mirror of
https://github.com/NoFxAiOS/nofx.git
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70eb35d6de
- Add QwenAgent client for Alibaba Cloud Bailian platform - Support both sync and streaming chat modes - Add multi-turn conversation with session management - Add OpenAI-compatible API support - Add indicator calculation tests comparing local vs AI results - Use environment variables for API credentials (QWEN_APP_ID, QWEN_API_KEY)
738 lines
20 KiB
Go
738 lines
20 KiB
Go
package llm
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import (
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"context"
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"encoding/json"
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"fmt"
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"math"
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"nofx/market"
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"nofx/provider/coinank"
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"nofx/provider/coinank/coinank_api"
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"nofx/provider/coinank/coinank_enum"
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"regexp"
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"strconv"
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"strings"
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"testing"
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"time"
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)
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// IndicatorResult AI 计算的指标结果
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type IndicatorResult struct {
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EMA12 float64 `json:"ema12"`
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EMA26 float64 `json:"ema26"`
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MACD float64 `json:"macd"`
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RSI14 float64 `json:"rsi14"`
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BOLLUp float64 `json:"boll_upper"`
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BOLLMid float64 `json:"boll_middle"`
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BOLLLow float64 `json:"boll_lower"`
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ATR14 float64 `json:"atr14"`
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SMA20 float64 `json:"sma20"`
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}
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// 本地计算指标（使用 market 包的函数）
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func calculateLocalIndicators(klines []market.Kline) IndicatorResult {
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result := IndicatorResult{}
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if len(klines) >= 12 {
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result.EMA12 = market.ExportCalculateEMA(klines, 12)
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}
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if len(klines) >= 26 {
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result.EMA26 = market.ExportCalculateEMA(klines, 26)
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result.MACD = market.ExportCalculateMACD(klines)
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}
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if len(klines) > 14 {
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result.RSI14 = market.ExportCalculateRSI(klines, 14)
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}
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if len(klines) >= 20 {
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result.BOLLUp, result.BOLLMid, result.BOLLLow = market.ExportCalculateBOLL(klines, 20, 2.0)
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// SMA20 就是 BOLL 中轨
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result.SMA20 = result.BOLLMid
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}
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if len(klines) > 14 {
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result.ATR14 = market.ExportCalculateATR(klines, 14)
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}
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return result
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}
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// 格式化 K 线数据为文本，发给 AI
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func formatKlinesForAI(klines []market.Kline) string {
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var sb strings.Builder
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sb.WriteString("以下是K线数据（从旧到新排列）：\n")
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sb.WriteString("序号 | 时间 | 开盘价 | 最高价 | 最低价 | 收盘价 | 成交量\n")
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sb.WriteString("-----|------|--------|--------|--------|--------|--------\n")
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for i, k := range klines {
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t := time.UnixMilli(k.OpenTime)
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sb.WriteString(fmt.Sprintf("%d | %s | %.2f | %.2f | %.2f | %.2f | %.2f\n",
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i+1, t.Format("01-02 15:04"), k.Open, k.High, k.Low, k.Close, k.Volume))
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}
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return sb.String()
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}
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// 构建 AI 计算指标的 prompt
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func buildIndicatorPrompt(klines []market.Kline) string {
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klinesText := formatKlinesForAI(klines)
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prompt := fmt.Sprintf(`%s
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请根据以上 %d 根K线数据，计算以下技术指标（使用标准算法）：
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1. EMA12（12周期指数移动平均线）
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2. EMA26（26周期指数移动平均线）
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3. MACD（EMA12 - EMA26）
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4. RSI14（14周期相对强弱指标，使用Wilder平滑法）
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5. BOLL布林带（20周期，2倍标准差）：上轨、中轨、下轨
