Metadata-Version: 2.4
Name: quillai
Version: 0.1.0
Summary: Python interface for Quill Express API - AI-powered financial data processing
Home-page: https://github.com/quillai/quill-python
Author: Quill AI
Author-email: Quill AI <support@quillai.com>
License: MIT
Project-URL: Homepage, https://github.com/quillai/quill-python
Project-URL: Repository, https://github.com/quillai/quill-python
Project-URL: Documentation, https://github.com/quillai/quill-python
Project-URL: Issues, https://github.com/quillai/quill-python/issues
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.3.0
Requires-Dist: requests>=2.25.0
Provides-Extra: dev
Requires-Dist: pytest>=6.0; extra == "dev"
Requires-Dist: black>=21.0; extra == "dev"
Requires-Dist: flake8>=3.8; extra == "dev"
Requires-Dist: mypy>=0.910; extra == "dev"
Dynamic: author
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python

# QuillAI Python Package

A Python package for interfacing with the Quill Express API, providing AI-powered financial data processing capabilities using SEC filings and earnings transcripts.

## Installation

```bash
pip install quillai
```

## Quick Start

```python
import pandas as pd
from quillai import fillDfModel

# Create a DataFrame with financial data
data = {
    '1Q23': [1000, 500, 200],
    '2Q23': [1100, 550, 220], 
    '3Q23': [1200, 600, 240],
    '4Q23': [1300, 650, 260],
    '1Q24': [1400, 700, 280],
    '2Q24': [1500, 750, 300],
    '3Q24': [1600, 800, 320],
    '4Q24': [1700, 850, 340]
}
df = pd.DataFrame(data, index=['Revenue', 'Cost of Sales', 'Operating Expenses'])

# Fill the model for the next quarter with AI predictions
updated_df = fillDfModel(df, company="AAPL")
print(updated_df)  # Shows original data plus predictions for the next period
```

## Features

- **AI-Powered Predictions**: Uses machine learning to predict financial values based on SEC filings and earnings transcripts
- **Easy DataFrame Integration**: Works seamlessly with pandas DataFrames
- **Automatic Period Detection**: Handles quarterly and fiscal year periods automatically
- **Company-Specific Analysis**: Tailored predictions based on individual company data

## Environment Variables

- `QUILL_API_KEY`: Your Quill API token (required)
- `QUILL_EXPRESS_BASE`: Base URL for the API (default: "express.quillai.com")

## License

MIT License
