一、需求问题
在开发项目中，我们可能会遇到这样的需求。在vue中使用Echarts绘制K线图，进行数据分析。下面是一个简单的K线图绘制，数据是伪造的，虽然不多，但是能够实现大致的K线图效果。
二、需求分析
对于使用Echarts绘制K线图，首先我们需要下载Echarts，可以通过 npm install echarts --save命令下载。在这个文件中引入echarts，创建echarts的挂载实例，在mounted中初始化echarts实例，进行相关的K线图绘制就可以了。
三、需求实现
完整的代码实例如下所示：
<template>
<div>
<h1>Echarts绘制k线图</h1>
<div id="echartContainer" ref="echartContainer" style="width:100%; height:400px"></div>
</div>
</template>
<script>
var echarts = require("echarts");
export default {
data() {
return {
//数据模型 time0 open1 close2 min3 max4 vol5 tag6 macd7 dif8 dea9
//['2019-10-18',18.56,18.25,18.19,18.56,55.00,0,-0.00,0.08,0.09]
data: [
["2019-3-16", 18.4, 18.58, 18.33, 18.79, 67.0, 1, 0.04, 0.11, 0.09],
["2019-3-19", 18.56, 18.25, 18.19, 18.56, 55.0, 0, -0.0, 0.08, 0.09],
["2019-3-20", 18.3, 18.22, 18.05, 18.41, 37.0, 0, 0.01, 0.09, 0.09],
["2019-3-21", 18.18, 18.69, 18.02, 18.98, 89.0, 0, 0.03, 0.1, 0.08],
["2019-3-22", 18.42, 18.29, 18.22, 18.48, 43.0, 0, -0.06, 0.05, 0.08],
["2019-3-23", 18.26, 18.19, 18.08, 18.36, 46.0, 0, -0.1, 0.03, 0.09],
["2019-3-26", 18.33, 18.07, 17.98, 18.35, 65.0, 0, -0.15, 0.03, 0.1],
["2019-3-27", 18.08, 18.04, 17.88, 18.13, 37.0, 0, -0.19, 0.03, 0.12],
["2019-3-28", 17.96, 17.86, 17.82, 17.99, 35.0, 0, -0.24, 0.03, 0.15],
["2019-3-29", 17.85, 17.81, 17.8, 17.93, 27.0, 0, -0.24, 0.06, 0.18],
["2019-3-30", 17.79, 17.93, 17.78, 18.08, 43.0, 0, -0.22, 0.11, 0.22],
["2019-4-02", 17.78, 17.83, 17.78, 18.04, 27.0, 0, -0.2, 0.15, 0.25],
["2019-4-03", 17.84, 17.9, 17.84, 18.06, 34.0, 0, -0.12, 0.22, 0.28],
["2019-4-04", 17.97, 18.36, 17.85, 18.39, 62.0, 0, -0.0, 0.3, 0.3],
["2019-4-05", 18.3, 18.57, 18.18, 19.08, 177.0, 0, 0.07, 0.33, 0.3],
["2019-4-06", 18.53, 18.68, 18.3, 18.71, 95.0, 0, 0.12, 0.35, 0.29],
["2019-4-09", 18.75, 19.08, 18.75, 19.98, 202.0, 1, 0.16, 0.35, 0.27],
["2019-4-10", 18.85, 18.64, 18.56, 18.99, 85.0, 0, 0.09, 0.29, 0.25],
["2019-4-11", 18.64, 18.44, 18.31, 18.64, 50.0, 0, 0.06, 0.27, 0.23],
["2019-4-12", 18.55, 18.27, 18.17, 18.57, 43.0, 0, 0.05, 0.25, 0.23],
["2019-4-13", 18.13, 18.14, 18.09, 18.34, 35.0, 0, 0.05, 0.24, 0.22],
["2019-4-16", 18.01, 18.1, 17.93, 18.17, 34.0, 0, 0.07, 0.25, 0.21],
["2019-4-17", 18.2, 18.14, 18.08, 18.45, 58.0, 0, 0.11, 0.25, 0.2],
["2019-4-18", 18.23, 18.16, 18.0, 18.45, 47.0, 0, 0.13, 0.25, 0.19],
["2019-4-19", 18.08, 18.2, 18.05, 18.25, 32.0, 0, 0.15, 0.24, 0.17],
["2019-4-20", 18.15, 18.15, 18.11, 18.29, 36.0, 0, 0.13, 0.21, 0.15],