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6. ATR14（14周期平均真实波幅，使用Wilder平滑法）
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7. SMA20（20周期简单移动平均线）
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请严格按照以下 JSON 格式返回结果，不要添加任何其他文字：
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{
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"ema12": 数值,
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"ema26": 数值,
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"macd": 数值,
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"rsi14": 数值,
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"boll_upper": 数值,
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"boll_middle": 数值,
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"boll_lower": 数值,
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"atr14": 数值,
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"sma20": 数值
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}
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注意：
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- 所有数值保留2位小数
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- EMA计算使用SMA作为初始值，乘数为 2/(period+1)
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- RSI使用Wilder平滑法
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- 只返回JSON，不要解释过程`, klinesText, len(klines))
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return prompt
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}
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// 从 AI 响应中提取 JSON
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func extractJSONFromResponse(text string) (IndicatorResult, error) {
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var result IndicatorResult
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// 尝试直接解析
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if err := json.Unmarshal([]byte(text), &result); err == nil {
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return result, nil
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}
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// 提取 JSON 部分
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re := regexp.MustCompile(`\{[^{}]*"ema12"[^{}]*\}`)
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match := re.FindString(text)
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if match == "" {
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// 尝试更宽松的匹配
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start := strings.Index(text, "{")
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end := strings.LastIndex(text, "}")
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if start != -1 && end != -1 && end > start {
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match = text[start : end+1]
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}
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}
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if match == "" {
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return result, fmt.Errorf("no JSON found in response: %s", text[:min(200, len(text))])
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}
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if err := json.Unmarshal([]byte(match), &result); err != nil {
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return result, fmt.Errorf("parse JSON failed: %w, json: %s", err, match)
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}
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return result, nil
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}
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// 比较两个指标结果，返回误差百分比
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func compareIndicators(local, ai IndicatorResult) map[string]float64 {
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errors := make(map[string]float64)
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calcError := func(name string, localVal, aiVal float64) {
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if localVal == 0 {
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if aiVal == 0 {
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errors[name] = 0
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} else {
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errors[name] = 100 // 本地为0但AI不为0
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}
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return
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}
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errors[name] = math.Abs(localVal-aiVal) / math.Abs(localVal) * 100
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}
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calcError("EMA12", local.EMA12, ai.EMA12)
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calcError("EMA26", local.EMA26, ai.EMA26)
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calcError("MACD", local.MACD, ai.MACD)
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calcError("RSI14", local.RSI14, ai.RSI14)
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calcError("BOLL_UP", local.BOLLUp, ai.BOLLUp)
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calcError("BOLL_MID", local.BOLLMid, ai.BOLLMid)
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calcError("BOLL_LOW", local.BOLLLow, ai.BOLLLow)
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calcError("ATR14", local.ATR14, ai.ATR14)
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calcError("SMA20", local.SMA20, ai.SMA20)
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return errors
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}
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// 生成测试用 K 线数据
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func generateTestKlines(count int, basePrice float64) []market.Kline {
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klines := make([]market.Kline, count)
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price := basePrice
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now := time.Now()
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for i := 0; i < count; i++ {
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// 模拟价格波动
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change := (float64(i%7) - 3) * 0.5 // -1.5 到 +1.5 的波动
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price = price + change
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open := price
