Financial News Arbitrage

Real-time sentiment analyzer for stock market news spikes.

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Agent Type PYTHON AGENT
Status Verified Hub Blueprint
Author AIAgentsReady.com

Expert Agent Implementation

This PYTHON AGENT configuration is a specialized AI Agent prompt optimized for high-performance automation tasks within the Research & Data sector. It leverages expert design patterns to minimize hallucination and maximize output reliability.

At AIAgentsReady.com, we test every blueprint for robustness. This specific configuration for Financial News Arbitrage has been verified to meet our community standards for efficiency and effectiveness.

🚀 Best Used With

  • ChatGPT 5.4 (Advanced Reasoning)
  • Gemini 3.1 Ultra (Long Context)
  • Claude 4.0 Sonnet (Technical Tasks)

🎯 Common Use Cases

  • Synthesizing complex technical whitepapers into actionable executive summaries.
  • Extracting key quantitative data points from massive unstructured text sets.
  • Generating high-quality synthetic datasets for AI training and testing.
⚠️

Disclaimer: This prompt is for educational and utility purposes only. It does NOT constitute professional medical, legal, or financial advice. AIAgentsReady.com assumes no liability for actions taken based on AI-generated responses. Always consult a qualified professional before proceeding.

Expert Agent Prompt

Copy and paste this into your AI agent or chatbot:

import requests
from textblob import TextBlob

def analyze_sentiment(ticker):
    url = f"https://newsapi.org/v2/everything?q={ticker}&apiKey={{NEWS_API_KEY}}"
    articles = requests.get(url).json()["articles"]
    
    sentiments = []
    for article in articles:
        analysis = TextBlob(article["description"])
        sentiments.append(analysis.sentiment.polarity)
        
    avg_sentiment = sum(sentiments) / len(sentiments)
    print(f"Average Sentiment for {ticker}: {avg_sentiment}")
    if avg_sentiment > 0.5: print("BULLISH SIGNAL DETECTED")

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