mirror of
https://github.com/laoxong/nofx.git
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Feature/custom strategy (#1172)
* feat: add Strategy Studio with multi-timeframe support - Add Strategy Studio page with three-column layout for strategy management - Support multi-timeframe K-line data selection (5m, 15m, 1h, 4h, etc.) - Add GetWithTimeframes() function in market package for fetching multiple timeframes - Add TimeframeSeriesData struct for storing per-timeframe technical indicators - Update formatMarketData() to display all selected timeframes in AI prompt - Add strategy API endpoints for CRUD operations and test run - Integrate real AI test runs with configured AI models - Support custom AI500 and OI Top API URLs from strategy config * docs: add Strategy Studio screenshot to README files * fix: correct strategy-studio.png filename case in README * refactor: remove legacy signal source config and simplify trader creation - Remove signal source configuration from traders page (now handled by strategy) - Remove advanced options (legacy config) from TraderConfigModal - Rename default strategy to "默认山寨策略" with AI500 coin pool URL - Delete SignalSourceModal and SignalSourceWarning components - Clean up related stores, hooks, and page components
This commit is contained in:
tinkle-community
GitHub
+269
@@ -112,6 +112,230 @@ func Get(symbol string) (*Data, error) {
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}, nil
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}
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// GetWithTimeframes 获取指定多个时间周期的市场数据
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// timeframes: 时间周期列表，如 ["5m", "15m", "1h", "4h"]
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// primaryTimeframe: 主时间周期（用于计算当前指标），默认使用 timeframes[0]
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// count: 每个时间周期的 K 线数量
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func GetWithTimeframes(symbol string, timeframes []string, primaryTimeframe string, count int) (*Data, error) {
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symbol = Normalize(symbol)
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if len(timeframes) == 0 {
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return nil, fmt.Errorf("至少需要一个时间周期")
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}
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// 如果未指定主周期，使用第一个
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if primaryTimeframe == "" {
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primaryTimeframe = timeframes[0]
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}
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// 确保主周期在列表中
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hasPrimary := false
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for _, tf := range timeframes {
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if tf == primaryTimeframe {
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hasPrimary = true
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break
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}
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}
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if !hasPrimary {
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timeframes = append([]string{primaryTimeframe}, timeframes...)
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}
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// 存储所有时间周期的数据
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timeframeData := make(map[string]*TimeframeSeriesData)
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var primaryKlines []Kline
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// 获取每个时间周期的 K 线数据
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for _, tf := range timeframes {
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klines, err := WSMonitorCli.GetCurrentKlines(symbol, tf)
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if err != nil {
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logger.Infof("⚠️ 获取 %s %s K线失败: %v", symbol, tf, err)
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continue
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}
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if len(klines) == 0 {
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logger.Infof("⚠️ %s %s K线数据为空", symbol, tf)
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continue
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}
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// 保存主周期的 K 线用于计算基础指标
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if tf == primaryTimeframe {
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primaryKlines = klines
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}
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// 计算该时间周期的系列数据
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seriesData := calculateTimeframeSeries(klines, tf)
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timeframeData[tf] = seriesData
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}
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// 如果主周期数据为空，返回错误
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if len(primaryKlines) == 0 {
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return nil, fmt.Errorf("主时间周期 %s K线数据为空", primaryTimeframe)
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}
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// Data staleness detection
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if isStaleData(primaryKlines, symbol) {
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logger.Infof("⚠️ WARNING: %s detected stale data (consecutive price freeze), skipping symbol", symbol)
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return nil, fmt.Errorf("%s data is stale, possible cache failure", symbol)
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}
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// 计算当前指标 (基于主周期最新数据)
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currentPrice := primaryKlines[len(primaryKlines)-1].Close
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currentEMA20 := calculateEMA(primaryKlines, 20)
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currentMACD := calculateMACD(primaryKlines)
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currentRSI7 := calculateRSI(primaryKlines, 7)
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// 计算价格变化
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priceChange1h := calculatePriceChangeByBars(primaryKlines, primaryTimeframe, 60) // 1小时
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priceChange4h := calculatePriceChangeByBars(primaryKlines, primaryTimeframe, 240) // 4小时
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// 获取OI数据
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oiData, err := getOpenInterestData(symbol)
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if err != nil {
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oiData = &OIData{Latest: 0, Average: 0}
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}
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// 获取Funding Rate
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fundingRate, _ := getFundingRate(symbol)
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return &Data{
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Symbol: symbol,
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CurrentPrice: currentPrice,
