feat(market): Add 15m/1h timeframes for comprehensive trend analysis

## Problem Merged WebSocket architecture (nofxaios/dev) only supported 3m/4h intervals, but adaptive.txt v5.5.6.1 requires 15m/1h data for: - BTC state evaluation (line 105-107) - Multi-confirmation checklist (line 146, 159) - False breakout detection (line 176, 182) - Confidence scoring (line 197) ## Solution Add 15m and 1h timeframe support across WebSocket pipeline: ### 1. market/monitor.go (+64/-40 lines) - Add klineDataMap15m and klineDataMap1h to WSMonitor struct - Update subKlineTime: ["3m", "4h"] → ["3m", "15m", "1h", "4h"] - Extend initializeHistoricalData() to load 15m/1h historical klines - Update getKlineDataMap() switch to handle all 4 timeframes - Fix bug: line 109 used wrong variable (klines vs klines4h) ### 2. market/types.go (+26/-1 lines) - Add MidTermData15m struct (15-minute short-term trend filtering) - Add MidTermData1h struct (1-hour mid-term trend confirmation) - Update Data struct to include: - MidTermSeries15m *MidTermData15m - MidTermSeries1h *MidTermData1h ### 3. market/data.go (+171/-2 lines) - Update Get() to fetch klines15m and klines1h via WebSocket - Implement calculateMidTermSeries15m() - computes EMA20, MACD, RSI7/14 for 15m - Implement calculateMidTermSeries1h() - computes EMA20, MACD, RSI7/14 for 1h - Update Format() to output 15m/1h series data for AI prompt context ## Impact Assessment ### WebSocket Load (Binance limit: 1024 streams/connection) - 8 coins × 4 timeframes = 32 streams (3% usage) ✅ - 100 coins × 4 timeframes = 400 streams (39% usage) ✅ - 250 coins × 4 timeframes = 1000 streams (98% usage) ⚠️ ### Benefits - Enables adaptive.txt standard mode: 15m/1h/4h multi-timeframe confirmation - Restores false breakout detection: 15m RSI vs 1h RSI divergence checks - Improves confidence scoring: 15m/1h/4h MACD alignment validation - Zero REST API calls (WebSocket cache, no rate limit risk) ## Testing Notes - Monitor initial subscription logs for "已加载 X 的历史K线数据-15m/1h" - Verify AI prompts contain "Mid-term series (15-minute intervals)" section - Check decision logs reference 15m/1h indicators in reasoning Related: adaptive.txt v5.5.6.1 requirements, NoFxAiOS/dev WebSocket merge
2026-07-04 19:41:02 +08:00 · 2025-11-03 19:17:12 +08:00
parent bead75ef8f
commit 8d12514498
3 changed files with 231 additions and 30 deletions
--- a/market/data.go
+++ b/market/data.go
@@ -12,18 +12,31 @@ import (

 // Get 获取指定代币的市场数据
 func Get(symbol string) (*Data, error) {
-	var klines3m, klines4h []Kline
+	var klines3m, klines15m, klines1h, klines4h []Kline
 	var err error
 	// 标准化symbol
 	symbol = Normalize(symbol)
+
 	// 获取3分钟K线数据 (最近10个)
-	klines3m, err = WSMonitorCli.GetCurrentKlines(symbol, "3m") // 多获取一些用于计算
+	klines3m, err = WSMonitorCli.GetCurrentKlines(symbol, "3m")
 	if err != nil {
 		return nil, fmt.Errorf("获取3分钟K线失败: %v", err)
 	}

-	// 获取4小时K线数据 (最近10个)
-	klines4h, err = WSMonitorCli.GetCurrentKlines(symbol, "4h") // 多获取用于计算指标
+	// 获取15分钟K线数据 (最近40个) - 短期趋势
+	klines15m, err = WSMonitorCli.GetCurrentKlines(symbol, "15m")
+	if err != nil {
+		return nil, fmt.Errorf("获取15分钟K线失败: %v", err)
+	}
+
+	// 获取1小时K线数据 (最近60个) - 中期趋势
+	klines1h, err = WSMonitorCli.GetCurrentKlines(symbol, "1h")
+	if err != nil {
+		return nil, fmt.Errorf("获取1小时K线失败: %v", err)
+	}
+
+	// 获取4小时K线数据 (最近60个) - 长期趋势
+	klines4h, err = WSMonitorCli.GetCurrentKlines(symbol, "4h")
 	if err != nil {
 		return nil, fmt.Errorf("获取4小时K线失败: %v", err)
 	}
@@ -63,10 +76,16 @@ func Get(symbol string) (*Data, error) {
 	// 获取Funding Rate
 	fundingRate, _ := getFundingRate(symbol)

