新增内置AI评分

修改market/data.go Get函数获取K线为流式获取(可以解决传入币种比较多的情况下耗时问题)
market目录下新增文件
main.go 新增运行入口
通过inside_coins=true控制
该评分默认初始化大约需要2分钟左右(因为币种列表比较多，api有限速)
使用时应该注意engine.go下的流动性过滤问题

market/feature_engine.go

@@ -0,0 +1,229 @@
+package market
+
+import (
+	"fmt"
+	"math"
+	"time"
+)
+
+type FeatureEngine struct {
+	alertThresholds AlertThresholds
+}
+
+func NewFeatureEngine(thresholds AlertThresholds) *FeatureEngine {
+	return &FeatureEngine{
+		alertThresholds: thresholds,
+	}
+}
+
+func (e *FeatureEngine) CalculateFeatures(symbol string, klines []Kline) *SymbolFeatures {
+	if len(klines) < 20 {
+		return nil
+	}
+
+	features := &SymbolFeatures{
+		Symbol:    symbol,
+		Timestamp: time.Now(),
+	}
+
+	// 提取价格和交易量数据
+	closes := make([]float64, len(klines))
+	volumes := make([]float64, len(klines))
+	highs := make([]float64, len(klines))
+	lows := make([]float64, len(klines))
+
+	for i, k := range klines {
+		closes[i] = k.Close
+		volumes[i] = k.Volume
+		highs[i] = k.High
+		lows[i] = k.Low
+	}
+
+	// 价格特征
+	features.Price = closes[len(closes)-1]
+	features.PriceChange15Min = (closes[len(closes)-1] - closes[len(closes)-2]) / closes[len(closes)-2]
+
+	if len(closes) >= 5 {
+		features.PriceChange1H = (closes[len(closes)-1] - closes[len(closes)-5]) / closes[len(closes)-5]
+	}
+	if len(closes) >= 17 {
+		features.PriceChange4H = (closes[len(closes)-1] - closes[len(closes)-17]) / closes[len(closes)-17]
+	}
+
+	// 交易量特征
+	currentVolume := volumes[len(volumes)-1]
+	features.Volume = currentVolume
+
+	// 5周期平均交易量
+	if len(volumes) >= 6 {
+		avgVolume5 := e.calculateAverage(volumes[len(volumes)-6 : len(volumes)-1])
+		features.VolumeRatio5 = currentVolume / avgVolume5
+	}
+
+	// 20周期平均交易量
+	if len(volumes) >= 21 {
+		avgVolume20 := e.calculateAverage(volumes[len(volumes)-21 : len(volumes)-1])
+		features.VolumeRatio20 = currentVolume / avgVolume20
+	}
+
+	// 交易量趋势
+	if features.VolumeRatio20 > 0 {
+		features.VolumeTrend = features.VolumeRatio5 / features.VolumeRatio20
+	}
+
+	// 技术指标
+	features.RSI14 = e.calculateRSI(closes, 14)
+	features.SMA5 = e.calculateSMA(closes, 5)
+	features.SMA10 = e.calculateSMA(closes, 10)
+	features.SMA20 = e.calculateSMA(closes, 20)
+
+	// 波动特征
+	currentHigh := highs[len(highs)-1]
+	currentLow := lows[len(lows)-1]
+	features.HighLowRatio = (currentHigh - currentLow) / features.Price
+	features.Volatility20 = e.calculateVolatility(closes, 20)
+
+	// 价格在区间中的位置
+	if currentHigh != currentLow {
+		features.PositionInRange = (features.Price - currentLow) / (currentHigh - currentLow)
+	} else {
+		features.PositionInRange = 0.5
+	}
+
+	return features
+}
+
+func (e *FeatureEngine) calculateAverage(values []float64) float64 {
+	sum := 0.0
+	for _, v := range values {
+		sum += v
+	}
+	return sum / float64(len(values))
+}
+
+func (e *FeatureEngine) calculateSMA(prices []float64, period int) float64 {
+	if len(prices) < period {
+		return 0
+	}
+	return e.calculateAverage(prices[len(prices)-period:])
+}
+
+func (e *FeatureEngine) calculateRSI(prices []float64, period int) float64 {
+	if len(prices) <= period {
+		return 50
+	}
+
+	gains := make([]float64, 0)
