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import random
from datetime import datetime
class SentimentAnalyzer:
def __init__(self):
self.sentiment_sources = [
"Federal Reserve hints at rate pause - positive for gold",
"Inflation data higher than expected - gold demand rising",
"Dollar strength weighs on precious metals",
"Central banks continue gold accumulation",
"Geopolitical tensions support safe-haven demand",
"Gold ETFs see outflows amid risk-on sentiment",
"Technical breakout above resistance level",
"Profit-taking observed after recent rally"
]
def analyze_gold_sentiment(self):
"""Analyze sentiment for gold market"""
try:
# Simulate sentiment analysis
# In production, would use actual news API and NLP model
# Generate random sentiment around current market conditions
base_sentiment = random.uniform(-0.5, 0.5)
# Add some realistic variation
if random.random() > 0.7:
# Strong sentiment event
sentiment = base_sentiment + random.uniform(-0.5, 0.5)
else:
sentiment = base_sentiment
# Clamp between -1 and 1
sentiment = max(-1, min(1, sentiment))
# Generate news summary
num_news = random.randint(3, 6)
selected_news = random.sample(self.sentiment_sources, num_news)
news_html = "<div style='max-height: 300px; overflow-y: auto;'>"
news_html += "<h4 style='color: #FFD700;'>Latest Gold News</h4>"
for i, news in enumerate(selected_news, 1):
sentiment_label = "🟢" if "positive" in news or "rising" in news or "support" in news else \
"🔴" if "weighs" in news or "outflows" in news or "Profit-taking" in news else \
"🟡"
news_html += f"<p style='margin: 10px 0; padding: 10px; background: rgba(255,255,255,0.05); border-radius: 5px;'>{sentiment_label} {news}</p>"
news_html += "</div>"
return sentiment, news_html
except Exception as e:
return 0, f"<p>Error analyzing sentiment: {str(e)}</p>" |