Quick Start

Welcome!
This guide will help you install MorningPy, configure your environment, and make your first request in just a few minutes.

MorningPy is designed to be simple to use while remaining powerful and fully extensible.

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✅ 1. Install MorningPy

If you haven’t installed MorningPy yet, run:

```bash pip install morningpy Or refer to the full installation guide:

👉 Installation

🔑 2. Set Your API Key (If Required) MorningPy automatically detects your API credentials from:

Environment variables

A .env file

Local configuration files

Example using environment variables:

bash Copier le code export MORNINGPY_API_KEY="your_api_key" On Windows (PowerShell):

powershell Copier le code setx MORNINGPY_API_KEY "your_api_key" If your project uses .env:

ini Copier le code MORNINGPY_API_KEY=your_api_key 🚀 3. Your First Request Here is the simplest example to fetch the latest price for a stock:

python Copier le code import morningpy as mp

price = mp.Price("AAPL").latest() print(price) You should receive a validated PriceSchema object with the most recent value.

📈 4. Fetch Historical Prices You can easily request historical data:

python Copier le code import morningpy as mp

history = mp.Price("MSFT").history(start="2020-01-01", end="2024-01-01") print(history.head()) MorningPy returns a tidy pandas DataFrame ready for analysis.

📊 5. Access Fundamentals Retrieve fundamental financial metrics for any instrument:

python Copier le code from morningpy import Fundamentals

f = Fundamentals("AAPL").overview() print(f) 📰 6. Retrieve Latest News python Copier le code from morningpy import News

news = News("AAPL").latest(limit=5) for article in news: print(article.title) 🧩 7. Explore the API Modules The main modules available in MorningPy include:

Price — real-time & historical data

Fundamentals — ratios, financials, metadata

ETF — composition, analytics

News — aggregates & filters market news

Portfolio — analytics & helper tools

More details here:

👉 API Reference

🧠 8. Explore Example Notebooks If you prefer learning interactively:

👉 Notebooks

These include examples for:

price analytics

portfolio construction

fundamentals screening

ETF data extraction