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high := price + math.Abs(change)*0.5 + 0.5
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low := price - math.Abs(change)*0.5 - 0.3
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close := price + (change * 0.3)
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klines[i] = market.Kline{
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OpenTime: now.Add(time.Duration(-count+i) * time.Hour).UnixMilli(),
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Open: open,
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High: high,
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Low: low,
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Close: close,
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Volume: 1000 + float64(i*100),
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CloseTime: now.Add(time.Duration(-count+i+1) * time.Hour).UnixMilli(),
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}
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}
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return klines
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}
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// TestQwenIndicatorCalculation 测试 AI 计算技术指标
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func TestQwenIndicatorCalculation(t *testing.T) {
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agent := NewQwenAgent(QwenAppID, QwenAPIKey)
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ctx := context.Background()
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// 生成 30 根测试 K 线
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klines := generateTestKlines(30, 95000)
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t.Log("===== K线数据 (最后5根) =====")
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for i := len(klines) - 5; i < len(klines); i++ {
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k := klines[i]
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t.Logf(" [%d] O:%.2f H:%.2f L:%.2f C:%.2f", i+1, k.Open, k.High, k.Low, k.Close)
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}
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// 本地计算
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t.Log("\n===== 本地计算结果 =====")
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localResult := calculateLocalIndicators(klines)
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t.Logf(" EMA12: %.2f", localResult.EMA12)
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t.Logf(" EMA26: %.2f", localResult.EMA26)
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t.Logf(" MACD: %.2f", localResult.MACD)
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t.Logf(" RSI14: %.2f", localResult.RSI14)
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t.Logf(" BOLL上轨: %.2f", localResult.BOLLUp)
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t.Logf(" BOLL中轨: %.2f", localResult.BOLLMid)
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t.Logf(" BOLL下轨: %.2f", localResult.BOLLLow)
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t.Logf(" ATR14: %.2f", localResult.ATR14)
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t.Logf(" SMA20: %.2f", localResult.SMA20)
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// AI 计算
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t.Log("\n===== 调用 AI 计算 =====")
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prompt := buildIndicatorPrompt(klines)
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t.Logf("Prompt 长度: %d 字符", len(prompt))
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start := time.Now()
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resp, err := agent.Chat(ctx, prompt)
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elapsed := time.Since(start)
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if err != nil {
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t.Fatalf("AI 调用失败: %v", err)
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}
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t.Logf("AI 响应耗时: %v", elapsed)
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t.Logf("AI 原始响应:\n%s", resp.Output.Text)
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// 解析 AI 结果
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aiResult, err := extractJSONFromResponse(resp.Output.Text)
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if err != nil {
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t.Fatalf("解析 AI 结果失败: %v", err)
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}
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t.Log("\n===== AI 计算结果 =====")
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t.Logf(" EMA12: %.2f", aiResult.EMA12)
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t.Logf(" EMA26: %.2f", aiResult.EMA26)
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t.Logf(" MACD: %.2f", aiResult.MACD)
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t.Logf(" RSI14: %.2f", aiResult.RSI14)
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t.Logf(" BOLL上轨: %.2f", aiResult.BOLLUp)
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t.Logf(" BOLL中轨: %.2f", aiResult.BOLLMid)
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t.Logf(" BOLL下轨: %.2f", aiResult.BOLLLow)
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t.Logf(" ATR14: %.2f", aiResult.ATR14)
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t.Logf(" SMA20: %.2f", aiResult.SMA20)
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// 对比结果
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t.Log("\n===== 误差对比 (%) =====")
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errors := compareIndicators(localResult, aiResult)
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totalError := 0.0
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for name, errPct := range errors {
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status := "✓"
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if errPct > 5 {
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status = "⚠"
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}
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if errPct > 10 {
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status = "✗"
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}