-	// 计算日内系列数据
+	// 计算日内系列数据 (3分钟)
 	intradayData := calculateIntradaySeries(klines3m)

-	// 计算长期数据
+	// 计算15分钟系列数据
+	midTermData15m := calculateMidTermSeries15m(klines15m)
+
+	// 计算1小时系列数据
+	midTermData1h := calculateMidTermSeries1h(klines1h)
+
+	// 计算长期数据 (4小时)
 	longerTermData := calculateLongerTermData(klines4h)

 	return &Data{
@@ -80,6 +99,8 @@ func Get(symbol string) (*Data, error) {
 		OpenInterest:      oiData,
 		FundingRate:       fundingRate,
 		IntradaySeries:    intradayData,
+		MidTermSeries15m:  midTermData15m,
+		MidTermSeries1h:   midTermData1h,
 		LongerTermContext: longerTermData,
 	}, nil
 }
@@ -243,6 +264,96 @@ func calculateIntradaySeries(klines []Kline) *IntradayData {
 	return data
 }

+// calculateMidTermSeries15m 计算15分钟系列数据
+func calculateMidTermSeries15m(klines []Kline) *MidTermData15m {
+	data := &MidTermData15m{
+		MidPrices:   make([]float64, 0, 10),
+		EMA20Values: make([]float64, 0, 10),
+		MACDValues:  make([]float64, 0, 10),
+		RSI7Values:  make([]float64, 0, 10),
+		RSI14Values: make([]float64, 0, 10),
+	}
+
+	// 获取最近10个数据点
+	start := len(klines) - 10
+	if start < 0 {
+		start = 0
+	}
+
+	for i := start; i < len(klines); i++ {
+		data.MidPrices = append(data.MidPrices, klines[i].Close)
+
+		// 计算每个点的EMA20
+		if i >= 19 {
+			ema20 := calculateEMA(klines[:i+1], 20)
+			data.EMA20Values = append(data.EMA20Values, ema20)
+		}
+
+		// 计算每个点的MACD
+		if i >= 25 {
+			macd := calculateMACD(klines[:i+1])
+			data.MACDValues = append(data.MACDValues, macd)
+		}
+
+		// 计算每个点的RSI
+		if i >= 7 {
+			rsi7 := calculateRSI(klines[:i+1], 7)
+			data.RSI7Values = append(data.RSI7Values, rsi7)
+		}
+		if i >= 14 {
+			rsi14 := calculateRSI(klines[:i+1], 14)
+			data.RSI14Values = append(data.RSI14Values, rsi14)
+		}
+	}
+
+	return data
+}
+
+// calculateMidTermSeries1h 计算1小时系列数据
+func calculateMidTermSeries1h(klines []Kline) *MidTermData1h {
+	data := &MidTermData1h{
+		MidPrices:   make([]float64, 0, 10),
+		EMA20Values: make([]float64, 0, 10),
+		MACDValues:  make([]float64, 0, 10),
+		RSI7Values:  make([]float64, 0, 10),
+		RSI14Values: make([]float64, 0, 10),
+	}
+
+	// 获取最近10个数据点
+	start := len(klines) - 10
+	if start < 0 {
+		start = 0
+	}
+
+	for i := start; i < len(klines); i++ {
+		data.MidPrices = append(data.MidPrices, klines[i].Close)
+
+		// 计算每个点的EMA20
+		if i >= 19 {
+			ema20 := calculateEMA(klines[:i+1], 20)
+			data.EMA20Values = append(data.EMA20Values, ema20)
+		}
+
+		// 计算每个点的MACD
+		if i >= 25 {
+			macd := calculateMACD(klines[:i+1])
+			data.MACDValues = append(data.MACDValues, macd)
+		}
+
+		// 计算每个点的RSI
+		if i >= 7 {
+			rsi7 := calculateRSI(klines[:i+1], 7)
+			data.RSI7Values = append(data.RSI7Values, rsi7)
+		}
+		if i >= 14 {
+			rsi14 := calculateRSI(klines[:i+1], 14)
+			data.RSI14Values = append(data.RSI14Values, rsi14)
+		}
+	}
+
+	return data
+}
+
 // calculateLongerTermData 计算长期数据
 func calculateLongerTermData(klines []Kline) *LongerTermData {
 	data := &LongerTermData{
@@ -396,6 +507,54 @@ func Format(data *Data) string {
 		}
 	}

