Using OpenBB to Create a Data Processor

In this article, we will attempt to create a backtesting framework in Python, which should include the following features: Modularity – We want it to be modular, allowing for easy combination and replacement. Scalability – The code should be easily expandable. Support for single-asset and multi-asset strategies Access to historical equity data and multiple data providers Inclusion of trading costs and commissions Performance metrics included To achieve this, we will need several key components, including: Data management: Responsible for the import, storage, and retrieval of OHLCV data, as well as any alternative data sources used for generating signals. Signal generation: Contains the logic for analyzing data and generating buy/sell signals based on predefined strategies or indicators. Execution engine: Simulates trade execution based on signals, considering commissions, slippage, and optionally taking into account bid-ask spreads.