liuyu/nofx
1
0
Fork 0
mirror of https://github.com/NoFxAiOS/nofx.git synced 2026-07-16 17:12:25 +08:00
Code Issues Packages Projects Releases Wiki Activity

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

Browse Source 
## 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
This commit is contained in:
ZhouYongyou
2025-11-03 19:17:12 +08:00
parent bead75ef8f
commit 8d12514498
3 changed files with 231 additions and 30 deletions
Download Patch File Download Diff File Expand all files Collapse all files

171
market/data.go
View File

@@ -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")