finpull 1.1.0

Complete financial data scraper with CLI, GUI, and API interfaces

FinPull - Financial Data Scraper

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.

Installation

pip install finpull

For API-only usage (lightweight):

pip install finpull-core

Quick Start

GUI Mode (Default)

finpull

Command Line Interface

# Interactive mode
finpull --interactive

# Direct commands
finpull add AAPL GOOGL MSFT
finpull show AAPL --full
finpull export portfolio.xlsx --xlsx
finpull refresh

Programmatic API

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']}")

Performance

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

Key Features

• Multiple Interfaces: GUI, CLI, and API access
• Export Options: JSON, CSV, and Excel formats
• Progress Tracking: Real-time operation status
• Cross-Platform: Windows, macOS, and Linux support

Interfaces

Graphical User Interface (GUI)

Launch: finpull or finpull --gui

Features:

• Data Grid: Complete view of all 27 financial metrics with horizontal/vertical scrolling
• Multi-Selection: Select and manage multiple tickers simultaneously (Ctrl+Click, Shift+Click)
• Smart Sorting: Click column headers to sort by any metric with data type awareness
• Real-time Updates: Progress indicators showing 🔄 loading, ✅ success, ❌ error states
• Export Dialog: Save data to JSON, CSV, or Excel with file browser integration
• Status Bar: Current operation status and ticker count display
• Responsive Layout: Adapts to different screen sizes and resolutions

Command Line Interface (CLI)

Launch: finpull --interactive or direct commands

Available Commands:

• add <tickers> - Add ticker symbols for tracking
• remove <tickers> - Remove tickers from tracking
• show [ticker] [--full] - Display ticker information
• refresh [ticker] - Update data from sources
• export <filename> [--json] [--csv] [--xlsx] - Save data to files
• stats - Show system statistics and health
• clear - Remove all tracked data (with confirmation)

Features:

• Interactive Mode: Shell-like interface (finpull> prompt) for exploration
• Direct Commands: Single-command operations perfect for scripting and automation
• Batch Operations: Process multiple tickers efficiently in one command
• Formatted Output: Beautiful ASCII tables with aligned columns and borders
• Progress Indicators: Real-time status updates for long-running operations
• Auto-completion: Tab completion for commands and options (in interactive mode)

API Interface

Import: from finpull import FinancialDataAPI, FinancialDataScraper

Classes:

• FinancialDataAPI: High-level interface with error handling and validation
• FinancialDataScraper: Low-level scraper for direct data access and control
• FinancialData: Data model with 27+ financial attributes

Features:

• Consistent Responses: All methods return standardized JSON format
• Comprehensive Error Handling: Detailed error codes and descriptive messages
• Type Hints: Full type annotation support for better IDE integration
• Validation: Built-in ticker format validation and data sanitization
• Callback Support: Progress callbacks for batch operations and real-time updates

Data Coverage

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.

Configuration

Environment Variables

# 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"

Storage

Data is persisted locally in JSON format with automatic backups:

• Linux/macOS: ~/.finpull/data.json
• Windows: %USERPROFILE%\.finpull\data.json
• Backup: Automatic backup before major operations
• Format: Human-readable JSON with proper indentation

Rate Limiting

Built-in intelligent rate limiting prevents API blocks:

• Default: 1 request per second (configurable)
• Adaptive: Automatically increases delays if rate limits detected
• Burst Protection: Prevents accidental rapid-fire requests
• Source-Specific: Different limits for different data sources

Documentation

• Interface Guide - Complete interface documentation
• API Reference - API methods and responses
• Data Format - JSON schema and field descriptions

Web Integration

Supports browser integration via Pyodide and Node.js via CLI commands. See main repository README for complete integration examples and code samples.

Package Architecture

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:

• No code duplication between packages
• Consistent API across all interfaces
• Modular installation options
• Streamlined maintenance and updates

Examples

Portfolio Management

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']}")

Automated CLI Workflow

#!/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"

Real-time Monitoring

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

GUI Automation

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()

Error Handling & Logging

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")

License

MIT License - see LICENSE file for details.

Links

• Core Package - Lightweight API-only version
• Source Code - GitHub repository
• Issues - Bug reports and feature requests