mirror of
https://github.com/NoFxAiOS/nofx.git
synced 2026-06-06 05:51:19 +08:00
913 lines
25 KiB
Go
913 lines
25 KiB
Go
package market
|
||
|
||
import (
|
||
"encoding/json"
|
||
"fmt"
|
||
"io"
|
||
"nofx/logger"
|
||
"math"
|
||
"strconv"
|
||
"strings"
|
||
"sync"
|
||
"time"
|
||
)
|
||
|
||
// FundingRateCache is the funding rate cache structure
|
||
// Binance Funding Rate only updates every 8 hours, using 1-hour cache can significantly reduce API calls
|
||
type FundingRateCache struct {
|
||
Rate float64
|
||
UpdatedAt time.Time
|
||
}
|
||
|
||
var (
|
||
fundingRateMap sync.Map // map[string]*FundingRateCache
|
||
frCacheTTL = 1 * time.Hour
|
||
)
|
||
|
||
// Get retrieves market data for the specified token
|
||
func Get(symbol string) (*Data, error) {
|
||
var klines3m, klines4h []Kline
|
||
var err error
|
||
// Normalize symbol
|
||
symbol = Normalize(symbol)
|
||
// Get 3-minute K-line data (latest 10)
|
||
klines3m, err = WSMonitorCli.GetCurrentKlines(symbol, "3m") // Get more for calculation
|
||
if err != nil {
|
||
return nil, fmt.Errorf("Failed to get 3-minute K-line: %v", err)
|
||
}
|
||
|
||
// Data staleness detection: Prevent DOGEUSDT-style price freeze issues
|
||
if isStaleData(klines3m, symbol) {
|
||
logger.Infof("⚠️ WARNING: %s detected stale data (consecutive price freeze), skipping symbol", symbol)
|
||
return nil, fmt.Errorf("%s data is stale, possible cache failure", symbol)
|
||
}
|
||
|
||
// Get 4-hour K-line data (latest 10)
|
||
klines4h, err = WSMonitorCli.GetCurrentKlines(symbol, "4h") // Get more for indicator calculation
|
||
if err != nil {
|
||
return nil, fmt.Errorf("Failed to get 4-hour K-line: %v", err)
|
||
}
|
||
|
||
// Check if data is empty
|
||
if len(klines3m) == 0 {
|
||
return nil, fmt.Errorf("3-minute K-line data is empty")
|
||
}
|
||
if len(klines4h) == 0 {
|
||
return nil, fmt.Errorf("4-hour K-line data is empty")
|
||
}
|
||
|
||
// Calculate current indicators (based on 3-minute latest data)
|
||
currentPrice := klines3m[len(klines3m)-1].Close
|
||
currentEMA20 := calculateEMA(klines3m, 20)
|
||
currentMACD := calculateMACD(klines3m)
|
||
currentRSI7 := calculateRSI(klines3m, 7)
|
||
|
||
// Calculate price change percentage
|
||
// 1-hour price change = price from 20 3-minute K-lines ago
|
||
priceChange1h := 0.0
|
||
if len(klines3m) >= 21 { // Need at least 21 K-lines (current + 20 previous)
|
||
price1hAgo := klines3m[len(klines3m)-21].Close
|
||
if price1hAgo > 0 {
|
||
priceChange1h = ((currentPrice - price1hAgo) / price1hAgo) * 100
|
||
}
|
||
}
|
||
|
||
// 4-hour price change = price from 1 4-hour K-line ago
|
||
priceChange4h := 0.0
|
||
if len(klines4h) >= 2 {
|
||
price4hAgo := klines4h[len(klines4h)-2].Close
|
||
if price4hAgo > 0 {
|
||
priceChange4h = ((currentPrice - price4hAgo) / price4hAgo) * 100
|
||
}
|
||
}
|
||
|
||
// Get OI data
|
||
oiData, err := getOpenInterestData(symbol)
|
||
if err != nil {
|
||
// OI failure doesn't affect overall result, use default values
|
||
oiData = &OIData{Latest: 0, Average: 0}
|
||
}
|
||
|
||
// Get Funding Rate
|
||
fundingRate, _ := getFundingRate(symbol)
|
||
|
||
// Calculate intraday series data
|
||
intradayData := calculateIntradaySeries(klines3m)
|
||
|
||
// Calculate longer-term data
|
||
longerTermData := calculateLongerTermData(klines4h)
|
||
|
||
return &Data{
|
||
Symbol: symbol,
|
||
CurrentPrice: currentPrice,
|
||
PriceChange1h: priceChange1h,
|
||
PriceChange4h: priceChange4h,
|
||
CurrentEMA20: currentEMA20,
|
||
CurrentMACD: currentMACD,
|
||
CurrentRSI7: currentRSI7,
|
||