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t.Logf(" %s %s: %.2f%%", status, name, errPct)
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totalError += errPct
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}
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avgError := totalError / float64(len(errors))
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t.Logf("\n 平均误差: %.2f%%", avgError)
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if avgError > 10 {
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t.Logf("警告: AI 计算误差较大，可能算法理解有差异")
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} else if avgError < 5 {
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t.Log("AI 计算精度良好！")
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}
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}
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// TestQwenIndicatorWithRealKlines 使用真实 K 线测试
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func TestQwenIndicatorWithRealKlines(t *testing.T) {
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// 尝试获取真实 K 线数据
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client := market.NewAPIClient()
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klines, err := client.GetKlines("BTC", "1h", 30)
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if err != nil {
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t.Skipf("获取真实 K 线失败，跳过测试: %v", err)
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return
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}
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if len(klines) < 26 {
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t.Skipf("K 线数量不足: %d", len(klines))
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return
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}
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agent := NewQwenAgent(QwenAppID, QwenAPIKey)
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ctx := context.Background()
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t.Logf("获取到 %d 根 BTC 1h K线", len(klines))
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t.Log("最新价格:", klines[len(klines)-1].Close)
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// 本地计算
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localResult := calculateLocalIndicators(klines)
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t.Log("\n===== 本地计算 =====")
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t.Logf(" EMA12: %.2f, EMA26: %.2f, MACD: %.2f", localResult.EMA12, localResult.EMA26, localResult.MACD)
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t.Logf(" RSI14: %.2f", localResult.RSI14)
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t.Logf(" BOLL: %.2f / %.2f / %.2f", localResult.BOLLUp, localResult.BOLLMid, localResult.BOLLLow)
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// AI 计算
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prompt := buildIndicatorPrompt(klines)
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resp, err := agent.Chat(ctx, prompt)
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if err != nil {
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t.Fatalf("AI 调用失败: %v", err)
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}
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t.Log("\n===== AI 响应 =====")
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t.Log(resp.Output.Text)
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aiResult, err := extractJSONFromResponse(resp.Output.Text)
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if err != nil {
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t.Logf("解析失败: %v", err)
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return
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}
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// 对比
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errors := compareIndicators(localResult, aiResult)
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t.Log("\n===== 误差 =====")
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for name, errPct := range errors {
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t.Logf(" %s: %.2f%%", name, errPct)
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}
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}
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// TestQwenIndicatorMultiTimeframe 测试多个时间周期
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func TestQwenIndicatorMultiTimeframe(t *testing.T) {
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agent := NewQwenAgent(QwenAppID, QwenAPIKey)
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ctx := context.Background()
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timeframes := []struct {
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name string
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count int
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price float64
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}{
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{"5m周期", 30, 95000},
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{"1h周期", 50, 95000},
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{"4h周期", 40, 95000},
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}
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for _, tf := range timeframes {
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t.Run(tf.name, func(t *testing.T) {
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klines := generateTestKlines(tf.count, tf.price)
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localResult := calculateLocalIndicators(klines)
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// 简化的 prompt
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prompt := buildSimpleIndicatorPrompt(klines)
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resp, err := agent.Chat(ctx, prompt)
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if err != nil {
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t.Fatalf("AI 调用失败: %v", err)
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}
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aiResult, err := extractJSONFromResponse(resp.Output.Text)
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if err != nil {
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t.Logf("解析失败: %v", err)
