stock-data-loader

Stock Data Loader

A Python library for loading stock data from the Seeking Alpha API.

Installation

You can install the Stock Data Loader using pip:

pip install stock-data-loader

How to Use StockDataLoader

The StockDataLoader class provides an easy way to fetch and process stock data from the Seeking Alpha API. Here's a quick guide on how to use it:

1. Import the class:

   from stock_data_loader import StockDataLoader
   

2. Create an instance of the loader:

   loader = StockDataLoader()
   

3. Prepare a list of stock symbols you want to fetch data for:

   symbols = ['AAPL', 'GOOGL', 'MSFT', 'AMZN']
   

4. Use the load_symbol_data method to fetch and process the data:

   result_df = loader.load_symbol_data(symbols)
   

5. The result is a pandas DataFrame. You can now work with this data:

   print(result_df)
   

Example:

from stock_data_loader import StockDataLoader

loader = StockDataLoader()
symbols = ['AAPL', 'GOOGL', 'MSFT', 'AMZN']
result_df = loader.load_symbol_data(symbols)

# Print the first few rows of the result
print(result_df.head())

# Save the result to a CSV file
result_df.to_csv('stock_data.csv', index=False)

This will fetch data for the specified symbols, process it, and return a DataFrame with various attributes like symbol, name, follower count, exchange, and content counters for analysis, news, transcripts, etc.

Output Columns

The load_symbol_data method returns a pandas DataFrame with the following columns:

• id: Unique identifier for the stock
• type: Type of the data (usually "ticker")
• symbol: Stock symbol
• name: Full name of the company
• followersCount: Number of followers on Seeking Alpha
• exchange: Stock exchange where the stock is listed
• analysis: Number of analysis articles
• related_analysis: Number of related analysis articles
• transcripts: Number of earnings call transcripts
• earning_slides: Number of earning slides available
• news: Number of news articles
• partnerNews: Number of partner news articles
• pressReleases: Number of press releases
• bulls_say: Number of bullish opinions
• bears_say: Number of bearish opinions
• investing_groups: Number of investing groups discussing the stock
• annual_dividends: Number of annual dividend reports
• annual_earnings_estimates: Number of annual earnings estimates
• dividend_news: Number of dividend-related news items
• sec_filings: Number of SEC filings
• sec_filings_fin_and_news: Number of financial and news-related SEC filings
• sec_filings_tenders: Number of tender offer SEC filings
• sec_filings_other: Number of other SEC filings
• sec_filings_ownership: Number of ownership-related SEC filings
• sector_rating_change_notices: Number of sector rating change notices
• sector_quant_warnings: Number of quantitative warnings for the sector
• sector_dividend_safety_warnings: Number of dividend safety warnings for the sector
• quarterly_revenue: Number of quarterly revenue reports
• annual_revenue: Number of annual revenue reports
• market_open: Market open status
• market_open_time: Market open time
• analysis_count: Another count of analysis articles (may differ from analysis)
• news_count: Another count of news articles (may differ from news)
• transcripts_count: Another count of transcripts (may differ from transcripts)

Note: Some columns may be empty or have different values than expected due to variations in the API response.

Example Output

Here's a sample of what the output might look like:

print(result_df[['symbol', 'followersCount', 'analysis', 'news', 'sec_filings', 'annual_dividends']].head())

  symbol  followersCount  analysis  news  sec_filings  annual_dividends
0   AAPL        2713202    10037  10753          121                 0
1   TSLA        1151910     5929   5737          121                 0
2  GOOGL         459787     1974   4592           97                 0

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.