OpenInterest: oiData,
|
||
FundingRate: fundingRate,
|
||
IntradaySeries: intradayData,
|
||
LongerTermContext: longerTermData,
|
||
}, nil
|
||
}
|
||
|
||
// GetWithTimeframes retrieves market data for specified multiple timeframes
|
||
// timeframes: list of timeframes, e.g. ["5m", "15m", "1h", "4h"]
|
||
// primaryTimeframe: primary timeframe (used for calculating current indicators), defaults to timeframes[0]
|
||
// count: number of K-lines for each timeframe
|
||
func GetWithTimeframes(symbol string, timeframes []string, primaryTimeframe string, count int) (*Data, error) {
|
||
symbol = Normalize(symbol)
|
||
|
||
if len(timeframes) == 0 {
|
||
return nil, fmt.Errorf("at least one timeframe is required")
|
||
}
|
||
|
||
// If primary timeframe is not specified, use the first one
|
||
if primaryTimeframe == "" {
|
||
primaryTimeframe = timeframes[0]
|
||
}
|
||
|
||
// Ensure primary timeframe is in the list
|
||
hasPrimary := false
|
||
for _, tf := range timeframes {
|
||
if tf == primaryTimeframe {
|
||
hasPrimary = true
|
||
break
|
||
}
|
||
}
|
||
if !hasPrimary {
|
||
timeframes = append([]string{primaryTimeframe}, timeframes...)
|
||
}
|
||
|
||
// Store data for all timeframes
|
||
timeframeData := make(map[string]*TimeframeSeriesData)
|
||
var primaryKlines []Kline
|
||
|
||
// Get K-line data for each timeframe
|
||
for _, tf := range timeframes {
|
||
klines, err := WSMonitorCli.GetCurrentKlines(symbol, tf)
|
||
if err != nil {
|
||
logger.Infof("⚠️ Failed to get %s %s K-line: %v", symbol, tf, err)
|
||
continue
|
||
}
|
||
|
||
if len(klines) == 0 {
|
||
logger.Infof("⚠️ %s %s K-line data is empty", symbol, tf)
|
||
continue
|
||
}
|
||
|
||
// Save primary timeframe K-lines for calculating base indicators
|
||
if tf == primaryTimeframe {
|
||
primaryKlines = klines
|
||
}
|
||
|
||
// Calculate series data for this timeframe
|
||
seriesData := calculateTimeframeSeries(klines, tf)
|
||
timeframeData[tf] = seriesData
|
||
}
|
||
|
||
// If primary timeframe data is empty, return error
|
||
if len(primaryKlines) == 0 {
|
||
return nil, fmt.Errorf("Primary timeframe %s K-line data is empty", primaryTimeframe)
|
||
}
|
||
|
||
// Data staleness detection
|
||
if isStaleData(primaryKlines, symbol) {
|
||
logger.Infof("⚠️ WARNING: %s detected stale data (consecutive price freeze), skipping symbol", symbol)
|
||
return nil, fmt.Errorf("%s data is stale, possible cache failure", symbol)
|
||
}
|
||
|
||
// Calculate current indicators (based on primary timeframe latest data)
|
||
currentPrice := primaryKlines[len(primaryKlines)-1].Close
|
||
currentEMA20 := calculateEMA(primaryKlines, 20)
|
||
currentMACD := calculateMACD(primaryKlines)
|
||
currentRSI7 := calculateRSI(primaryKlines, 7)
|
||
|
||
// Calculate price changes
|
||
priceChange1h := calculatePriceChangeByBars(primaryKlines, primaryTimeframe, 60) // 1 hour
|
||
priceChange4h := calculatePriceChangeByBars(primaryKlines, primaryTimeframe, 240) // 4 hours
|
||
|
||
// Get OI data
|
||
oiData, err := getOpenInterestData(symbol)
|
||
if err != nil {
|
||
oiData = &OIData{Latest: 0, Average: 0}
|
||
}
|
||
|
||
// Get Funding Rate
|
||
fundingRate, _ := getFundingRate(symbol)
|
||
|
||
return &Data{
|
||
Symbol: symbol,
|
||
CurrentPrice: currentPrice,
|
||
PriceChange1h: priceChange1h,
|
||
PriceChange4h: priceChange4h,
|
||
CurrentEMA20: currentEMA20,
|
||
CurrentMACD: currentMACD,
|
||
CurrentRSI7: currentRSI7,
|
||
OpenInterest: oiData,
|
||
FundingRate: fundingRate,
|
||
TimeframeData: timeframeData,
|
||
}, nil
|
||
}
|
||
|
||