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PriceChange1h: priceChange1h,
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PriceChange4h: priceChange4h,
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CurrentEMA20: currentEMA20,
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CurrentMACD: currentMACD,
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CurrentRSI7: currentRSI7,
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OpenInterest: oiData,
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FundingRate: fundingRate,
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TimeframeData: timeframeData,
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}, nil
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}
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// calculateTimeframeSeries 计算单个时间周期的系列数据
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func calculateTimeframeSeries(klines []Kline, timeframe string) *TimeframeSeriesData {
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data := &TimeframeSeriesData{
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Timeframe: timeframe,
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MidPrices: make([]float64, 0, 10),
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EMA20Values: make([]float64, 0, 10),
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EMA50Values: make([]float64, 0, 10),
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MACDValues: make([]float64, 0, 10),
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RSI7Values: make([]float64, 0, 10),
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RSI14Values: make([]float64, 0, 10),
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Volume: make([]float64, 0, 10),
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}
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// 获取最近10个数据点
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start := len(klines) - 10
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if start < 0 {
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start = 0
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}
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for i := start; i < len(klines); i++ {
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data.MidPrices = append(data.MidPrices, klines[i].Close)
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data.Volume = append(data.Volume, klines[i].Volume)
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// 计算每个点的 EMA20
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if i >= 19 {
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ema20 := calculateEMA(klines[:i+1], 20)
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data.EMA20Values = append(data.EMA20Values, ema20)
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}
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// 计算每个点的 EMA50
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if i >= 49 {
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ema50 := calculateEMA(klines[:i+1], 50)
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data.EMA50Values = append(data.EMA50Values, ema50)
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}
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// 计算每个点的 MACD
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if i >= 25 {
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macd := calculateMACD(klines[:i+1])
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data.MACDValues = append(data.MACDValues, macd)
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}
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// 计算每个点的 RSI
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if i >= 7 {
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rsi7 := calculateRSI(klines[:i+1], 7)
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data.RSI7Values = append(data.RSI7Values, rsi7)
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}
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if i >= 14 {
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rsi14 := calculateRSI(klines[:i+1], 14)
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data.RSI14Values = append(data.RSI14Values, rsi14)
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}
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}
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// 计算 ATR14
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data.ATR14 = calculateATR(klines, 14)
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return data
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}
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// calculatePriceChangeByBars 根据时间周期计算需要回溯多少根 K 线来计算价格变化
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func calculatePriceChangeByBars(klines []Kline, timeframe string, targetMinutes int) float64 {
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if len(klines) < 2 {
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return 0
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}
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// 解析时间周期为分钟数
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tfMinutes := parseTimeframeToMinutes(timeframe)
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if tfMinutes <= 0 {
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return 0
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}
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// 计算需要回溯多少根 K 线
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barsBack := targetMinutes / tfMinutes
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if barsBack < 1 {
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barsBack = 1
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}
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currentPrice := klines[len(klines)-1].Close
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idx := len(klines) - 1 - barsBack
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if idx < 0 {
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idx = 0
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}
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oldPrice := klines[idx].Close
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if oldPrice > 0 {
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return ((currentPrice - oldPrice) / oldPrice) * 100
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}
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return 0
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}
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// parseTimeframeToMinutes 将时间周期字符串解析为分钟数
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func parseTimeframeToMinutes(tf string) int {
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switch tf {
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case "1m":
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return 1
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case "3m":
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return 3
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case "5m":
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return 5
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case "15m":
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return 15
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case "30m":
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return 30
