聊天命令

系统指标

/metrics                               显示当前指标
/metrics system                        CPU，内存，延迟
/metrics api                           API性能统计
/metrics ws                            WebSocket健康

交易指标

/metrics trades                        交易执行统计
/metrics fills                         填充率指标
/metrics latency                       订单延迟统计
/metrics errors                        错误率

自定义指标

/metrics track <name> <value>          跟踪自定义指标
/metrics query <name>                  查询指标历史
/metrics alert <name> > 100            设置指标警报

导出与报告

/metrics export csv                    导出到CSV
/metrics report daily                  生成日报告
/metrics dashboard                     打开指标仪表板

TypeScript API参考

创建指标服务

import { createMetricsService } from 'clodds/metrics';

const metrics = createMetricsService({
  // 收集
  collectInterval: 5000,  // ms
  retention: '30d',

  // 存储
  storage: 'sqlite',
  dbPath: './metrics.db',

  // 导出
  enablePrometheus: true,
  prometheusPort: 9090,
});

// 开始收集
await metrics.start();

系统指标

const system = await metrics.getSystemMetrics();

console.log('=== 系统健康 ===');
console.log(`CPU使用率: ${system.cpuUsage}%`);
console.log(`内存: ${system.memoryUsed}MB / ${system.memoryTotal}MB`);
console.log(`正常运行时间: ${system.uptimeHours}小时`);
console.log(`活动连接: ${system.activeConnections}`);
console.log(`事件循环延迟: ${system.eventLoopLag}ms`);

API指标

const api = await metrics.getApiMetrics();

console.log('=== API性能 ===');
console.log(`总请求数: ${api.totalRequests}`);
console.log(`请求/秒: ${api.requestsPerSecond}`);
console.log(`平均延迟: ${api.avgLatency}ms`);
console.log(`P50延迟: ${api.p50Latency}ms`);
console.log(`P95延迟: ${api.p95Latency}ms`);
console.log(`P99延迟: ${api.p99Latency}ms`);
console.log(`错误率: ${api.errorRate}%`);

console.log('
按端点：');
for (const endpoint of api.byEndpoint) {
  console.log(`  ${endpoint.path}: ${endpoint.avgLatency}ms (${endpoint.calls}次调用)`);
}

WebSocket指标

const ws = await metrics.getWebSocketMetrics();

console.log('=== WebSocket健康 ===');
console.log(`活动连接: ${ws.activeConnections}`);
console.log(`消息/秒: ${ws.messagesPerSecond}`);
console.log(`平均消息大小: ${ws.avgMessageSize}字节`);
console.log(`重新连接: ${ws.reconnections}`);
console.log(`丢失的消息: ${ws.droppedMessages}`);

console.log('
按Feed：');
for (const feed of ws.byFeed) {
  console.log(`  ${feed.name}: ${feed.messagesPerSecond}/s, ${feed.latency}ms延迟`);
}

交易执行指标

const trades = await metrics.getTradeMetrics();

console.log('=== 交易执行 ===');
console.log(`总订单数: ${trades.totalOrders}`);
console.log(`填充率: ${trades.fillRate}%`);
console.log(`部分填充: ${trades.partialFillRate}%`);
console.log(`拒绝率: ${trades.rejectionRate}%`);
console.log(`平均填充时间: ${trades.avgFillTime}ms`);
console.log(`平均滑点: ${trades.avgSlippage}%`);

console.log('
按平台：');
for (const platform of trades.byPlatform) {
  console.log(`  ${platform.name}:`);
  console.log(`    填充率: ${platform.fillRate}%`);
  console.log(`    平均延迟: ${platform.avgLatency}ms`);
}

延迟分解

const latency = await metrics.getLatencyBreakdown();

console.log('=== 延迟分解 ===');
console.log(`总订单延迟: ${latency.total}ms`);
console.log(`  信号处理: ${latency.signalProcessing}ms`);
console.log(`  订单构建: ${latency.orderConstruction}ms`);
console.log(`  网络往返: ${latency.networkRoundTrip}ms`);
console.log(`  交易所处理: ${latency.exchangeProcessing}ms`);
console.log(`  确认: ${latency.confirmation}ms`);