+	if data.MidTermSeries15m != nil {
+		sb.WriteString("Mid‑term series (15‑minute intervals, oldest → latest):\n\n")
+
+		if len(data.MidTermSeries15m.MidPrices) > 0 {
+			sb.WriteString(fmt.Sprintf("Mid prices: %s\n\n", formatFloatSlice(data.MidTermSeries15m.MidPrices)))
+		}
+
+		if len(data.MidTermSeries15m.EMA20Values) > 0 {
+			sb.WriteString(fmt.Sprintf("EMA indicators (20‑period): %s\n\n", formatFloatSlice(data.MidTermSeries15m.EMA20Values)))
+		}
+
+		if len(data.MidTermSeries15m.MACDValues) > 0 {
+			sb.WriteString(fmt.Sprintf("MACD indicators: %s\n\n", formatFloatSlice(data.MidTermSeries15m.MACDValues)))
+		}
+
+		if len(data.MidTermSeries15m.RSI7Values) > 0 {
+			sb.WriteString(fmt.Sprintf("RSI indicators (7‑Period): %s\n\n", formatFloatSlice(data.MidTermSeries15m.RSI7Values)))
+		}
+
+		if len(data.MidTermSeries15m.RSI14Values) > 0 {
+			sb.WriteString(fmt.Sprintf("RSI indicators (14‑Period): %s\n\n", formatFloatSlice(data.MidTermSeries15m.RSI14Values)))
+		}
+	}
+
+	if data.MidTermSeries1h != nil {
+		sb.WriteString("Mid‑term series (1‑hour intervals, oldest → latest):\n\n")
+
+		if len(data.MidTermSeries1h.MidPrices) > 0 {
+			sb.WriteString(fmt.Sprintf("Mid prices: %s\n\n", formatFloatSlice(data.MidTermSeries1h.MidPrices)))
+		}
+
+		if len(data.MidTermSeries1h.EMA20Values) > 0 {
+			sb.WriteString(fmt.Sprintf("EMA indicators (20‑period): %s\n\n", formatFloatSlice(data.MidTermSeries1h.EMA20Values)))
+		}
+
+		if len(data.MidTermSeries1h.MACDValues) > 0 {
+			sb.WriteString(fmt.Sprintf("MACD indicators: %s\n\n", formatFloatSlice(data.MidTermSeries1h.MACDValues)))
+		}
+
+		if len(data.MidTermSeries1h.RSI7Values) > 0 {
+			sb.WriteString(fmt.Sprintf("RSI indicators (7‑Period): %s\n\n", formatFloatSlice(data.MidTermSeries1h.RSI7Values)))
+		}
+
+		if len(data.MidTermSeries1h.RSI14Values) > 0 {
+			sb.WriteString(fmt.Sprintf("RSI indicators (14‑Period): %s\n\n", formatFloatSlice(data.MidTermSeries1h.RSI14Values)))
+		}
+	}
+
 	if data.LongerTermContext != nil {
 		sb.WriteString("Longer‑term context (4‑hour timeframe):\n\n")

--- a/market/monitor.go
+++ b/market/monitor.go
@@ -15,9 +15,11 @@ type WSMonitor struct {
 	symbols        []string
 	featuresMap    sync.Map
 	alertsChan     chan Alert
-	klineDataMap3m sync.Map // 存储每个交易对的K线历史数据
-	klineDataMap4h sync.Map // 存储每个交易对的K线历史数据
-	tickerDataMap  sync.Map // 存储每个交易对的ticker数据
+	klineDataMap3m  sync.Map // 存储每个交易对的K线历史数据
+	klineDataMap15m sync.Map // 存储每个交易对的15分钟K线历史数据
+	klineDataMap1h  sync.Map // 存储每个交易对的1小时K线历史数据
+	klineDataMap4h  sync.Map // 存储每个交易对的K线历史数据
+	tickerDataMap   sync.Map // 存储每个交易对的ticker数据
 	batchSize      int
 	filterSymbols  sync.Map // 使用sync.Map来存储需要监控的币种和其状态
 	symbolStats    sync.Map // 存储币种统计信息
@@ -32,7 +34,7 @@ type SymbolStats struct {
 }

 var WSMonitorCli *WSMonitor
-var subKlineTime = []string{"3m", "4h"} // 管理订阅流的K线周期
+var subKlineTime = []string{"3m", "15m", "1h", "4h"} // 管理订阅流的K线周期

 func NewWSMonitor(batchSize int) *WSMonitor {
 	WSMonitorCli = &WSMonitor{
@@ -89,25 +91,40 @@ func (m *WSMonitor) initializeHistoricalData() error {
 			defer wg.Done()
 			defer func() { <-semaphore }()