["2019-4-23", 18.16, 18.19, 18.12, 18.34, 47.0, 0, 0.11, 0.18, 0.13],
["2019-4-24", 18.23, 17.88, 17.7, 18.23, 62.0, 0, 0.03, 0.13, 0.11],
["2019-4-25", 17.85, 17.73, 17.56, 17.85, 66.0, 0, -0.03, 0.09, 0.11],
["2019-4-26", 17.79, 17.53, 17.5, 17.92, 63.0, 0, -0.1, 0.06, 0.11],
["2019-4-27", 17.51, 17.04, 16.9, 17.51, 67.0, 0, -0.16, 0.05, 0.13],
["2019-4-30", 17.07, 17.2, 16.98, 17.32, 55.0, 0, -0.12, 0.09, 0.15],
["2019-5-01", 17.28, 17.11, 16.91, 17.28, 39.0, 0, -0.09, 0.12, 0.16],
["2019-5-02", 17.13, 17.91, 17.05, 17.99, 102.0, 0, -0.01, 0.17, 0.18],
["2019-5-03", 17.8, 17.78, 17.61, 17.98, 71.0, 0, -0.09, 0.14, 0.18],
["2019-5-04", 17.6, 17.25, 17.13, 17.69, 51.0, 0, -0.18, 0.1, 0.19],
["2019-5-07", 17.2, 17.39, 17.15, 17.45, 43.0, 0, -0.19, 0.12, 0.22],
["2019-5-08", 17.3, 17.42, 17.18, 17.62, 45.0, 0, -0.23, 0.13, 0.24],
["2019-5-09", 17.33, 17.39, 17.32, 17.59, 44.0, 0, -0.29, 0.13, 0.28],
["2019-5-10", 17.39, 17.26, 17.21, 17.65, 44.0, 0, -0.37, 0.13, 0.32],
["2019-5-11", 17.23, 16.92, 16.66, 17.26, 114.0, 1, -0.44, 0.15, 0.37],
["2019-5-14", 16.75, 17.06, 16.5, 17.09, 94.0, 0, -0.44, 0.21, 0.44],
["2019-5-15", 17.03, 17.03, 16.9, 17.06, 46.0, 0, -0.44, 0.28, 0.5],
["2019-5-16", 17.08, 16.96, 16.87, 17.09, 30.0, 0, -0.4, 0.36, 0.56],
["2019-5-17", 17.0, 17.1, 16.95, 17.12, 50.0, 0, -0.3, 0.47, 0.62],
["2019-5-18", 17.09, 17.52, 17.04, 18.06, 156.0, 0, -0.14, 0.59, 0.66],
["2019-5-21", 17.43, 18.23, 17.35, 18.45, 152.0, 1, 0.02, 0.69, 0.68],
["2019-5-22", 18.14, 18.27, 18.06, 18.32, 94.0, 0, 0.08, 0.72, 0.68],
["2019-5-23", 18.28, 18.19, 18.17, 18.71, 108.0, 0, 0.13, 0.73, 0.67],
["2019-5-24", 18.18, 18.14, 18.01, 18.31, 37.0, 0, 0.19, 0.74, 0.65],
["2019-5-25", 18.22, 18.33, 18.2, 18.36, 48.0, 0, 0.26, 0.75, 0.62],
["2019-5-28", 18.35, 17.84, 17.8, 18.39, 48.0, 0, 0.27, 0.72, 0.59],
["2019-5-29", 17.83, 17.94, 17.71, 17.97, 36.0, 0, 0.36, 0.73, 0.55],
["2019-5-30", 17.9, 18.26, 17.55, 18.3, 71.0, 1, 0.43, 0.71, 0.5],
["2019-5-31", 18.12, 17.99, 17.91, 18.33, 72.0, 0, 0.4, 0.63, 0.43],
["2019-6-04", 17.91, 17.28, 17.16, 17.95, 37.0, 1, 0.34, 0.55, 0.38],
["2019-6-05", 17.17, 17.23, 17.0, 17.55, 51.0, 0, 0.37, 0.51, 0.33],
["2019-6-06", 17.2, 17.31, 17.06, 17.33, 31.0, 0, 0.37, 0.46, 0.28],
["2019-6-07", 17.15, 16.67, 16.51, 17.15, 19.0, 0, 0.3, 0.37, 0.22],
["2019-6-08", 16.8, 16.81, 16.61, 17.06, 60.0, 0, 0.29, 0.32, 0.18],