// calculateTimeframeSeries calculates series data for a single timeframe
|
||
func calculateTimeframeSeries(klines []Kline, timeframe string) *TimeframeSeriesData {
|
||
data := &TimeframeSeriesData{
|
||
Timeframe: timeframe,
|
||
MidPrices: make([]float64, 0, 10),
|
||
EMA20Values: make([]float64, 0, 10),
|
||
EMA50Values: make([]float64, 0, 10),
|
||
MACDValues: make([]float64, 0, 10),
|
||
RSI7Values: make([]float64, 0, 10),
|
||
RSI14Values: make([]float64, 0, 10),
|
||
Volume: make([]float64, 0, 10),
|
||
}
|
||
|
||
// Get latest 10 data points
|
||
start := len(klines) - 10
|
||
if start < 0 {
|
||
start = 0
|
||
}
|
||
|
||
for i := start; i < len(klines); i++ {
|
||
data.MidPrices = append(data.MidPrices, klines[i].Close)
|
||
data.Volume = append(data.Volume, klines[i].Volume)
|
||
|
||
// Calculate EMA20 for each point
|
||
if i >= 19 {
|
||
ema20 := calculateEMA(klines[:i+1], 20)
|
||
data.EMA20Values = append(data.EMA20Values, ema20)
|
||
}
|
||
|
||
// Calculate EMA50 for each point
|
||
if i >= 49 {
|
||
ema50 := calculateEMA(klines[:i+1], 50)
|
||
data.EMA50Values = append(data.EMA50Values, ema50)
|
||
}
|
||
|
||
// Calculate MACD for each point
|
||
if i >= 25 {
|
||
macd := calculateMACD(klines[:i+1])
|
||
data.MACDValues = append(data.MACDValues, macd)
|
||
}
|
||
|
||
// Calculate RSI for each point
|
||
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)
|
||
}
|
||
}
|
||
|
||
// Calculate ATR14
|
||
data.ATR14 = calculateATR(klines, 14)
|
||
|
||
return data
|
||
}
|
||
|
||
// calculatePriceChangeByBars calculates how many K-lines to look back for price change based on timeframe
|
||
func calculatePriceChangeByBars(klines []Kline, timeframe string, targetMinutes int) float64 {
|
||
if len(klines) < 2 {
|
||
return 0
|
||
}
|
||
|
||
// Parse timeframe to minutes
|
||
tfMinutes := parseTimeframeToMinutes(timeframe)
|
||
if tfMinutes <= 0 {
|
||
return 0
|
||
}
|
||
|
||
// Calculate how many K-lines to look back
|
||
barsBack := targetMinutes / tfMinutes
|
||
if barsBack < 1 {
|
||
barsBack = 1
|
||
}
|
||
|
||
currentPrice := klines[len(klines)-1].Close
|
||
idx := len(klines) - 1 - barsBack
|
||
if idx < 0 {
|
||
idx = 0
|
||
}
|
||
|
||
oldPrice := klines[idx].Close
|
||
if oldPrice > 0 {
|
||
return ((currentPrice - oldPrice) / oldPrice) * 100
|
||
}
|
||
return 0
|
||
}
|
||
|
||
// parseTimeframeToMinutes parses timeframe string to minutes
|
||
func parseTimeframeToMinutes(tf string) int {
|
||
switch tf {
|
||
case "1m":
|
||
return 1
|
||
case "3m":
|
||
return 3
|
||
case "5m":
|
||
return 5
|
||
case "15m":
|
||
return 15
|
||
case "30m":
|
||
return 30
|
||
case "1h":
|
||
return 60
|
||
case "2h":
|
||
return 120
|
||
case "4h":
|
||
return 240
|
||
case "6h":
|
||
return 360
|
||
case "8h":
|
||
return 480
|
||
case "12h":
|
||
return 720
|
||
case "1d":
|
||
return 1440
|
||
case "3d":
|
||
return 4320
|
||
case "1w":
|
||
return 10080
|
||
default:
|
||
return 0
|
||
}
|
||
}
|
||
|
||
// calculateEMA calculates EMA
|
||
func calculateEMA(klines []Kline, period int) float64 {
|
||
if len(klines) < period {
|
||
return 0
|
||
}
|
||
|
||
// Calculate SMA as initial EMA
|
||
sum := 0.0
|
||
for i := 0; i < period; i++ {
|
||
sum += klines[i].Close
|
||
}
|
||
ema := sum / float64(period)
|
||
|
||
// Calculate EMA
|
||
multiplier := 2.0 / float64(period+1)
|
||
for i := period; i < len(klines); i++ {
|
||
ema = (klines[i].Close-ema)*multiplier + ema
|
||
}
|
||
|
||
return ema
|
||
}
|
||
|
||
// calculateMACD calculates MACD
|
||
func calculateMACD(klines []Kline) float64 {
|
||
