FetchFinancialsExcel

A Python package for fetching and analyzing fundamental financial data using the EODHD API in the background. This package allows you to process Excel files containing stock tickers to generate financial analysis reports. It reads ticker symbols from an input Excel sheet, queries the EODHD API for relevant financial data, and outputs a comprehensive analysis report in a new Excel file.

IMPORTANT: This software is licensed for noncommercial use only under the PolyForm Noncommercial License 1.0.0. Commercial use is prohibited without a separate commercial license. See the LICENSE file for details.

Features

• Concurrent Data Fetching: Efficiently fetches financial data for multiple stocks concurrently.
• Comprehensive Analysis: Calculate key financial metrics not directly avaibale in EODHD including:
  • Conservative investment scores
  • Quality scores
  • P/E ratios, ROCE, revenue growth, EPS growth
  • Free cash flow metrics, buyback data, insider ownership
  • Technical indicators and moving averages
• Excel Integration: Read ticker lists from Excel file and output results to an file

Installation

From PyPI

pip install FetchFinancialsExcel

Development Installation

git clone https://github.com/username/FetchFinancialsExcel.git
cd FetchFinancialsExcel
pip install -r requirements.txt
pip install -e .

Prerequisites

1. EODHD API Key: You need an API key from EODHD to fetch financial data

Start

1. Prepare Excel File

Create an Excel file (.xlsx) with the following format:

Important Notes:

• First Column: Company names
• Second Column: Ticker symbols
• Note! Make sure to use proper ticker formats (e.g., DANSKE.CO for Copenhagen exchange) that are compatible with EODHD.

2. Run the Analysis

fetch-financials-excel --api-key YOUR_EODHD_API_KEY --input tickers.xlsx --output results.xlsx

3. View Results

The output Excel file will contain comprehensive financial data and analysis for all tickers.

Command Line Usage

Basic Usage

fetch-financials-excel --api-key YOUR_API_KEY --input input.xlsx --output output.xlsx

Advanced Usage

# Custom number of concurrent workers (default: 10)
fetch-financials-excel --api-key YOUR_API_KEY --input tickers.xlsx --output results.xlsx --workers 5

# Short form arguments
fetch-financials-excel --api-key YOUR_API_KEY -i tickers.xlsx -o results.xlsx -w 5

Command Line Arguments

Argument	Short	Required	Description
--api-key		Yes	Your EODHD API key
--input	-i	Yes	Path to input Excel file
--output	-o	YEs	Path to output Excel file
--workers	-w		Number of concurrent workers (1-50, default: 10)
--version			Show version information
--help	-h		Show help message

Python API Usage

from fetchfinancialsexcel import FundamentalDataFetcher

# Initialize with your API key
fetcher = FundamentalDataFetcher(api_key="YOUR_EODHD_API_KEY")

# Process an Excel file
fetcher.process_excel_file(
    input_file="path/to/tickers.xlsx",
    output_file="path/to/results.xlsx",
    max_workers=10
)

# Or work with data directly
company_list, ticker_list = fetcher.extract_tickers_from_excel("tickers.xlsx")
df, separate_data = fetcher.fetch_all_data(company_list, ticker_list)
analyzed_df = fetcher.analyze_data(df, separate_data)

Output Data

The output Excel file contains the following types of data:

Financial Metrics

• Price Information: Current price, currency, sector
• Valuation Ratios: P/E ratios (current and 5-year average), ROCE
• Growth Metrics: Revenue growth, EPS growth, asset growth
• Profitability: Gross profitability, free cash flow metrics
• Balance Sheet: Accruals, total yield, insider ownership percentage

Analysis Scores

• Greenblatt Formula: Magic Formula ranking based on P/E and ROCE
• Conservative Formula: Risk-adjusted scoring based on volatility, NPY, and momentum
• Quality Score: Overall quality assessment

Technical Data

• Moving Averages: Various period moving averages
• Volatility Metrics: Historical volatility measures
• Price Momentum: Momentum indicators

Testing

Before pushing changes, run the test script to validate functionality:

python test_package.py

License

This project is licensed under the PolyForm Noncommercial License 1.0.0. Commercial use is prohibited. See the LICENSE file or visit https://polyformproject.org/licenses/noncommercial/1.0.0/ for full terms.

Author

Carl Viggo Gravenhorst-Lövenstierne