Python interface for Quill Express API - AI-powered financial data processing
Project description
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
pip install quillai
Quick Start
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
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