-			// 获取历史K线数据
-			klines, err := apiClient.GetKlines(s, "3m", 100)
+			// 获取3分钟历史K线数据
+			klines3m, err := apiClient.GetKlines(s, "3m", 100)
 			if err != nil {
-				log.Printf("获取 %s 历史数据失败: %v", s, err)
-				return
+				log.Printf("获取 %s 3m历史数据失败: %v", s, err)
+			} else if len(klines3m) > 0 {
+				m.klineDataMap3m.Store(s, klines3m)
+				log.Printf("已加载 %s 的历史K线数据-3m: %d 条", s, len(klines3m))
 			}
-			if len(klines) > 0 {
-				m.klineDataMap3m.Store(s, klines)
-				log.Printf("已加载 %s 的历史K线数据-3m: %d 条", s, len(klines))
+
+			// 获取15分钟历史K线数据
+			klines15m, err := apiClient.GetKlines(s, "15m", 100)
+			if err != nil {
+				log.Printf("获取 %s 15m历史数据失败: %v", s, err)
+			} else if len(klines15m) > 0 {
+				m.klineDataMap15m.Store(s, klines15m)
+				log.Printf("已加载 %s 的历史K线数据-15m: %d 条", s, len(klines15m))
 			}
-			// 获取历史K线数据
+
+			// 获取1小时历史K线数据
+			klines1h, err := apiClient.GetKlines(s, "1h", 100)
+			if err != nil {
+				log.Printf("获取 %s 1h历史数据失败: %v", s, err)
+			} else if len(klines1h) > 0 {
+				m.klineDataMap1h.Store(s, klines1h)
+				log.Printf("已加载 %s 的历史K线数据-1h: %d 条", s, len(klines1h))
+			}
+
+			// 获取4小时历史K线数据
 			klines4h, err := apiClient.GetKlines(s, "4h", 100)
 			if err != nil {
-				log.Printf("获取 %s 历史数据失败: %v", s, err)
-				return
-			}
-			if len(klines4h) > 0 {
-				m.klineDataMap4h.Store(s, klines)
-				log.Printf("已加载 %s 的历史K线数据-4h: %d 条", s, len(klines))
+				log.Printf("获取 %s 4h历史数据失败: %v", s, err)
+			} else if len(klines4h) > 0 {
+				m.klineDataMap4h.Store(s, klines4h)
+				log.Printf("已加载 %s 的历史K线数据-4h: %d 条", s, len(klines4h))
 			}
 		}(symbol)
 	}
@@ -180,11 +197,16 @@ func (m *WSMonitor) handleKlineData(symbol string, ch <-chan []byte, _time strin

 func (m *WSMonitor) getKlineDataMap(_time string) *sync.Map {
 	var klineDataMap *sync.Map
-	if _time == "3m" {
+	switch _time {
+	case "3m":
 		klineDataMap = &m.klineDataMap3m
-	} else if _time == "4h" {
+	case "15m":
+		klineDataMap = &m.klineDataMap15m
+	case "1h":
+		klineDataMap = &m.klineDataMap1h
+	case "4h":
 		klineDataMap = &m.klineDataMap4h
-	} else {
+	default:
 		klineDataMap = &sync.Map{}
 	}
 	return klineDataMap
--- a/market/types.go
+++ b/market/types.go
@@ -13,8 +13,10 @@ type Data struct {
 	CurrentRSI7       float64
 	OpenInterest      *OIData
 	FundingRate       float64
-	IntradaySeries    *IntradayData
-	LongerTermContext *LongerTermData
+	IntradaySeries    *IntradayData      // 3分钟数据 - 实时价格
+	MidTermSeries15m  *MidTermData15m    // 15分钟数据 - 短期趋势
+	MidTermSeries1h   *MidTermData1h     // 1小时数据 - 中期趋势
+	LongerTermContext *LongerTermData    // 4小时数据 - 长期趋势
 }

 // OIData Open Interest数据
@@ -23,7 +25,7 @@ type OIData struct {
 	Average float64
 }

-// IntradayData 日内数据(3分钟间隔)
+// IntradayData 日内数据(3分钟间隔) - 主要用于获取实时价格
 type IntradayData struct {
 	MidPrices   []float64
 	EMA20Values []float64
@@ -32,6 +34,24 @@ type IntradayData struct {
 	RSI14Values []float64
 }

+// MidTermData15m 15分钟时间框架数据 - 短期趋势过滤
+type MidTermData15m struct {
+	MidPrices   []float64
+	EMA20Values []float64
+	MACDValues  []float64
+	RSI7Values  []float64
+	RSI14Values []float64
+}
+
+// MidTermData1h 1小时时间框架数据 - 中期趋势确认
+type MidTermData1h struct {
+	MidPrices   []float64
+	EMA20Values []float64
+	MACDValues  []float64
+	RSI7Values  []float64
+	RSI14Values []float64
+}
+
 // LongerTermData 长期数据(4小时时间框架)
 type LongerTermData struct {
 	EMA20         float64