+	losses := make([]float64, 0)
+
+	for i := 1; i < len(prices); i++ {
+		change := prices[i] - prices[i-1]
+		if change > 0 {
+			gains = append(gains, change)
+			losses = append(losses, 0)
+		} else {
+			gains = append(gains, 0)
+			losses = append(losses, -change)
+		}
+	}
+
+	// 只取最近period个数据点
+	if len(gains) > period {
+		gains = gains[len(gains)-period:]
+		losses = losses[len(losses)-period:]
+	}
+
+	avgGain := e.calculateAverage(gains)
+	avgLoss := e.calculateAverage(losses)
+
+	if avgLoss == 0 {
+		return 100
+	}
+
+	rs := avgGain / avgLoss
+	return 100 - (100 / (1 + rs))
+}
+
+func (e *FeatureEngine) calculateVolatility(prices []float64, period int) float64 {
+	if len(prices) < period {
+		return 0
+	}
+
+	periodPrices := prices[len(prices)-period:]
+	mean := e.calculateAverage(periodPrices)
+
+	variance := 0.0
+	for _, price := range periodPrices {
+		variance += math.Pow(price-mean, 2)
+	}
+	variance /= float64(len(periodPrices))
+
+	return math.Sqrt(variance) / mean
+}
+
+func (e *FeatureEngine) DetectAlerts(features *SymbolFeatures) []Alert {
+	var alerts []Alert
+
+	// 交易量放大检测
+	if features.VolumeRatio5 > e.alertThresholds.VolumeSpike {
+		alerts = append(alerts, Alert{
+			Type:      "VOLUME_SPIKE",
+			Symbol:    features.Symbol,
+			Value:     features.VolumeRatio5,
+			Threshold: e.alertThresholds.VolumeSpike,
+			Message:   fmt.Sprintf("%s 交易量放大 %.2f 倍", features.Symbol, features.VolumeRatio5),
+			Timestamp: time.Now(),
+		})
+	}
+
+	// 15分钟价格异动
+	if math.Abs(features.PriceChange15Min) > e.alertThresholds.PriceChange15Min {
+		direction := "上涨"
+		if features.PriceChange15Min < 0 {
+			direction = "下跌"
+		}
+		alerts = append(alerts, Alert{
+			Type:      "PRICE_CHANGE_15MIN",
+			Symbol:    features.Symbol,
+			Value:     features.PriceChange15Min,
+			Threshold: e.alertThresholds.PriceChange15Min,
+			Message:   fmt.Sprintf("%s 15分钟%s %.2f%%", features.Symbol, direction, features.PriceChange15Min*100),
+			Timestamp: time.Now(),
+		})
+	}
+
+	// 交易量趋势
+	if features.VolumeTrend > e.alertThresholds.VolumeTrend {
+		alerts = append(alerts, Alert{
+			Type:      "VOLUME_TREND",
+			Symbol:    features.Symbol,
+			Value:     features.VolumeTrend,
+			Threshold: e.alertThresholds.VolumeTrend,
+			Message:   fmt.Sprintf("%s 交易量趋势增强 %.2f 倍", features.Symbol, features.VolumeTrend),
+			Timestamp: time.Now(),
+		})
+	}
+
+	// RSI超买超卖
+	if features.RSI14 > e.alertThresholds.RSIOverbought {
+		alerts = append(alerts, Alert{
+			Type:      "RSI_OVERBOUGHT",
+			Symbol:    features.Symbol,
+			Value:     features.RSI14,
+			Threshold: e.alertThresholds.RSIOverbought,
+			Message:   fmt.Sprintf("%s RSI超买: %.2f", features.Symbol, features.RSI14),
+			Timestamp: time.Now(),
+		})
+	} else if features.RSI14 < e.alertThresholds.RSIOversold {
+		alerts = append(alerts, Alert{
+			Type:      "RSI_OVERSOLD",
+			Symbol:    features.Symbol,
+			Value:     features.RSI14,
+			Threshold: e.alertThresholds.RSIOversold,
+			Message:   fmt.Sprintf("%s RSI超卖: %.2f", features.Symbol, features.RSI14),
+			Timestamp: time.Now(),
+		})
+	}
+
+	return alerts
+}