["2019-6-11", 16.68, 16.04, 16.0, 16.68, 65.0, 0, 0.2, 0.24, 0.14],
["2019-6-12", 16.03, 15.98, 15.88, 16.25, 46.0, 0, 0.2, 0.21, 0.11],
["2019-6-13", 16.21, 15.87, 15.78, 16.21, 57.0, 0, 0.2, 0.18, 0.08],
["2019-6-14", 15.55, 15.89, 15.52, 15.96, 42.0, 0, 0.2, 0.16, 0.05],
["2019-6-15", 15.87, 15.48, 15.45, 15.92, 34.0, 1, 0.17, 0.11, 0.02],
["2019-6-18", 15.39, 15.42, 15.36, 15.7, 26.0, 0, 0.21, 0.1, -0.0],
["2019-6-19", 15.58, 15.71, 15.35, 15.77, 38.0, 0, 0.25, 0.09, -0.03],
["2019-6-20", 15.56, 15.52, 15.24, 15.68, 38.0, 0, 0.23, 0.05, -0.07],
["2019-6-21", 15.41, 15.3, 15.28, 15.68, 35.0, 0, 0.21, 0.0, -0.1],
["2019-6-22", 15.48, 15.28, 15.13, 15.49, 30.0, 0, 0.21, -0.02, -0.13],
["2019-6-25", 15.29, 15.48, 15.2, 15.49, 21.0, 0, 0.2, -0.06, -0.16],
["2019-6-26", 15.33, 14.86, 14.78, 15.39, 30.0, 0, 0.12, -0.13, -0.19],
["2019-6-27", 14.96, 15.0, 14.84, 15.22, 51.0, 0, 0.13, -0.14, -0.2],
["2019-6-28", 14.96, 14.72, 14.62, 15.06, 25.0, 0, 0.1, -0.17, -0.22],
["2019-6-29", 14.75, 14.99, 14.62, 15.08, 36.0, 0, 0.13, -0.17, -0.24],
["2019-7-01", 14.98, 14.72, 14.48, 15.18, 27.0, 0, 0.1, -0.21, -0.26],
["2019-7-02", 14.65, 14.85, 14.65, 14.95, 18.0, 0, 0.11, -0.21, -0.27],
["2019-7-03", 14.72, 14.67, 14.55, 14.8, 23.0, 0, 0.1, -0.24, -0.29],
["2019-7-04", 14.79, 14.88, 14.69, 14.93, 22.0, 0, 0.13, -0.24, -0.3],
["2019-7-05", 14.9, 14.86, 14.78, 14.93, 16.0, 0, 0.12, -0.26, -0.32],
["2019-7-15", 14.5, 14.66, 14.47, 14.82, 19.0, 0, 0.11, -0.28, -0.34],
["2019-7-16", 14.77, 14.94, 14.72, 15.05, 26.0, 0, 0.14, -0.28, -0.35],
["2019-7-17", 14.95, 15.03, 14.88, 15.07, 38.0, 0, 0.12, -0.31, -0.37],
["2019-7-18", 14.95, 14.9, 14.87, 15.06, 28.0, 0, 0.07, -0.35, -0.39],
["2019-7-19", 14.9, 14.75, 14.68, 14.94, 22.0, 0, 0.03, -0.38, -0.4],
["2019-7-22", 14.88, 15.01, 14.79, 15.11, 38.0, 1, 0.01, -0.4, -0.4],
["2019-7-23", 15.01, 14.83, 14.72, 15.01, 24.0, 0, -0.09, -0.45, -0.4]
]
};
},
mounted() {
// 这里实现的是一个比较简单的，可以按照需求将函数移动到methods函数中
var data0 = splitData(this.data);
// macd计算
function splitData(rawData) {
var categoryData = [];
var values = [];
var macds = [];
var difs = [];
var deas = [];
for (var i = 0; i < rawData.length; i++) {
categoryData.push(rawData[i].splice(0, 1)[0]);
values.push(rawData[i]);
macds.push(rawData[i][6]);
difs.push(rawData[i][7]);
deas.push(rawData[i][8]);
}
return {
categoryData: categoryData,
values: values,
macds: macds,
difs: difs,
deas: deas
};
}
// ma均线函数
