About xai-stock-sentiment
A financial analysis skill that uses Grok's X integration to examine sentiment for individual stocks or groups, earnings reactions, sectors, and unusual activity. It can also put sentiment in the context of price data.
Key features
- 01Analyze single-stock sentiment
- 02Compare sentiment across stocks
- 03Review earnings and sector sentiment
- 04Detect unusual sentiment or activity
- 05Combine sentiment with price data
Use cases
- 01Track sentiment across a stock watchlist
- 02Compare sentiment among stocks in a sector
- 03Contextualize market sentiment with price movements
Install
Copy the command and run it in a terminal.
$
npx skills add https:/After installing, type/xai-stock-sentimentin Claude Code to confirm it loaded.
Details
From the project READMExAI Stock Sentiment Analysis
Real-time stock sentiment from Twitter/X using Grok's native integration - a unique capability for financial analysis.
Quick Start
import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("XAI_API_KEY"),
base_url="https://api.x.ai/v1"
)
def get_stock_sentiment(ticker: str) -> dict:
"""Get real-time sentiment for a stock ticker."""
response = client.chat.completions.create(
model="grok-4-1-fast",
messages=[{
"role": "user",
"content": f"""Analyze X sentiment for ${ticker} stock.
Search recent posts and return JSON:
{{
"ticker": "{ticker}",
"sentiment": {{
"overall": "bullish" | "bearish" | "neutral",
"score": -1.0 to 1.0,
"confidence": 0.0 to 1.0
}},
"metrics": {{
"bullish_percent": 0-100,
"bearish_percent": 0-100,
"neutral_percent": 0-100,
"volume": "high" | "medium" | "low",
"velocity": "increasing" | "stable" | "decreasing"
}},
"key_drivers": ["driver1", "driver2"],
"notable_mentions": [
{{"user": "@handle", "summary": "...", "influence": "high/med/low"}}
],
"risks": ["risk1", "risk2"],
"catalysts": ["catalyst1", "catalyst2"]
}}"""
}]
)
return response.choices[0].message.content
# Example
sentiment = get_stock_sentiment("AAPL")
print(sentiment)
Financial Handles to Track
FINANCIAL_INFLUENCERS = [
# Breaking News
"DeItaone",
"FirstSquawk",
"LiveSquawk",
# Options Flow
"unusual_whales",
"OptionsHawk",
# Analysis
"jimcramer",
"Carl_C_Icahn",
"elerianm",
# Retail Sentiment
"wallstreetbets",
"StockMarketNewz"
]
Sentiment Functions
Single Stock Analysis
def analyze_single_stock(ticker: str, timeframe: str = "24h") -> dict:
"""Comprehensive single stock sentiment analysis."""
response = client.chat.completions.create(
model="grok-4-1-fast",
messages=[{
"role": "user",
"content": f"""Perform deep sentiment analysis for ${ticker} on X.
Timeframe: Last {timeframe}
Return comprehensive JSON:
{{
"ticker": "{ticker}",
"timestamp": "current time",
"sentiment_analysis": {{
"overall_score": -1 to 1,
"label": "very bullish/bullish/neutral/bearish/very bearish",
"confidence": 0 to 1,
"sample_size": n
}},
"breakdown": {{
"retail_sentiment": {{
"score": -1 to 1,
"volume": "high/med/low",
"trending_hashtags": [...]
}},
"influencer_sentiment": {{
"score": -1 to 1,
"key_opinions": [...]
}},
"news_reaction": {{
"recent_news": [...],
"reaction_sentiment": -1 to 1
}}
}},
"trading_signals": {{
"momentum": "bullish/bearish/neutral",
"volume_trend": "increasing/stable/decreasing",
"unusual_activity": true/false,
"options_chatter": "calls heavy/puts heavy/balanced/minimal"
}},
"catalysts": {{
"upcoming": [...],
"recent": [...]
}},
"risks": [...],
"summary": "2-3 sentence summary"
}}"""
}]
)
return response.choices[0].message.content
Multi-Stock Comparison
def compare_stocks(tickers: list) -> dict:
"""Compare sentiment across multiple stocks."""
tickers_str = ", ".join([f"${t}" for t in tickers])
response = client.chat.completions.create(
model="grok-4-1-fast",
messages=[{
"role": "user",
"content": f"""Compare X sentiment for: {tickers_str}
Return JSON:
{{
"comparison": [
{{
"ticker": "...",
"sentiment_score": -1 to 1,
"volume": "high/med/low",
"trend": "improving/stable/declining",
"key_driver": "..."
}}
],
"rankings": {{
"most_bullish": "TICKER",
"most_bearish": "TICKER",
"highest_volume": "TICKER",
"best_momentum": "TICKER"
}},
"sector_sentiment": "...",
"recommendation": "..."
}}"""
}]
)
return response.choices[0].message.content
Earnings Reaction
def earnings_reaction(ticker: str) -> dict:
"""Analyze X reaction to earnings announcement."""
response = client.chat.completions.create(
model="grok-4-1-fast",
messages=[{
"role": "user",
"content": f"""Analyze X reaction to ${ticker} earnings.
