stock-data-loader 0.1.4
A Python library for loading stock data from the Seeking Alpha API
Requires Python: >=3.6

# 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:
   ```python
   from stock_data_loader import StockDataLoader
   ```

2. Create an instance of the loader:
   ```python
   loader = StockDataLoader()
   ```

3. Prepare a list of stock symbols you want to fetch data for:
   ```python
   symbols = ['AAPL', 'GOOGL', 'MSFT', 'AMZN']
   ```

4. Use the `load_symbol_data` method to fetch and process the data:
   ```python
   result_df = loader.load_symbol_data(symbols)
   ```

5. The result is a pandas DataFrame. You can now work with this data:
   ```python
   print(result_df)
   ```

Example:
```python
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:

```python
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](LICENSE) file for details.