if len(klines) < 26 {
|
||
return 0
|
||
}
|
||
|
||
// Calculate 12-period and 26-period EMA
|
||
ema12 := calculateEMA(klines, 12)
|
||
ema26 := calculateEMA(klines, 26)
|
||
|
||
// MACD = EMA12 - EMA26
|
||
return ema12 - ema26
|
||
}
|
||
|
||
// calculateRSI calculates RSI
|
||
func calculateRSI(klines []Kline, period int) float64 {
|
||
if len(klines) <= period {
|
||
return 0
|
||
}
|
||
|
||
gains := 0.0
|
||
losses := 0.0
|
||
|
||
// Calculate initial average gain/loss
|
||
for i := 1; i <= period; i++ {
|
||
change := klines[i].Close - klines[i-1].Close
|
||
if change > 0 {
|
||
gains += change
|
||
} else {
|
||
losses += -change
|
||
}
|
||
}
|
||
|
||
avgGain := gains / float64(period)
|
||
avgLoss := losses / float64(period)
|
||
|
||
// Use Wilder smoothing method to calculate subsequent RSI
|
||
for i := period + 1; i < len(klines); i++ {
|
||
change := klines[i].Close - klines[i-1].Close
|
||
if change > 0 {
|
||
avgGain = (avgGain*float64(period-1) + change) / float64(period)
|
||
avgLoss = (avgLoss * float64(period-1)) / float64(period)
|
||
} else {
|
||
avgGain = (avgGain * float64(period-1)) / float64(period)
|
||
avgLoss = (avgLoss*float64(period-1) + (-change)) / float64(period)
|
||
}
|
||
}
|
||
|
||
if avgLoss == 0 {
|
||
return 100
|
||
}
|
||
|
||
rs := avgGain / avgLoss
|
||
rsi := 100 - (100 / (1 + rs))
|
||
|
||
return rsi
|
||
}
|
||
|
||
// calculateATR calculates ATR
|
||
func calculateATR(klines []Kline, period int) float64 {
|
||
if len(klines) <= period {
|
||
return 0
|
||
}
|
||
|
||
trs := make([]float64, len(klines))
|
||
for i := 1; i < len(klines); i++ {
|
||
high := klines[i].High
|
||
low := klines[i].Low
|
||
prevClose := klines[i-1].Close
|
||
|
||
tr1 := high - low
|
||
tr2 := math.Abs(high - prevClose)
|
||
tr3 := math.Abs(low - prevClose)
|
||
|
||
trs[i] = math.Max(tr1, math.Max(tr2, tr3))
|
||
}
|
||
|
||
// Calculate initial ATR
|
||
sum := 0.0
|
||
for i := 1; i <= period; i++ {
|
||
sum += trs[i]
|
||
}
|
||
atr := sum / float64(period)
|
||
|
||
// Wilder smoothing
|
||
for i := period + 1; i < len(klines); i++ {
|
||
atr = (atr*float64(period-1) + trs[i]) / float64(period)
|
||
}
|
||
|
||
return atr
|
||
}
|
||
|
||
// calculateIntradaySeries calculates intraday series data
|
||
func calculateIntradaySeries(klines []Kline) *IntradayData {
|
||
data := &IntradayData{
|
||
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),
|
||
Volume: make([]float64, 0, 10),
|
||
}
|
||
|
||
// Get latest 10 data points
|
||
start := len(klines) - 10
|
||
if start < 0 {
|
||
start = 0
|
||
}
|
||
|
||
for i := start; i < len(klines); i++ {
|
||
data.MidPrices = append(data.MidPrices, klines[i].Close)
|
||
data.Volume = append(data.Volume, klines[i].Volume)
|
||
|
||
// Calculate EMA20 for each point
|
||
if i >= 19 {
|
||
ema20 := calculateEMA(klines[:i+1], 20)
|
||
data.EMA20Values = append(data.EMA20Values, ema20)
|
||
}
|
||
|
||
// Calculate MACD for each point
|
||
if i >= 25 {
|
||
macd := calculateMACD(klines[:i+1])
|
||
data.MACDValues = append(data.MACDValues, macd)
|
||
}
|
||
|
||
// Calculate RSI for each point
|
||
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)
|
||
}
|
||
}
|
||
|
||
// Calculate 3m ATR14
|
||
data.ATR14 = calculateATR(klines, 14)
|
||
|
||
return data
|
||
}
|
||
|
||
// calculateLongerTermData calculates longer-term data
|
||
func calculateLongerTermData(klines []Kline) *LongerTermData {
|
||
data := &LongerTermData{
|
||
MACDValues: make([]float64, 0, 10),
|
||
RSI14Values: make([]float64, 0, 10),
|
||
}
|
||
|
||
// Calculate EMA