Search for post-earnings discussion and return JSON:
{{
"ticker": "{ticker}",
"earnings_reaction": {{
"immediate_sentiment": "positive/negative/mixed",
"sentiment_score": -1 to 1,
"surprise_reaction": "beat expectations/met/missed"
}},
"key_topics": {{
"positives_mentioned": [...],
"concerns_raised": [...],
"guidance_reaction": "..."
}},
"influencer_reactions": [
{{"user": "@handle", "stance": "bullish/bearish", "key_point": "..."}}
],
"price_action_sentiment": {{
"pre_market_mood": "...",
"opening_reaction": "..."
}},
"forward_outlook": "bullish/bearish/cautious"
}}"""
}]
)
return response.choices[0].message.content
Sector Sentiment
def sector_sentiment(sector: str, tickers: list = None) -> dict:
"""Analyze sentiment for an entire sector."""
response = client.chat.completions.create(
model="grok-4-1-fast",
messages=[{
"role": "user",
"content": f"""Analyze X sentiment for the {sector} sector.
{"Key tickers to focus on: " + ", ".join([f"${t}" for t in tickers]) if tickers else ""}
Return JSON:
{{
"sector": "{sector}",
"overall_sentiment": {{
"score": -1 to 1,
"label": "bullish/bearish/neutral",
"trend": "improving/stable/declining"
}},
"key_themes": [...],
"leaders": [
{{"ticker": "...", "sentiment": ..., "reason": "..."}}
],
"laggards": [
{{"ticker": "...", "sentiment": ..., "reason": "..."}}
],
"sector_catalysts": [...],
"macro_factors": [...]
}}"""
}]
)
return response.choices[0].message.content
Unusual Activity Detection
def detect_unusual_activity(ticker: str) -> dict:
"""Detect unusual sentiment or activity for a stock."""
response = client.chat.completions.create(
model="grok-4-1-fast",
messages=[{
"role": "user",
"content": f"""Check for unusual activity on X for ${ticker}.
Look for:
- Unusual volume of mentions
- Sudden sentiment shifts
- Whale/insider mentions
- Breaking news
- Options flow chatter
Return JSON:
{{
"ticker": "{ticker}",
"unusual_activity_detected": true/false,
"alerts": [
{{
"type": "volume_spike/sentiment_shift/news_break/whale_alert/options_unusual",
"severity": "high/medium/low",
"description": "...",
"source": "..."
}}
],
"normal_baseline": {{
"typical_mention_volume": "...",
"typical_sentiment": ...
}},
"current_state": {{
"mention_volume": "...",
"sentiment": ...
}},
"deviation": "significant/moderate/minor/none",
"recommended_action": "..."
}}"""
}]
)
return response.choices[0].message.content
Integration with Price Data
def sentiment_with_price(ticker: str, price_data: dict) -> dict:
"""Combine sentiment with price data for context."""
response = client.chat.completions.create(
model="grok-4-1-fast",
messages=[{
"role": "user",
"content": f"""Analyze ${ticker} sentiment in context of price action:
Current Price: ${price_data.get('price', 'N/A')}
Change: {price_data.get('change_percent', 'N/A')}%
52-Week High: ${price_data.get('high_52w', 'N/A')}
52-Week Low: ${price_data.get('low_52w', 'N/A')}
Search X for current sentiment and analyze:
1. Is sentiment aligned with price action?
2. Any divergence signals?
3. Support/resistance levels mentioned?
Return JSON with sentiment-price analysis."""
}]
)
return response.choices[0].message.content
Watchlist Monitoring
def monitor_watchlist(tickers: list) -> dict:
"""Monitor sentiment for a watchlist of stocks."""
tickers_str = ", ".join([f"${t}" for t in tickers])
response = client.chat.completions.create(
model="grok-4-1-fast",
messages=[{
"role": "user",
"content": f"""Monitor X sentiment for watchlist: {tickers_str}
Return JSON:
{{
"timestamp": "...",
"stocks": [
{{
"ticker": "...",
"sentiment_score": -1 to 1,
"change_from_yesterday": -1 to 1,
"alert_level": "none/watch/action",
"key_update": "..."
}}
],
"alerts": [
{{"ticker": "...", "alert_type": "...", "message": "..."}}
],
"market_mood": "risk-on/risk-off/mixed"
}}"""
}]
)
return response.choices[0].message.content
Best Practices
1. Time Your Queries
- Pre-market: Catch overnight sentiment
- Market open: Capture opening reaction
- After hours: Earnings reactions
2. Combine Data Sources
# Best signal = sentiment + price + volume
x_sentiment = get_stock_sentiment(ticker)
price_data = finnhub.get_quote(ticker)
fundamentals = fmp.get_financials(ticker)
3. Filter for Quality
Focus on verified accounts and high-follower influencers.
4. Watch for Manipulation
- Sudden coordinated pumps
- Bot-like patterns
- Unrealistic claims
Related Skills
xai-x-search- Raw X searchxai-sentiment- General sentimentxai-crypto-sentiment- Crypto focusxai-financial-integration- Data integrationfinnhub-api- Price datafmp-api- Fundamentals
References
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