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t.Logf("AI 响应: %s", resp.Output.Text[:min(500, len(resp.Output.Text))])
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return
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}
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errors := compareIndicators(localResult, aiResult)
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// 计算平均误差
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total := 0.0
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for _, e := range errors {
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total += e
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}
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avgErr := total / float64(len(errors))
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t.Logf("本地 MACD: %.2f, AI MACD: %.2f, 误差: %.2f%%", localResult.MACD, aiResult.MACD, errors["MACD"])
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t.Logf("本地 RSI: %.2f, AI RSI: %.2f, 误差: %.2f%%", localResult.RSI14, aiResult.RSI14, errors["RSI14"])
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t.Logf("平均误差: %.2f%%", avgErr)
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})
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time.Sleep(2 * time.Second) // 避免请求过快
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}
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}
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||
|
||
// 简化的 prompt
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func buildSimpleIndicatorPrompt(klines []market.Kline) string {
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// 只提供收盘价序列，减少 token
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var prices []string
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for _, k := range klines {
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prices = append(prices, fmt.Sprintf("%.2f", k.Close))
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}
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||
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return fmt.Sprintf(`收盘价序列（从旧到新）: [%s]
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请计算技术指标并返回 JSON：
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- ema12: 12周期EMA
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||
- ema26: 26周期EMA
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||
- macd: EMA12-EMA26
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||
- rsi14: 14周期RSI(Wilder平滑)
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||
- boll_upper, boll_middle, boll_lower: 20周期BOLL(2倍标准差)
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||
- atr14: 0 (无高低价数据)
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||
- sma20: 20周期SMA
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只返回JSON格式：{"ema12":数值,"ema26":数值,...}`, strings.Join(prices, ","))
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}
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// TestQwenIndicatorAccuracy 精度测试：使用简单数据验证算法
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func TestQwenIndicatorAccuracy(t *testing.T) {
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agent := NewQwenAgent(QwenAppID, QwenAPIKey)
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||
ctx := context.Background()
|
||
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||
// 使用简单递增数据，便于验证
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||
prices := []float64{
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||
100, 101, 102, 103, 104, 105, 106, 107, 108, 109, // 1-10
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110, 111, 112, 113, 114, 115, 116, 117, 118, 119, // 11-20
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120, 121, 122, 123, 124, 125, 126, 127, 128, 129, // 21-30
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}
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||
|
||
// 构建 K 线
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||
klines := make([]market.Kline, len(prices))
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||
for i, p := range prices {
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||
klines[i] = market.Kline{
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||
Open: p - 0.5,
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||
High: p + 1,
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||
Low: p - 1,
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Close: p,
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}
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||
}
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||
|
||
// 本地计算
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||
localResult := calculateLocalIndicators(klines)
|
||
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||
t.Log("===== 简单递增数据测试 =====")
|
||
t.Logf("价格序列: %v", prices)
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||
t.Logf("本地计算:")
|
||
t.Logf(" SMA20 = %.4f (理论值: 119.5)", localResult.SMA20)
|
||
t.Logf(" EMA12 = %.4f", localResult.EMA12)
|
||
t.Logf(" RSI14 = %.4f (持续上涨应接近100)", localResult.RSI14)
|
||
|
||
// AI 计算
|
||
var priceStrs []string
|
||
for _, p := range prices {
|
||
priceStrs = append(priceStrs, strconv.FormatFloat(p, 'f', 0, 64))
|
||
}
|
||
|
||
prompt := fmt.Sprintf(`收盘价序列: [%s]
|
||
|
||
请计算:
|
||
1. SMA20 (20周期简单移动平均)
|
||
2. EMA12 (12周期指数移动平均，初始值用SMA，乘数=2/13)
|
||
3. RSI14 (14周期RSI，Wilder平滑法)
|
||
|
||
返回JSON: {"sma20":数值,"ema12":数值,"rsi14":数值}
|
||
只返回JSON`, strings.Join(priceStrs, ","))
|
||
|
||
resp, err := agent.Chat(ctx, prompt)
|
||
if err != nil {
|
||
t.Fatalf("AI 调用失败: %v", err)
|
||
}
|
||
|
||
t.Logf("\nAI 响应: %s", resp.Output.Text)
|
||
|
||
// 简单解析
|
||
var aiSimple struct {
|
||
SMA20 float64 `json:"sma20"`
|
||
EMA12 float64 `json:"ema12"`
|
||
RSI14 float64 `json:"rsi14"`
|
||
}
|
||
|
||
text := resp.Output.Text
|
||
start := strings.Index(text, "{")
|
||
end := strings.LastIndex(text, "}")
|
||
if start != -1 && end > start {
|
||
json.Unmarshal([]byte(text[start:end+1]), &aiSimple)
|
||
}
|
||
|
||
t.Logf("\nAI 计算:")
|
||
t.Logf(" SMA20 = %.4f", aiSimple.SMA20)
|
||
t.Logf(" EMA12 = %.4f", aiSimple.EMA12)
|
||
t.Logf(" RSI14 = %.4f", aiSimple.RSI14)
|
||
|
||
// 验证 SMA20 (理论值应该是 110+...+129 的平均 = 119.5)
|
||
expectedSMA := 119.5
|
||
if math.Abs(aiSimple.SMA20-expectedSMA) < 0.1 {
|
||
t.Log("\n✓ AI 的 SMA20 计算正确!")