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case "1h":
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return 60
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case "2h":
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return 120
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case "4h":
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return 240
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case "6h":
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return 360
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case "8h":
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return 480
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case "12h":
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return 720
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case "1d":
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return 1440
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case "3d":
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return 4320
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case "1w":
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return 10080
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default:
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return 0
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}
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}
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// calculateEMA 计算EMA
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func calculateEMA(klines []Kline, period int) float64 {
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if len(klines) < period {
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@@ -481,9 +705,54 @@ func Format(data *Data) string {
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}
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}
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// 多时间周期数据（新增）
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if len(data.TimeframeData) > 0 {
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// 按时间周期排序输出
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timeframeOrder := []string{"1m", "3m", "5m", "15m", "30m", "1h", "2h", "4h", "6h", "8h", "12h", "1d", "3d", "1w"}
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for _, tf := range timeframeOrder {
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if tfData, ok := data.TimeframeData[tf]; ok {
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sb.WriteString(fmt.Sprintf("=== %s Timeframe ===\n\n", strings.ToUpper(tf)))
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formatTimeframeData(&sb, tfData)
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}
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}
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}
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return sb.String()
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}
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// formatTimeframeData 格式化单个时间周期的数据
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func formatTimeframeData(sb *strings.Builder, data *TimeframeSeriesData) {
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if len(data.MidPrices) > 0 {
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sb.WriteString(fmt.Sprintf("Mid prices: %s\n\n", formatFloatSlice(data.MidPrices)))
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}
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if len(data.EMA20Values) > 0 {
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sb.WriteString(fmt.Sprintf("EMA indicators (20‑period): %s\n\n", formatFloatSlice(data.EMA20Values)))
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}
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if len(data.EMA50Values) > 0 {
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sb.WriteString(fmt.Sprintf("EMA indicators (50‑period): %s\n\n", formatFloatSlice(data.EMA50Values)))
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}
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if len(data.MACDValues) > 0 {
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sb.WriteString(fmt.Sprintf("MACD indicators: %s\n\n", formatFloatSlice(data.MACDValues)))
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}
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if len(data.RSI7Values) > 0 {
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sb.WriteString(fmt.Sprintf("RSI indicators (7‑Period): %s\n\n", formatFloatSlice(data.RSI7Values)))
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}
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if len(data.RSI14Values) > 0 {
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sb.WriteString(fmt.Sprintf("RSI indicators (14‑Period): %s\n\n", formatFloatSlice(data.RSI14Values)))
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}
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if len(data.Volume) > 0 {
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sb.WriteString(fmt.Sprintf("Volume: %s\n\n", formatFloatSlice(data.Volume)))
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}
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sb.WriteString(fmt.Sprintf("ATR (14‑period): %.3f\n\n", data.ATR14))
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}
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// formatPriceWithDynamicPrecision 根据价格区间动态选择精度
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// 这样可以完美支持从超低价 meme coin (< 0.0001) 到 BTC/ETH 的所有币种
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func formatPriceWithDynamicPrecision(price float64) string {
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@@ -15,6 +15,21 @@ type Data struct {
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FundingRate float64
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IntradaySeries *IntradayData
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LongerTermContext *LongerTermData
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// 多时间周期数据（新增）
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TimeframeData map[string]*TimeframeSeriesData `json:"timeframe_data,omitempty"`
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}
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// TimeframeSeriesData 单个时间周期的序列数据
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type TimeframeSeriesData struct {
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Timeframe string `json:"timeframe"` // 时间周期标识，如 "5m", "15m", "1h"
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MidPrices []float64 `json:"mid_prices"` // 价格序列
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EMA20Values []float64 `json:"ema20_values"` // EMA20 序列
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EMA50Values []float64 `json:"ema50_values"` // EMA50 序列
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MACDValues []float64 `json:"macd_values"` // MACD 序列
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RSI7Values []float64 `json:"rsi7_values"` // RSI7 序列
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RSI14Values []float64 `json:"rsi14_values"` // RSI14 序列
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Volume []float64 `json:"volume"` // 成交量序列
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ATR14 float64 `json:"atr14"` // ATR14
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}
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// OIData Open Interest数据
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