|
||
data.EMA20 = calculateEMA(klines, 20)
|
||
data.EMA50 = calculateEMA(klines, 50)
|
||
|
||
// Calculate ATR
|
||
data.ATR3 = calculateATR(klines, 3)
|
||
data.ATR14 = calculateATR(klines, 14)
|
||
|
||
// Calculate volume
|
||
if len(klines) > 0 {
|
||
data.CurrentVolume = klines[len(klines)-1].Volume
|
||
// Calculate average volume
|
||
sum := 0.0
|
||
for _, k := range klines {
|
||
sum += k.Volume
|
||
}
|
||
data.AverageVolume = sum / float64(len(klines))
|
||
}
|
||
|
||
// Calculate MACD and RSI series
|
||
start := len(klines) - 10
|
||
if start < 0 {
|
||
start = 0
|
||
}
|
||
|
||
for i := start; i < len(klines); i++ {
|
||
if i >= 25 {
|
||
macd := calculateMACD(klines[:i+1])
|
||
data.MACDValues = append(data.MACDValues, macd)
|
||
}
|
||
if i >= 14 {
|
||
rsi14 := calculateRSI(klines[:i+1], 14)
|
||
data.RSI14Values = append(data.RSI14Values, rsi14)
|
||
}
|
||
}
|
||
|
||
return data
|
||
}
|
||
|
||
// getOpenInterestData retrieves OI data
|
||
func getOpenInterestData(symbol string) (*OIData, error) {
|
||
url := fmt.Sprintf("https://fapi.binance.com/fapi/v1/openInterest?symbol=%s", symbol)
|
||
|
||
apiClient := NewAPIClient()
|
||
resp, err := apiClient.client.Get(url)
|
||
if err != nil {
|
||
return nil, err
|
||
}
|
||
defer resp.Body.Close()
|
||
|
||
body, err := io.ReadAll(resp.Body)
|
||
if err != nil {
|
||
return nil, err
|
||
}
|
||
|
||
var result struct {
|
||
OpenInterest string `json:"openInterest"`
|
||
Symbol string `json:"symbol"`
|
||
Time int64 `json:"time"`
|
||
}
|
||
|
||
if err := json.Unmarshal(body, &result); err != nil {
|
||
return nil, err
|
||
}
|
||
|
||
oi, _ := strconv.ParseFloat(result.OpenInterest, 64)
|
||
|
||
return &OIData{
|
||
Latest: oi,
|
||
Average: oi * 0.999, // Approximate average
|
||
}, nil
|
||
}
|
||
|
||
// getFundingRate retrieves funding rate (optimized: uses 1-hour cache)
|
||
func getFundingRate(symbol string) (float64, error) {
|
||
// Check cache (1-hour validity)
|
||
// Funding Rate only updates every 8 hours, 1-hour cache is very reasonable
|
||
if cached, ok := fundingRateMap.Load(symbol); ok {
|
||
cache := cached.(*FundingRateCache)
|
||
if time.Since(cache.UpdatedAt) < frCacheTTL {
|
||
// Cache hit, return directly
|
||
return cache.Rate, nil
|
||
}
|
||
}
|
||
|
||
// Cache expired or doesn't exist, call API
|
||
url := fmt.Sprintf("https://fapi.binance.com/fapi/v1/premiumIndex?symbol=%s", symbol)
|
||
|
||
apiClient := NewAPIClient()
|
||
resp, err := apiClient.client.Get(url)
|
||
if err != nil {
|
||
return 0, err
|
||
}
|
||
defer resp.Body.Close()
|
||
|
||
body, err := io.ReadAll(resp.Body)
|
||
if err != nil {
|
||
return 0, err
|
||
}
|
||
|
||
var result struct {
|
||
Symbol string `json:"symbol"`
|
||
MarkPrice string `json:"markPrice"`
|
||
IndexPrice string `json:"indexPrice"`
|
||
LastFundingRate string `json:"lastFundingRate"`
|
||
NextFundingTime int64 `json:"nextFundingTime"`
|
||
InterestRate string `json:"interestRate"`
|
||
Time int64 `json:"time"`
|
||
}
|
||
|
||
if err := json.Unmarshal(body, &result); err != nil {
|
||
return 0, err
|
||
}
|
||
|
||
rate, _ := strconv.ParseFloat(result.LastFundingRate, 64)
|
||
|
||
// Update cache
|
||
fundingRateMap.Store(symbol, &FundingRateCache{
|
||
Rate: rate,
|
||
UpdatedAt: time.Now(),
|
||
})
|
||
|
||
return rate, nil
|
||
}
|
||
|
||
// Format formats and outputs market data
|
||
func Format(data *Data) string {
|
||
var sb strings.Builder
|
||
|
||
// Format price with dynamic precision
|
||
priceStr := formatPriceWithDynamicPrecision(data.CurrentPrice)
