javascript
const radarOption = {
tooltip: {},
legend: { top: '2%' },
radar: {
indicator: [
{ name: '网络效应', max: 10 },
{ name: '转换成本', max: 10 },
{ name: '品牌价值', max: 10 },
{ name: '规模经济', max: 10 },
{ name: '无形资产', max: 10 },
{ name: '成本优势', max: 10 }
],
radius: '65%',
axisName: { fontSize: 11, fontFamily: 'Noto Sans SC' },
splitArea: { areaStyle: { color: ['rgba(37,99,235,0.02)', 'rgba(37,99,235,0.05)'] } }
},
series: [{
type: 'radar',
data: [
{ name: '腾讯', value: [9.5, 9.0, 7.0, 8.0, 8.5, 5.0], areaStyle: { opacity: 0.25 }, lineStyle: { width: 2 } },
{ name: '阿里巴巴', value: [7.0, 7.5, 6.5, 8.5, 6.0, 6.0], areaStyle: { opacity: 0.15 }, lineStyle: { width: 2 } }
]
}]
};
```
---
## 散点图 / 气泡图
两变量相关性,气泡大小编码第三维度。
```javascript
const scatterOption = {
tooltip: {
formatter: p => p.data[3] + '<br/>PE: ' + p.data[0] + 'x<br/>ROE: ' + p.data[1] + '%<br/>市值: ' + formatCN(p.data[2])
},
grid: { left: '10%', right: '8%', top: '8%', bottom: '12%' },
xAxis: { name: 'PE (TTM)', nameLocation: 'center', nameGap: 30, scale: true },
yAxis: { name: 'ROE (%)', nameLocation: 'center', nameGap: 35, scale: true },
series: [{
type: 'scatter',
symbolSize: p => Math.sqrt(p[2]) / 800,
data: [
[22.8, 22.5, 53000e8, '腾讯'],
[16.2, 12.8, 22000e8, '阿里巴巴'],
[28.5, 35.2, 16000e8, 'Meta'],
[35.0, 42.0, 33000e8, '微软'],
[25.0, 30.0, 25000e8, '字节(估)']
],
label: { show: true, formatter: p => p.data[3], position: 'top', fontSize: 10 },
emphasis: { itemStyle: { shadowBlur: 10, shadowColor: 'rgba(0,0,0,0.3)' } }
}]
};
```