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Financial News Tracker

A command-line tool that fetches and analyzes real-time financial news using Perplexity’s Sonar API. Get comprehensive market insights, news summaries, and investment analysis for any financial topic.

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Features

  • Real-time financial news aggregation from multiple sources
  • Market sentiment analysis (Bullish/Bearish/Neutral)
  • Impact assessment for news items (High/Medium/Low)
  • Sector and company-specific analysis
  • Investment insights and recommendations
  • Customizable time ranges (24h to 1 year)
  • Structured JSON output support
  • Beautiful emoji-enhanced CLI output

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Installation

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1. Install required dependencies

# Install from requirements file (recommended)
pip install -r requirements.txt

# Or install manually
pip install requests pydantic

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2. Make the script executable

chmod +x financial_news_tracker.py

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API Key Setup

The tool requires a Perplexity API key. You can provide it in one of these ways: 
export PPLX_API_KEY=YOUR_API_KEY

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2. As a command-line argument

./financial_news_tracker.py "tech stocks" --api-key YOUR_API_KEY

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3. In a file

Create a file named pplx_api_key or .pplx_api_key in the same directory: 
echo "YOUR_API_KEY" > .pplx_api_key
chmod 600 .pplx_api_key

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Quick Start

Get the latest tech stock news: 
./financial_news_tracker.py "tech stocks"
This will fetch recent financial news about tech stocks, analyze market sentiment, and provide actionable insights.

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Usage Examples

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Basic usage - Get news for a specific topic

./financial_news_tracker.py "S&P 500"

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Get cryptocurrency news from the past week

./financial_news_tracker.py "cryptocurrency" --time-range 1w

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Track specific company news

./financial_news_tracker.py "AAPL Apple stock"

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Get news about market sectors

./financial_news_tracker.py "energy sector oil prices"

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Output as JSON for programmatic use

./financial_news_tracker.py "inflation rates" --json

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Use a different model

./financial_news_tracker.py "Federal Reserve interest rates" --model sonar

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Enable structured output (requires Tier 3+ API access)

./financial_news_tracker.py "tech earnings" --structured-output

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Time Range Options

  • 24h - Last 24 hours (default)
  • 1w - Last week
  • 1m - Last month
  • 3m - Last 3 months
  • 1y - Last year

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Output Format

The tool provides comprehensive financial analysis including:

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1. Executive Summary

A brief overview of the key financial developments

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2. Market Analysis

  • Market Sentiment: Overall market mood (🐂 Bullish, 🐻 Bearish, ⚖️ Neutral)
  • Key Drivers: Factors influencing the market
  • Risks: Current market risks and concerns
  • Opportunities: Potential investment opportunities

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3. News Items

Each news item includes:
  • Headline: The main news title
  • Impact: Market impact level (🔴 High, 🟡 Medium, 🟢 Low)
  • Summary: Brief description of the news
  • Affected Sectors: Industries or companies impacted
  • Source: News source attribution

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4. Investment Insights

Actionable recommendations and analysis based on the news

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Example Output

📊 FINANCIAL NEWS REPORT: tech stocks
📅 Period: Last 24 hours

📝 EXECUTIVE SUMMARY:
Tech stocks showed mixed performance today as AI-related companies surged while 
semiconductor stocks faced pressure from supply chain concerns...

📈 MARKET ANALYSIS:
  Sentiment: 🐂 BULLISH

  Key Drivers:
    • Strong Q4 earnings from major tech companies
    • AI sector momentum continues
    • Federal Reserve signals potential rate cuts

  ⚠️ Risks:
    • Semiconductor supply chain disruptions
    • Regulatory scrutiny on big tech
    • Valuation concerns in AI sector

  💡 Opportunities:
    • Cloud computing growth
    • AI infrastructure plays
    • Cybersecurity demand surge

📰 KEY NEWS ITEMS:

1. Microsoft Hits All-Time High on AI Growth
   Impact: 🔴 HIGH
   Summary: Microsoft stock reached record levels following strong Azure AI revenue...
   Sectors: Cloud Computing, AI, Software
   Source: Bloomberg

💼 INSIGHTS & RECOMMENDATIONS:
  • Consider diversifying within tech sector
  • AI infrastructure companies show strong momentum
  • Monitor semiconductor sector for buying opportunities

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Advanced Features

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Custom Queries

You can combine multiple topics for comprehensive analysis: 
# Get news about multiple related topics
./financial_news_tracker.py "NVIDIA AMD semiconductor AI chips"

# Track geopolitical impacts on markets
./financial_news_tracker.py "oil prices Middle East geopolitics"

# Monitor economic indicators
./financial_news_tracker.py "inflation CPI unemployment Federal Reserve"

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JSON Output

For integration with other tools or scripts: 
./financial_news_tracker.py "bitcoin" --json | jq '.market_analysis.market_sentiment'

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Tips for Best Results

  1. Be Specific: Include company tickers, sector names, or specific events
  2. Combine Topics: Mix company names with relevant themes (e.g., “TSLA electric vehicles”)
  3. Use Time Ranges: Match the time range to your investment horizon
  4. Regular Monitoring: Set up cron jobs for daily market updates

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Limitations

  • Results depend on available public information
  • Not financial advice - always do your own research
  • Historical data may be limited for very recent events
  • Structured output requires Tier 3+ Perplexity API access

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Error Handling

The tool includes comprehensive error handling for:
  • Invalid API keys
  • Network connectivity issues
  • API rate limits
  • Invalid queries
  • Parsing errors

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Integration Examples

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Daily Market Report

Create a script for daily updates: 
#!/bin/bash
# daily_market_report.sh

echo "=== Daily Market Report ===" > market_report.txt
echo "Date: $(date)" >> market_report.txt
echo "" >> market_report.txt

./financial_news_tracker.py "S&P 500 market overview" >> market_report.txt
./financial_news_tracker.py "top gaining stocks" >> market_report.txt
./financial_news_tracker.py "cryptocurrency bitcoin ethereum" >> market_report.txt

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Python Integration

import subprocess
import json

def get_financial_news(query, time_range="24h"):
    result = subprocess.run(
        ["./financial_news_tracker.py", query, "--time-range", time_range, "--json"],
        capture_output=True,
        text=True
    )
    
    if result.returncode == 0:
        return json.loads(result.stdout)
    else:
        raise Exception(f"Error fetching news: {result.stderr}")

# Example usage
news = get_financial_news("tech stocks", "1w")
print(f"Market sentiment: {news['market_analysis']['market_sentiment']}")