Building an Automated Stock Portfolio Backtesting Pipeline with Polygon, Alpaca API, and Python: Performance Analysis and Risk Management Optimization
An automated stock portfolio backtesting pipeline is essential for evaluating and optimizing investment strategies based on historical data. This article details how to build a backtesting pipeline with Python by combining Polygon's accurate stock data and Alpaca API's convenient trading features, and how to improve performance analysis and risk management. Stop wasting time on spreadsheets. This pipeline enables faster, more accurate, and data-driven investment decisions.
1. The Challenge / Context
The stock market is constantly fluctuating, and successful investing requires a systematic and data-driven approach. Backtesting, which tests investment strategies based on historical data, plays a crucial role in reducing risk and evaluating potential profitability. However, manually collecting and analyzing data is time-consuming and prone to errors. In particular, securing accurate and comprehensive stock market data, and effectively analyzing and visualizing backtesting results are major challenges. Currently, many individual investors and small investment groups face difficulties due to limited access to the data and tools needed for backtesting. This can exacerbate information asymmetry and lead to a decline in the quality of investment decisions.
2. Deep Dive: Alpaca API and Polygon.io
Alpaca API is a platform that allows programmatic trading of stocks, ETFs, and other financial instruments. Its key features include:
- REST API: Provides a
