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JeremyWhittaker / repository
This repository provides a set of Python scripts to simplify fetching and processing fundamental stock market data using the [EODHD API](https://eodhistoricaldata.com/).
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In order to create a quantitative investment strategy based on fundamental data, you need to have a robust local repository of data. This program downloads all stock, ETF, and mutual fund data from the EODHD API and saves it as Parquet files for efficient storage and access. Additionally, the program generates HTML reports for analysis and provides examples demonstrating how to access various fields within the JSON files.
Clone this repository:
git clone https://github.com/JeremyWhittaker/stock-market-data-fetcher.git
cd stock-market-data-fetcher
Install dependencies (also see requirements.txt for details):
pip install -r requirements.txt
Create a .env file at the root directory and set the API key for EODHD:
EODHD_API_KEY=your_api_key_here
get_symbols_from_exchange.py: Fetches exchange symbols from the EODHD API and saves them as JSON files for each exchange.get_fundamental_data.py: Downloads and saves fundamental data for all assets (stocks, ETFs, mutual funds) as Parquet files for efficient storage and analysis.get_unique_exchanges.py: Lists all unique exchanges available in the JSON files for a specific country.generate_html.py: Generates detailed HTML reports from the saved JSON data, including plots and tables for easy exploration.analyze_json.py: Analyzes the structure of JSON files, detecting patterns, data types, and repetitive keys.etf_data_to_csv.py: Converts ETF JSON data into CSV format, with peer comparisons based on holdings overlap.generate_market_cap_categories.py: Generates market cap categories (e.g., nano, micro, mega) based on market capitalization values and saves results as CSV files.get_market_cap.py: Extracts and saves market cap data for assets from JSON files and provides options to categorize assets by market cap.To fetch symbols for all exchanges:
python get_symbols_from_exchange.py
To download and save fundamental data for a specific country and exchange:
python get_fundamental_data.py --country US --exchange NASDAQ --output_dir ./data/fundamental_data --days 7
To list all unique exchanges for a given country:
python get_unique_exchanges.py --country US
To generate an HTML report for a specific stock:
python generate_html.py --symbol AAPL --data-dir ./data/fundamental_data --output-dir ./html
To analyze the structure of a specific JSON file:
python analyze_json.py --json-path ./data/fundamental_data/aapl.json --max-depth 4
To convert ETF data to CSV format and calculate peer overlaps:
python etf_data_to_csv.py --json-dir ./data/fundamental_data --output-csv ./data/etfs.csv --peers 85.0
To generate CSV files with categorized market caps:
python generate_market_cap_categories.py --data_dir ./data/fundamental_data --output_dir ./market_cap_categories
To list market cap data and categorize it:
python get_market_cap.py --data-dir ./data/fundamental_data --output-csv ./data/market_caps.csv
Logs are saved to exchange_symbols.log and other log files as needed. You can monitor the log to check details about processed exchanges and error messages.
Fork the repository.
Create a new branch:
git checkout -b feature-branch-name
Commit your changes:
git commit -m 'Add new feature'
Push to the branch:
git push origin feature-branch-name
Create a pull request.
This project is licensed under the MIT License. See LICENSE for more details.