|
||
} else {
|
||
t.Logf("\n✗ AI 的 SMA20 有误差，期望 %.2f", expectedSMA)
|
||
}
|
||
}
|
||
|
||
// coinankKlinesToMarket 将 coinank K线转换为 market.Kline
|
||
func coinankKlinesToMarket(klines []coinank.KlineResult) []market.Kline {
|
||
result := make([]market.Kline, len(klines))
|
||
for i, k := range klines {
|
||
result[i] = market.Kline{
|
||
OpenTime: k.StartTime,
|
||
Open: k.Open,
|
||
High: k.High,
|
||
Low: k.Low,
|
||
Close: k.Close,
|
||
Volume: k.Volume,
|
||
CloseTime: k.EndTime,
|
||
}
|
||
}
|
||
return result
|
||
}
|
||
|
||
// TestQwenETHMultiTimeframe 使用 Coinank 免费 API 获取真实 ETH 数据测试多周期指标
|
||
func TestQwenETHMultiTimeframe(t *testing.T) {
|
||
ctx := context.Background()
|
||
agent := NewQwenAgent(QwenAppID, QwenAPIKey)
|
||
|
||
// 测试多个时间周期
|
||
timeframes := []struct {
|
||
name string
|
||
interval coinank_enum.Interval
|
||
size int
|
||
}{
|
||
{"5分钟", coinank_enum.Minute5, 50},
|
||
{"1小时", coinank_enum.Hour1, 50},
|
||
{"4小时", coinank_enum.Hour4, 50},
|
||
{"日线", coinank_enum.Day1, 30},
|
||
}
|
||
|
||
now := time.Now()
|
||
|
||
for _, tf := range timeframes {
|
||
t.Run(tf.name, func(t *testing.T) {
|
||
// 使用 coinank 免费 API 获取 ETH K线数据
|
||
coinankKlines, err := coinank_api.Kline(ctx, "ETHUSDT", coinank_enum.Binance,
|
||
now.UnixMilli(), coinank_enum.To, tf.size, tf.interval)
|
||
if err != nil {
|
||
t.Fatalf("获取 %s K线失败: %v", tf.name, err)
|
||
}
|
||
|
||
if len(coinankKlines) < 26 {
|
||
t.Skipf("K线数量不足: %d", len(coinankKlines))
|
||
return
|
||
}
|
||
|
||
// 转换为 market.Kline
|
||
klines := coinankKlinesToMarket(coinankKlines)
|
||
|
||
t.Logf("获取到 %d 根 ETH %s K线", len(klines), tf.name)
|
||
t.Logf("最新收盘价: %.2f, 时间: %s",
|
||
klines[len(klines)-1].Close,
|
||
time.UnixMilli(klines[len(klines)-1].CloseTime).Format("2006-01-02 15:04"))
|
||
|
||
// 本地计算
|
||
localResult := calculateLocalIndicators(klines)
|
||
t.Log("\n===== 本地计算 =====")
|
||
t.Logf(" EMA12: %.2f, EMA26: %.2f, MACD: %.4f",
|
||
localResult.EMA12, localResult.EMA26, localResult.MACD)
|
||
t.Logf(" RSI14: %.2f", localResult.RSI14)
|
||
t.Logf(" BOLL: %.2f / %.2f / %.2f",
|
||
localResult.BOLLUp, localResult.BOLLMid, localResult.BOLLLow)
|
||
t.Logf(" ATR14: %.4f", localResult.ATR14)
|
||
|
||
// AI 计算 - 使用简化 prompt（只发收盘价）
|
||
prompt := buildSimpleIndicatorPrompt(klines)
|
||
t.Logf("\nPrompt 长度: %d 字符", len(prompt))
|
||