|
||
sb.WriteString(fmt.Sprintf("current_price = %s, current_ema20 = %.3f, current_macd = %.3f, current_rsi (7 period) = %.3f\n\n",
|
||
priceStr, data.CurrentEMA20, data.CurrentMACD, data.CurrentRSI7))
|
||
|
||
sb.WriteString(fmt.Sprintf("In addition, here is the latest %s open interest and funding rate for perps:\n\n",
|
||
data.Symbol))
|
||
|
||
if data.OpenInterest != nil {
|
||
// Format OI data with dynamic precision
|
||
oiLatestStr := formatPriceWithDynamicPrecision(data.OpenInterest.Latest)
|
||
oiAverageStr := formatPriceWithDynamicPrecision(data.OpenInterest.Average)
|
||
sb.WriteString(fmt.Sprintf("Open Interest: Latest: %s Average: %s\n\n",
|
||
oiLatestStr, oiAverageStr))
|
||
}
|
||
|
||
sb.WriteString(fmt.Sprintf("Funding Rate: %.2e\n\n", data.FundingRate))
|
||
|
||
if data.IntradaySeries != nil {
|
||
sb.WriteString("Intraday series (3‑minute intervals, oldest → latest):\n\n")
|
||
|
||
if len(data.IntradaySeries.MidPrices) > 0 {
|
||
sb.WriteString(fmt.Sprintf("Mid prices: %s\n\n", formatFloatSlice(data.IntradaySeries.MidPrices)))
|
||
}
|
||
|
||
if len(data.IntradaySeries.EMA20Values) > 0 {
|
||
sb.WriteString(fmt.Sprintf("EMA indicators (20‑period): %s\n\n", formatFloatSlice(data.IntradaySeries.EMA20Values)))
|
||
}
|
||
|
||
if len(data.IntradaySeries.MACDValues) > 0 {
|
||
sb.WriteString(fmt.Sprintf("MACD indicators: %s\n\n", formatFloatSlice(data.IntradaySeries.MACDValues)))
|
||
}
|
||
|
||
if len(data.IntradaySeries.RSI7Values) > 0 {
|
||
sb.WriteString(fmt.Sprintf("RSI indicators (7‑Period): %s\n\n", formatFloatSlice(data.IntradaySeries.RSI7Values)))
|
||
}
|
||
|
||
if len(data.IntradaySeries.RSI14Values) > 0 {
|
||
sb.WriteString(fmt.Sprintf("RSI indicators (14‑Period): %s\n\n", formatFloatSlice(data.IntradaySeries.RSI14Values)))
|
||
}
|
||
|
||
if len(data.IntradaySeries.Volume) > 0 {
|
||
sb.WriteString(fmt.Sprintf("Volume: %s\n\n", formatFloatSlice(data.IntradaySeries.Volume)))
|
||
}
|
||
|
||
sb.WriteString(fmt.Sprintf("3m ATR (14‑period): %.3f\n\n", data.IntradaySeries.ATR14))
|
||
}
|
||
|
||
if data.LongerTermContext != nil {
|
||
sb.WriteString("Longer‑term context (4‑hour timeframe):\n\n")
|
||
|
||
sb.WriteString(fmt.Sprintf("20‑Period EMA: %.3f vs. 50‑Period EMA: %.3f\n\n",
|
||
data.LongerTermContext.EMA20, data.LongerTermContext.EMA50))
|
||
|
||
sb.WriteString(fmt.Sprintf("3‑Period ATR: %.3f vs. 14‑Period ATR: %.3f\n\n",
|
||
data.LongerTermContext.ATR3, data.LongerTermContext.ATR14))
|
||
|
||
sb.WriteString(fmt.Sprintf("Current Volume: %.3f vs. Average Volume: %.3f\n\n",
|
||
data.LongerTermContext.CurrentVolume, data.LongerTermContext.AverageVolume))
|
||
|
||
if len(data.LongerTermContext.MACDValues) > 0 {
|
||
sb.WriteString(fmt.Sprintf("MACD indicators: %s\n\n", formatFloatSlice(data.LongerTermContext.MACDValues)))
|
||
}
|
||
|
||
if len(data.LongerTermContext.RSI14Values) > 0 {
|
||
sb.WriteString(fmt.Sprintf("RSI indicators (14‑Period): %s\n\n", formatFloatSlice(data.LongerTermContext.RSI14Values)))
|
||
}
|
||
}
|
||
|
||
// Multi-timeframe data (new)
|
||
if len(data.TimeframeData) > 0 {
|
||
// Output sorted by timeframe
|
||
timeframeOrder := []string{"1m", "3m", "5m", "15m", "30m", "1h", "2h", "4h", "6h", "8h", "12h", "1d", "3d", "1w"}
|
||
for _, tf := range timeframeOrder {
|
||
if tfData, ok := data.TimeframeData[tf]; ok {
|
||
sb.WriteString(fmt.Sprintf("=== %s Timeframe ===\n\n", strings.ToUpper(tf)))
|
||
formatTimeframeData(&sb, tfData)
|
||
}
|
||
}
|
||
}
|
||
|
||
return sb.String()
|
||
}
|
||
|
||
// formatTimeframeData formats data for a single timeframe
