news-tracker

Files (2)

• SKILL.md
• news_digest.py · scripts/

SKILL.md (first lines)

# News Tracker

Stay informed on crypto news and market sentiment.

## Overview

- **News Aggregation** - Top crypto news sources
- **Sentiment Analysis** - Bullish/bearish signals from headlines
- **Event Calendar** - Upcoming launches, forks, etc.
- **Social Monitoring** - Twitter, Reddit trends

## News Sources

| Source | URL | Focus |
|--------|-----|-------|
| CoinDesk | coindesk.com | General crypto |
| CoinTelegraph | cointelegraph.com | News & analysis |
| The Block | theblock.co | Institutional |
| Decrypt | decrypt.co | DeFi, NFTs |
| CryptoSlate | cryptoslate.com | Market data |

## Commands

### Fetch Latest Crypto News

```bash
python3 -c "
import feedparser

feeds = [
    ('CoinDesk', 'https://www.coindesk.com/arc/outboundfeeds/rss/'),
    ('CoinTelegraph', 'https://cointelegraph.com/rss'),
]

print('📰 LATEST CRYPTO NEWS')
print('=' * 60)

for source, url in feeds:
    try:
        feed = feedparser.parse(url)
        print(f'\\n📌 {source}')
        for entry in feed.entries[:3]:
            print(f'  • {entry.title[:60]}...')
            print(f'    {entry.link}')
    except Exception as e:
        print(f'  ⚠️ Error: {e}')
"
```

### Search News for Specific Coin

```bash
python3 -c "
import requests
from bs4 import BeautifulSoup

coin = 'bitcoin'
url = f'https://cryptonews.com/news/{coin}-news/'

print(f'📰 NEWS SEARCH: {coin.upper()}')
print('=' * 60)

try:
    response = requests.get(url, headers={'User-Agent': 'Mozilla/5.0'}, timeout=10)
    soup = BeautifulSoup(response.text, 'html.parser')
    
    # Find news items (structure may vary)
    articles = soup.find_all('article', limit=5)
    
    for article in articles:
        title = article.find(['h2', 'h3', 'a'])
        if title:
            print(f'• {title.get_text().strip()[:70]}')
except Exception as e:
    print(f'⚠️ Error fetching news: {e}')
"
```

### Simple Sentiment Analysis

```bash
python3 -c "
import feedparser
import re

# Simple keyword-based sentiment
bullish_words = ['surge', 'rally', 'breakout', 'bullish', 'soar', 'jump', 'gain', 'rise', 'ATH', 'moon', 'pump']
bearish_words = ['crash', 'dump', 'bearish', 'plunge', 'fall', 'drop', 'fear', 'sell-off', 'decline', 'tank']

feed = feedparser.parse('https://cointelegraph.com/rss')

bullish_count = 0
bearish_count = 0
headlines = []

for entry in feed.entries[:20]:
    title = entry.title.lower()
    headlines.append(entry.title)
    
    for word in bullish_words:
        if word.lower() in title:
            bullish_count += 1
            break
    
    for word in bearish_words:
        if word.lower() in title:
            bearish_count += 1
            break

total = bullish_count + bearish_count
if total > 0:
    bullish_pct = (bullish_count / total) * 100
    bearish_pct = (bearish_count / total) * 100
else:
    bullish_pct = bearish_pct = 50

print('📊 NEWS SENTIMENT ANALYSIS')
print('=' * 60)
print(f'Headlines analyzed: {len(headlines)}')

From Signal-Execution-Labs/forex-trading-ai-agent, MIT.

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