|
||
start := time.Now()
|
||
resp, err := agent.Chat(ctx, prompt)
|
||
elapsed := time.Since(start)
|
||
|
||
if err != nil {
|
||
t.Fatalf("AI 调用失败: %v", err)
|
||
}
|
||
|
||
t.Logf("AI 响应耗时: %v", elapsed)
|
||
|
||
// 解析 AI 结果
|
||
aiResult, err := extractJSONFromResponse(resp.Output.Text)
|
||
if err != nil {
|
||
t.Logf("AI 原始响应:\n%s", resp.Output.Text[:min(500, len(resp.Output.Text))])
|
||
t.Fatalf("解析失败: %v", err)
|
||
}
|
||
|
||
t.Log("\n===== AI 计算 =====")
|
||
t.Logf(" EMA12: %.2f, EMA26: %.2f, MACD: %.4f",
|
||
aiResult.EMA12, aiResult.EMA26, aiResult.MACD)
|
||
t.Logf(" RSI14: %.2f", aiResult.RSI14)
|
||
t.Logf(" BOLL: %.2f / %.2f / %.2f",
|
||
aiResult.BOLLUp, aiResult.BOLLMid, aiResult.BOLLLow)
|
||
|
||
// 对比误差
|
||
t.Log("\n===== 误差对比 =====")
|
||
errors := compareIndicators(localResult, aiResult)
|
||
totalErr := 0.0
|
||
for name, errPct := range errors {
|
||
status := "✓"
|
||
if errPct > 1 {
|
||
status = "⚠"
|
||
}
|
||
if errPct > 5 {
|
||
status = "✗"
|
||
}
|
||
t.Logf(" %s %-10s: %.2f%%", status, name, errPct)
|
||
totalErr += errPct
|
||
}
|
||
|
||
avgErr := totalErr / float64(len(errors))
|
||
t.Logf("\n 平均误差: %.2f%%", avgErr)
|
||
|
||
if avgErr < 1 {
|
||
t.Log(" ✓ AI 计算精度优秀!")
|
||
} else if avgErr < 5 {
|
||
t.Log(" ⚠ AI 计算精度良好")
|
||
} else {
|
||
t.Log(" ✗ AI 计算误差较大")
|
||
}
|
||
|
||
// 等待避免请求过快
|
||
time.Sleep(2 * time.Second)
|
||
})
|
||
}
|
||
}
|
||
|
||
// TestQwenETHIndicatorComparison ETH 指标对比：使用 Coinank 免费 API + Qwen 标准 API
|
||
func TestQwenETHIndicatorComparison(t *testing.T) {
|
||
ctx := context.Background()
|
||
agent := NewQwenAgent(QwenAppID, QwenAPIKey)
|
||
|
||
// 使用 coinank 免费 API 获取 ETH 1小时 K线
|
||
now := time.Now()
|
||
coinankKlines, err := coinank_api.Kline(ctx, "ETHUSDT", coinank_enum.Binance,
|
||
now.UnixMilli(), coinank_enum.To, 30, coinank_enum.Hour1)
|
||
if err != nil {
|
||
t.Fatalf("获取 K线失败: %v", err)
|
||
}
|
||
|
||
// 转换为 market.Kline
|
||
klines := coinankKlinesToMarket(coinankKlines)
|
||
|
||
t.Logf("获取到 %d 根 ETH 1h K线", len(klines))
|
||
|
||
// 只用收盘价，简化 prompt
|
||
var prices []string
|
||
for _, k := range klines {
|
||
prices = append(prices, fmt.Sprintf("%.2f", k.Close))
|
||
}
|
||
|
||
// 本地计算
|
||
localResult := calculateLocalIndicators(klines)
|
||
|
||
t.Log("\n===== 本地计算结果 =====")
|
||
t.Logf("SMA20: %.2f", localResult.SMA20)
