Files
nofx/nofxi/internal/perception/news.go
shinchan-zhai 3e5b280987 feat(nofxi): proactive intelligence - Sentinel, Brain, Learner
🧠 Brain (proactive intelligence):
- Receives signals from Sentinel and decides when to notify
- Signal debouncing (same type+symbol within 10min)
- Morning brief at 08:30, evening brief at 20:30 (AI-generated)
- Crypto news scanning every 5 minutes
- Auto-filters high-impact news by sentiment + relevance

👁️ Sentinel (market anomaly detection):
- Watches BTC/ETH/SOL by default (expandable)
- Price breakout detection (>3% in 5 minutes)
- Volume spike detection (>3x average)
- Funding rate anomaly detection (>0.1%)
- 60-second scan interval, 1-hour price history buffer

📰 News Monitor:
- CryptoCompare API (free, no auth)
- Keyword-based sentiment classification (bullish/bearish/neutral)
- Symbol extraction from headlines
- Deduplication via seen URLs

📚 Learner (trading memory):
- User profile analysis (win rate, preferred side, risk tolerance)
- Trading lessons storage (win/loss patterns, strategy notes)
- AI prediction tracking (log predictions → resolve with actual P/L)
- Prediction accuracy metrics per model

NOFXi is no longer a passive chatbot. It watches, thinks, learns, and acts.
2026-03-22 23:02:46 +08:00

137 lines
3.4 KiB
Go

package perception
import (
"encoding/json"
"fmt"
"io"
"log/slog"
"net/http"
"strings"
"time"
)
// NewsItem represents a crypto news headline.
type NewsItem struct {
Title string `json:"title"`
Source string `json:"source"`
URL string `json:"url"`
Sentiment string `json:"sentiment"` // "bullish", "bearish", "neutral"
Symbols []string `json:"symbols"` // Related symbols
Timestamp time.Time `json:"timestamp"`
}
// NewsMonitor fetches crypto news and detects sentiment shifts.
type NewsMonitor struct {
httpClient *http.Client
logger *slog.Logger
lastCheck time.Time
seenURLs map[string]bool
}
// NewNewsMonitor creates a new news monitor.
func NewNewsMonitor(logger *slog.Logger) *NewsMonitor {
return &NewsMonitor{
httpClient: &http.Client{Timeout: 15 * time.Second},
logger: logger,
seenURLs: make(map[string]bool),
}
}
// FetchNews gets recent crypto news from CryptoCompare (free, no auth needed).
func (n *NewsMonitor) FetchNews() ([]NewsItem, error) {
url := "https://min-api.cryptocompare.com/data/v2/news/?lang=EN&sortOrder=latest"
resp, err := n.httpClient.Get(url)
if err != nil {
return nil, fmt.Errorf("fetch news: %w", err)
}
defer resp.Body.Close()
body, err := io.ReadAll(resp.Body)
if err != nil {
return nil, err
}
var result struct {
Data []struct {
Title string `json:"title"`
Source string `json:"source"`
URL string `json:"url"`
Body string `json:"body"`
Categories string `json:"categories"`
PublishedOn int64 `json:"published_on"`
} `json:"Data"`
}
if err := json.Unmarshal(body, &result); err != nil {
return nil, fmt.Errorf("parse news: %w", err)
}
var items []NewsItem
for _, d := range result.Data {
if n.seenURLs[d.URL] {
continue
}
n.seenURLs[d.URL] = true
item := NewsItem{
Title: d.Title,
Source: d.Source,
URL: d.URL,
Sentiment: classifySentiment(d.Title + " " + d.Body),
Symbols: extractSymbols(d.Title + " " + d.Categories),
Timestamp: time.Unix(d.PublishedOn, 0),
}
items = append(items, item)
}
// Keep seen URLs map from growing forever
if len(n.seenURLs) > 1000 {
n.seenURLs = make(map[string]bool)
}
n.lastCheck = time.Now()
return items, nil
}
// classifySentiment does basic keyword-based sentiment analysis.
func classifySentiment(text string) string {
lower := strings.ToLower(text)
bullish := []string{"surge", "rally", "soar", "bullish", "breakout", "all-time high", "ath",
"pump", "moon", "gain", "rise", "uptrend", "buy signal", "accumulate", "adoption"}
bearish := []string{"crash", "dump", "plunge", "bearish", "sell-off", "selloff", "decline",
"drop", "fall", "liquidat", "hack", "exploit", "ban", "fraud", "scam", "risk"}
bullCount, bearCount := 0, 0
for _, w := range bullish {
if strings.Contains(lower, w) {
bullCount++
}
}
for _, w := range bearish {
if strings.Contains(lower, w) {
bearCount++
}
}
if bullCount > bearCount {
return "bullish"
}
if bearCount > bullCount {
return "bearish"
}
return "neutral"
}
// extractSymbols finds crypto symbols mentioned in text.
func extractSymbols(text string) []string {
upper := strings.ToUpper(text)
known := []string{"BTC", "ETH", "SOL", "BNB", "XRP", "DOGE", "ADA", "AVAX", "DOT", "LINK", "MATIC", "UNI", "AAVE"}
var found []string
for _, s := range known {
if strings.Contains(upper, s) {
found = append(found, s)
}
}
return found
}