FinPull is a comprehensive financial data scraping tool providing multiple interfaces for accessing financial market data. The package includes API, command-line, and graphical user interfaces, making it suitable for various use cases from automated trading systems to interactive data analysis.
pip install finpull
For API-only usage (lightweight):
pip install finpull-core
finpull
# Interactive mode
finpull --interactive
# Direct commands
finpull add AAPL GOOGL MSFT
finpull show AAPL --full
finpull export portfolio.xlsx --xlsx
finpull refresh
from finpull import FinancialDataAPI
api = FinancialDataAPI()
result = api.add_ticker("AAPL")
data = api.get_data("AAPL")
if data['success']:
stock_info = data['data']
print(f"Company: {stock_info['company_name']}")
print(f"Price: ${stock_info['price']}")
print(f"Market Cap: {stock_info['market_cap']}")
Comprehensive benchmarks on typical hardware with 10 test runs each:
| Metric | Core Package | Full Package | Difference |
|---|---|---|---|
| Package Size | 21.9 KB | 27.2 KB | +5.3 KB (+24.2%) |
| Installed Size | 134 KB | 188 KB | +54 KB (+40.3%) |
| Import Time (cached) | 0.0002s | 0.0002s | No difference |
| Dependencies | 3 packages | 4 packages | +openpyxl |
Launch: finpull or finpull --gui
Features:
Launch: finpull --interactive or direct commands
Available Commands:
add <tickers> - Add ticker symbols for trackingremove <tickers> - Remove tickers from trackingshow [ticker] [--full] - Display ticker informationrefresh [ticker] - Update data from sourcesexport <filename> [--json] [--csv] [--xlsx] - Save data to filesstats - Show system statistics and healthclear - Remove all tracked data (with confirmation)Features:
finpull> prompt) for explorationImport: from finpull import FinancialDataAPI, FinancialDataScraper
Classes:
Features:
Provides 27 financial metrics per ticker across all categories: basic info, valuation ratios, earnings, profitability, growth, financial position, and market data. Uses Finviz and Yahoo Finance with automatic failover for high reliability.
# Custom storage location
export FINPULL_STORAGE_FILE="/path/to/custom/storage.json"
# Rate limiting (seconds between requests)
export FINPULL_RATE_LIMIT="2"
# Logging level (DEBUG, INFO, WARNING, ERROR)
export FINPULL_LOG_LEVEL="INFO"
# GUI theme (if supported)
export FINPULL_GUI_THEME="default"
Data is persisted locally in JSON format with automatic backups:
~/.finpull/data.json%USERPROFILE%\.finpull\data.jsonBuilt-in intelligent rate limiting prevents API blocks:
Supports browser integration via Pyodide and Node.js via CLI commands. See main repository README for complete integration examples and code samples.
finpull/
├── Core Package (finpull-core)
│ ├── API interface
│ ├── Data scraping engine
│ ├── Storage management
│ └── Utility functions
└── Interface Extensions
├── Command-line interface
├── Graphical user interface
└── Excel export functionality
The full package depends on finpull-core for core functionality, ensuring:
from finpull import FinancialDataAPI
api = FinancialDataAPI()
# Build a technology portfolio
tech_stocks = ["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA", "META", "NVDA"]
results = api.batch_add_tickers(tech_stocks)
print(f"Successfully added {results['summary']['added_count']} stocks")
# Analyze portfolio performance
portfolio_data = api.get_data()
for stock in portfolio_data['data']:
print(f"{stock['ticker']}: ${stock['price']} | P/E: {stock['pe_ratio']} | Cap: {stock['market_cap']}")
#!/bin/bash
# Daily portfolio update script
echo "Updating portfolio..."
# Add new stocks if needed
finpull add AAPL GOOGL MSFT
# Refresh all data
finpull refresh
# Generate reports
finpull export "reports/portfolio_$(date +%Y%m%d)" --json --csv --xlsx
# Show summary
finpull show --full
echo "Portfolio update complete"
import time
from finpull import FinancialDataAPI
api = FinancialDataAPI()
# Add watchlist
watchlist = ["AAPL", "GOOGL", "MSFT", "TSLA"]
api.batch_add_tickers(watchlist)
while True:
# Refresh data every 5 minutes
api.refresh_data()
# Check for significant changes
data = api.get_data()
for stock in data['data']:
change_5y = float(stock['change_5y'].replace('%', ''))
if abs(change_5y) > 10: # More than 10% change
print(f"Alert: {stock['ticker']} changed {change_5y}% over 5 years")
time.sleep(300) # 5 minutes
import threading
from finpull import FinancialDataGUI
def setup_automated_gui():
gui = FinancialDataGUI()
# Pre-populate with data
gui.scraper.add_ticker("AAPL")
gui.scraper.add_ticker("GOOGL")
gui.refresh_display()
# Run GUI in separate thread
gui_thread = threading.Thread(target=gui.run)
gui_thread.start()
return gui
# Launch automated GUI session
gui = setup_automated_gui()
import logging
from finpull import FinancialDataAPI
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
api = FinancialDataAPI()
def safe_operation(ticker):
try:
result = api.add_ticker(ticker)
if result['success']:
logger.info(f"Successfully added {ticker}")
return True
else:
logger.warning(f"Failed to add {ticker}: {result.get('error')}")
return False
except Exception as e:
logger.error(f"Exception for {ticker}: {e}")
return False
# Safe batch processing
tickers = ["AAPL", "INVALID", "GOOGL", "MSFT"]
successful = [t for t in tickers if safe_operation(t)]
print(f"Successfully processed {len(successful)}/{len(tickers)} tickers")
MIT License - see LICENSE file for details.
FAQs
Complete financial data scraper with CLI, GUI, and API interfaces
We found that finpull demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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