|
||
func formatTimeframeData(sb *strings.Builder, data *TimeframeSeriesData) {
|
||
if len(data.MidPrices) > 0 {
|
||
sb.WriteString(fmt.Sprintf("Mid prices: %s\n\n", formatFloatSlice(data.MidPrices)))
|
||
}
|
||
|
||
if len(data.EMA20Values) > 0 {
|
||
sb.WriteString(fmt.Sprintf("EMA indicators (20‑period): %s\n\n", formatFloatSlice(data.EMA20Values)))
|
||
}
|
||
|
||
if len(data.EMA50Values) > 0 {
|
||
sb.WriteString(fmt.Sprintf("EMA indicators (50‑period): %s\n\n", formatFloatSlice(data.EMA50Values)))
|
||
}
|
||
|
||
if len(data.MACDValues) > 0 {
|
||
sb.WriteString(fmt.Sprintf("MACD indicators: %s\n\n", formatFloatSlice(data.MACDValues)))
|
||
}
|
||
|
||
if len(data.RSI7Values) > 0 {
|
||
sb.WriteString(fmt.Sprintf("RSI indicators (7‑Period): %s\n\n", formatFloatSlice(data.RSI7Values)))
|
||
}
|
||
|
||
if len(data.RSI14Values) > 0 {
|
||
sb.WriteString(fmt.Sprintf("RSI indicators (14‑Period): %s\n\n", formatFloatSlice(data.RSI14Values)))
|
||
}
|
||
|
||
if len(data.Volume) > 0 {
|
||
sb.WriteString(fmt.Sprintf("Volume: %s\n\n", formatFloatSlice(data.Volume)))
|
||
}
|
||
|
||
sb.WriteString(fmt.Sprintf("ATR (14‑period): %.3f\n\n", data.ATR14))
|
||
}
|
||
|
||
// formatPriceWithDynamicPrecision dynamically selects precision based on price range
|
||
// This perfectly supports all coins from ultra-low price meme coins (< 0.0001) to BTC/ETH
|
||
func formatPriceWithDynamicPrecision(price float64) string {
|
||
switch {
|
||
case price < 0.0001:
|
||
// Ultra-low price meme coins: 1000SATS, 1000WHY, DOGS
|
||
// 0.00002070 → "0.00002070" (8 decimal places)
|
||
return fmt.Sprintf("%.8f", price)
|
||
case price < 0.001:
|
||
// Low price meme coins: NEIRO, HMSTR, HOT, NOT
|
||
// 0.00015060 → "0.000151" (6 decimal places)
|
||
return fmt.Sprintf("%.6f", price)
|
||
case price < 0.01:
|
||
// Mid-low price coins: PEPE, SHIB, MEME
|
||
// 0.00556800 → "0.005568" (6 decimal places)
|
||
return fmt.Sprintf("%.6f", price)
|
||
case price < 1.0:
|
||
// Low price coins: ASTER, DOGE, ADA, TRX
|
||
// 0.9954 → "0.9954" (4 decimal places)
|
||
return fmt.Sprintf("%.4f", price)
|
||
case price < 100:
|
||
// Mid price coins: SOL, AVAX, LINK, MATIC
|
||
// 23.4567 → "23.4567" (4 decimal places)
|
||
return fmt.Sprintf("%.4f", price)
|
||
default:
|
||
// High price coins: BTC, ETH (save tokens)
|
||
// 45678.9123 → "45678.91" (2 decimal places)
|
||
return fmt.Sprintf("%.2f", price)
|
||
}
|
||
}
|
||
|
||
// formatFloatSlice formats float64 slice to string (using dynamic precision)
|
||
func formatFloatSlice(values []float64) string {
|
||
strValues := make([]string, len(values))
|
||
for i, v := range values {
|
||
strValues[i] = formatPriceWithDynamicPrecision(v)
|
||
}
|
||
return "[" + strings.Join(strValues, ", ") + "]"
|
||
}
|
||
|
||
// Normalize normalizes symbol, ensures it's a USDT trading pair
|
||
func Normalize(symbol string) string {
|
||
symbol = strings.ToUpper(symbol)
|
||
if strings.HasSuffix(symbol, "USDT") {
|
||
return symbol
|
||
}
|
||
return symbol + "USDT"
|
||
}
|
||
|
||
// parseFloat parses float value
|
||
func parseFloat(v interface{}) (float64, error) {
|
||
switch val := v.(type) {
|
||
case string:
|
||
return strconv.ParseFloat(val, 64)
|
||
case float64:
|
||
return val, nil
|
||
case int:
|
||
return float64(val), nil
|
||
case int64:
|
||
return float64(val), nil
|
||
default:
|
||
return 0, fmt.Errorf("unsupported type: %T", v)
|
||
}
|
||
}
|
||
|
||
// BuildDataFromKlines constructs market data snapshot from preloaded K-line series (for backtesting/simulation).
