Build a Python Algorithmic Paper-Trading System from Scratch

I build a momentum-based paper-trading application in Python, connecting Massive market data, SnapTrade brokerage linking, and Alpaca paper trading. The walkthrough covers setup through a working Django interface, with practical examples of database models, API integrations, and troubleshooting.

■ Account and project setup
- Create service accounts, configure credentials, and establish a Django project
■ Data and strategy implementation
- Model stocks, prices, portfolios, and trades; retrieve market data and calculate 12-month momentum
■ Application and paper-trading workflow
- Build dashboard and trading views, connect a simulated brokerage account, and test generated trade signals
■ Scope
- Focuses on a 50-stock universe and paper trading; backtesting is left as a placeholder, and live-money trading is not demonstrated

For Python developers and learners exploring algorithmic trading, this provides a step-by-step reference for building and testing a paper-trading workflow. Follow along by setting up the required accounts and credentials, then run the project against a simulated brokerage account before considering any further development.