错误指标

const errors = await metrics.getErrorMetrics();

console.log('=== 错误率 ===');
console.log(`总错误数: ${errors.totalErrors}`);
console.log(`错误率: ${errors.errorRate}%`);
console.log(`每小时错误数: ${errors.errorsPerHour}`);

console.log('
按类型：');
for (const type of errors.byType) {
  console.log(`  ${type.name}: ${type.count} (${type.percentage}%)`);
}

console.log('
按平台：');
for (const platform of errors.byPlatform) {
  console.log(`  ${platform.name}: ${platform.errorRate}%`);
}

自定义指标

// 跟踪自定义指标
metrics.track('edge_detected', 1, {
  market: 'trump-2028',
  edgeSize: 0.05,
});

// 增加计数器
metrics.increment('trades_executed');

// 设置量表
metrics.gauge('active_positions', 5);

// 记录时间
const timer = metrics.startTimer('order_execution');
// ...执行订单...
timer.end();

// 直方图
metrics.histogram('slippage', 0.003, {
  platform: 'polymarket',
});

查询指标

const query = await metrics.query({
  metric: 'edge_detected',
  period: '7d',
  aggregation: 'sum',
  groupBy: 'market',
});

console.log('按市场检测边缘：');
for (const row of query.results) {
  console.log(`  ${row.market}: ${row.value}次检测`);
}

指标警报

// 设置警报阈值
metrics.setAlert({
  metric: 'error_rate',
  condition: '>',
  threshold: 5,  // > 5%错误率
  window: '5m',
  action: 'notify',
});

metrics.setAlert({
  metric: 'latency_p99',
  condition: '>',
  threshold: 1000,  // > 1000ms
  window: '1m',
  action: 'escalate',
});

// 警报处理程序
metrics.on('alert', (alert) => {
  console.log(`🚨 警报: ${alert.metric} ${alert.condition} ${alert.threshold}`);
  console.log(`  当前值: ${alert.currentValue}`);
});

导出指标

// 导出到CSV
await metrics.export({
  format: 'csv',
  metrics: ['api_latency', 'trade_fill_rate', 'error_rate'],
  period: '30d',
  outputPath: './metrics-export.csv',
});

// 导出到Prometheus
const prometheusFormat = metrics.toPrometheus();

// 导出到JSON
const jsonMetrics = await metrics.toJSON({
  period: '24h',
});

生成报告

const report = await metrics.generateReport({
  type: 'daily',
  include: ['summary', 'api', 'trades', 'errors'],
});

console.log('=== 日指标报告 ===');
console.log(`日期: ${report.date}`);
console.log(`
摘要:`);
console.log(`  正常运行时间: ${report.summary.uptime}%`);
console.log(`  总请求数: ${report.summary.totalRequests}`);
console.log(`  总交易数: ${report.summary.totalTrades}`);
console.log(`  错误率: ${report.summary.errorRate}%`);
console.log(`
亮点:`);
for (const highlight of report.highlights) {
  console.log(`  - ${highlight}`);
}

实时流

// 实时流指标
const stream = metrics.stream(['cpu', 'memory', 'latency']);

stream.on('data', (data) => {
  console.log(`CPU: ${data.cpu}%, 内存: ${data.memory}MB, 延迟: ${data.latency}ms`);
});

// 停止流
stream.stop();

指标类型

类型	描述	示例
计数器	单调递增	`trades_total`
量表	特定时间点的值	`active_positions`
直方图	分布	`latency_ms`
计时器	持续时间测量	`order_execution_time`

内置指标

类别	指标
系统	cpu_usage, memory_used, uptime, connections
API	request_count, latency_p50/p95/p99, error_rate
WebSocket	messages_per_sec, lag, reconnections
交易	orders_total, fill_rate, slippage, execution_time
错误	error_count, error_rate_by_type

最佳实践

监控延迟百分位数 — P99比平均值更重要
主动设置警报 — 在用户注意到之前捕捉问题
跟踪自定义指标 — 特定于业务的KPIs
查看日报告 — 及早发现趋势
导出分析 — 使用外部工具进行深入分析