|
||
func BuildDataFromKlines(symbol string, primary []Kline, longer []Kline) (*Data, error) {
|
||
if len(primary) == 0 {
|
||
return nil, fmt.Errorf("primary series is empty")
|
||
}
|
||
|
||
symbol = Normalize(symbol)
|
||
current := primary[len(primary)-1]
|
||
currentPrice := current.Close
|
||
|
||
data := &Data{
|
||
Symbol: symbol,
|
||
CurrentPrice: currentPrice,
|
||
CurrentEMA20: calculateEMA(primary, 20),
|
||
CurrentMACD: calculateMACD(primary),
|
||
CurrentRSI7: calculateRSI(primary, 7),
|
||
PriceChange1h: priceChangeFromSeries(primary, time.Hour),
|
||
PriceChange4h: priceChangeFromSeries(primary, 4*time.Hour),
|
||
OpenInterest: &OIData{Latest: 0, Average: 0},
|
||
FundingRate: 0,
|
||
IntradaySeries: calculateIntradaySeries(primary),
|
||
LongerTermContext: nil,
|
||
}
|
||
|
||
if len(longer) > 0 {
|
||
data.LongerTermContext = calculateLongerTermData(longer)
|
||
}
|
||
|
||
return data, nil
|
||
}
|
||
|
||
func priceChangeFromSeries(series []Kline, duration time.Duration) float64 {
|
||
if len(series) == 0 || duration <= 0 {
|
||
return 0
|
||
}
|
||
last := series[len(series)-1]
|
||
target := last.CloseTime - duration.Milliseconds()
|
||
for i := len(series) - 1; i >= 0; i-- {
|
||
if series[i].CloseTime <= target {
|
||
price := series[i].Close
|
||
if price > 0 {
|
||
return ((last.Close - price) / price) * 100
|
||
}
|
||
break
|
||
}
|
||
}
|
||
return 0
|
||
}
|
||
|
||
// isStaleData detects stale data (consecutive price freeze)
|
||
// Fix DOGEUSDT-style issue: consecutive N periods with completely unchanged prices indicate data source anomaly
|
||
func isStaleData(klines []Kline, symbol string) bool {
|
||
if len(klines) < 5 {
|
||
return false // Insufficient data to determine
|
||
}
|
||
|
||
// Detection threshold: 5 consecutive 3-minute periods with unchanged price (15 minutes without fluctuation)
|
||
const stalePriceThreshold = 5
|
||
const priceTolerancePct = 0.0001 // 0.01% fluctuation tolerance (avoid false positives)
|
||
|
||
// Take the last stalePriceThreshold K-lines
|
||
recentKlines := klines[len(klines)-stalePriceThreshold:]
|
||
firstPrice := recentKlines[0].Close
|
||
|
||
// Check if all prices are within tolerance
|
||
for i := 1; i < len(recentKlines); i++ {
|
||
priceDiff := math.Abs(recentKlines[i].Close-firstPrice) / firstPrice
|
||
if priceDiff > priceTolerancePct {
|
||
return false // Price fluctuation exists, data is normal
|
||
}
|
||
}
|
||
|
||
// Additional check: MACD and volume
|
||
// If price is unchanged but MACD/volume shows normal fluctuation, it might be a real market situation (extremely low volatility)
|
||
// Check if volume is also 0 (data completely frozen)
|
||
allVolumeZero := true
|
||
for _, k := range recentKlines {
|
||
if k.Volume > 0 {
|
||
allVolumeZero = false
|
||
break
|
||
}
|
||
}
|
||
|
||
if allVolumeZero {
|
||
logger.Infof("⚠️ %s stale data confirmed: price freeze + zero volume", symbol)
|
||
return true
|
||
}
|
||
|
||
// Price frozen but has volume: might be extremely low volatility market, allow but log warning
|
||
logger.Infof("⚠️ %s detected extreme price stability (no fluctuation for %d consecutive periods), but volume is normal", symbol, stalePriceThreshold)
|
||
return false
|
||
}
|