function calculateMA(dayCount) {
var result = [];
for (var i = 0, len = data0.values.length; i < len; i++) {
if (i < dayCount) {
result.push("-");
continue;
}
var sum = 0;
for (var j = 0; j < dayCount; j++) {
sum += data0.values[i - j][1];
}
result.push(sum / dayCount);
}
return result;
}
// k线配置项
var option = {
tooltip: {
trigger: "axis",
axisPointer: {
type: "cross"
}
},
grid: [
{
left: "3%",
top: "0",
height: "75%"
},
{
left: "3%",
right: "10%",
top: "80%",
height: "10%"
}
],
xAxis: [
{
type: "category",
data: data0.categoryData,
scale: true,
boundaryGap: false,
axisLine: {
onZero: false,
lineStyle: {
color: "red"
}
},
splitLine: {
show: false
},
splitNumber: 20
},
{
type: "category",
gridIndex: 1,
data: data0.categoryData,
axisLabel: { show: false }
}
],
yAxis: [
{
scale: true,
splitArea: {
show: true
},
axisLine: {
lineStyle: {
color: "red"
}
},
position: "right"
},
{
gridIndex: 1,
splitNumber: 3,
axisLine: { onZero: false },
axisTick: { show: false },
splitLine: { show: false },
axisLabel: { show: true },
axisLine: {
lineStyle: {
color: "red"
}
},
position: "right"
}
],
dataZoom: [
{
type: "inside",
start: 100,
end: 80
},
{
show: true,
type: "slider",
y: "90%",
start: 50,
end: 100
},
{
show: false,
xAxisIndex: [0, 1],
type: "slider",
start: 20,
end: 100
}
],
series: [
{
name: "555",
type: "candlestick",
data: data0.values,
markPoint: {
data: [
{
name: "XX标点"
}
]
},
markLine: {
silent: true,
data: [
{
yAxis: 2222
}
]
}
},
{
name: "MA5",
type: "line",
data: calculateMA(5),
smooth: true,
lineStyle: {
normal: {
opacity: 0.5
}
}
},
{
name: "MA10",
type: "line",
data: calculateMA(10),
smooth: true,
lineStyle: {
normal: {
opacity: 0.5
}
}
},
{
name: "MA20",
type: "line",
data: calculateMA(20),
smooth: true,
lineStyle: {
normal: {
opacity: 0.5
}
}
},
{
name: "MA30",
type: "line",
data: calculateMA(30),
smooth: true,
lineStyle: {
normal: {
opacity: 0.5
}
}
},
{
name: "MACD",
type: "bar",
xAxisIndex: 1,
yAxisIndex: 1,
data: data0.macds,
itemStyle: {
normal: {
color: function(params) {
var colorList;
if (params.data >= 0) {
colorList = "#ef232a";
} else {
colorList = "#14b143";
}
return colorList;
}
}
}
},
{
name: "DIF",
type: "line",
xAxisIndex: 1,
yAxisIndex: 1,
data: data0.difs
},
{
name: "DEA",
type: "line",
xAxisIndex: 1,
yAxisIndex: 1,
data: data0.deas
}
]
};
// 进行初始化
var charts = echarts.init(this.$refs.echartContainer);
charts.setOption(option);
}
};
</script>
<style scoped>
</style>
