Hi Product Hunt! I'm sharing OpenBacktesting, an open-source Python framework for historical strategy research. A headline return is only the start of a backtest. The useful questions are how orders filled, what costs were applied, where drawdown came from, and how results change with different parameters. OpenBacktesting brings those details into an inspectable local workflow. You can: • Write bar-by-bar Python strategies with built-in indicators and configurable inputs. • Model fees and execution timing, then inspect orders, trades, equity, and risk statistics. • Compare parameter experiments and save reports for later analysis. • Add the optional Studio to edit local code, explore charts and logs, and reopen or share saved results. The core works independently of Studio. The source is Apache 2.0 licensed, and the quickstart includes bundled data. Studio has a one-command macOS installer. This release focuses on historical, single-asset research. What would make this workflow more useful for your strategy research? I'd love feedback on the API, result inspection, and Studio. Get started: https://openbacktesting.com Source: https://github.com/lucidcog/open...

OpenBacktesting

Open-source Python backtesting with a local visual workspace

Open-source Python backtesting with a local visual workspace