|
||
t.Logf("EMA12: %.2f", localResult.EMA12)
|
||
t.Logf("EMA26: %.2f", localResult.EMA26)
|
||
t.Logf("MACD: %.4f", localResult.MACD)
|
||
t.Logf("RSI14: %.2f", localResult.RSI14)
|
||
|
||
// 简化的 AI prompt
|
||
prompt := fmt.Sprintf(`ETH 最近30根1小时K线收盘价（从旧到新）:
|
||
[%s]
|
||
|
||
请计算以下指标并返回纯 JSON:
|
||
1. sma20: 最后20个价格的简单移动平均
|
||
2. ema12: 12周期EMA（初始值用前12个价格的SMA，乘数=2/13）
|
||
3. ema26: 26周期EMA（初始值用前26个价格的SMA，乘数=2/27）
|
||
4. macd: EMA12 - EMA26
|
||
5. rsi14: 14周期RSI（Wilder平滑法）
|
||
|
||
只返回JSON格式: {"sma20":数值,"ema12":数值,"ema26":数值,"macd":数值,"rsi14":数值}
|
||
不要任何解释文字`, strings.Join(prices, ", "))
|
||
|
||
t.Logf("\n发送 Prompt (%d 字符)", len(prompt))
|
||
|
||
// 使用标准 API
|
||
resp, err := agent.ChatWithModel(ctx, "qwen-max", prompt)
|
||
if err != nil {
|
||
t.Fatalf("AI 调用失败: %v", err)
|
||
}
|
||
|
||
aiText := resp.GetContent()
|
||
t.Logf("\nAI 响应:\n%s", aiText)
|
||
|
||
// 解析
|
||
var aiResult struct {
|
||
SMA20 float64 `json:"sma20"`
|
||
EMA12 float64 `json:"ema12"`
|
||
EMA26 float64 `json:"ema26"`
|
||
MACD float64 `json:"macd"`
|
||
RSI14 float64 `json:"rsi14"`
|
||
}
|
||
|
||
start := strings.Index(aiText, "{")
|
||
end := strings.LastIndex(aiText, "}")
|
||
if start != -1 && end > start {
|
||
if err := json.Unmarshal([]byte(aiText[start:end+1]), &aiResult); err != nil {
|
||
t.Logf("JSON 解析失败: %v", err)
|
||
}
|
||
}
|
||
|
||
t.Log("\n===== AI 计算结果 =====")
|
||
t.Logf("SMA20: %.2f", aiResult.SMA20)
|
||
t.Logf("EMA12: %.2f", aiResult.EMA12)
|
||
t.Logf("EMA26: %.2f", aiResult.EMA26)
|
||
t.Logf("MACD: %.4f", aiResult.MACD)
|
||
t.Logf("RSI14: %.2f", aiResult.RSI14)
|
||
|
||
// 计算误差
|
||
t.Log("\n===== 误差 =====")
|
||
calcErr := func(name string, local, ai float64) {
|
||
if local == 0 {
|
||
t.Logf(" %s: 本地=0, AI=%.2f", name, ai)
|
||
return
|
||
}
|
||
errPct := math.Abs(local-ai) / math.Abs(local) * 100
|
||
status := "✓"
|
||
if errPct > 1 {
|
||
status = "⚠"
|
||
}
|
||
if errPct > 5 {
|
||
status = "✗"
|
||
}
|
||
t.Logf(" %s %s: 本地=%.2f, AI=%.2f, 误差=%.2f%%", status, name, local, ai, errPct)
|
||
}
|
||
|
||
calcErr("SMA20", localResult.SMA20, aiResult.SMA20)
|
||
calcErr("EMA12", localResult.EMA12, aiResult.EMA12)
|
||
calcErr("EMA26", localResult.EMA26, aiResult.EMA26)
|
||
calcErr("MACD", localResult.MACD, aiResult.MACD)
|
||
calcErr("RSI14", localResult.RSI14, aiResult.RSI14)
|
||
}
|