Backtesting stacks for retail quants: QuantMogul vs Python/backtrader vs TradingView

Choosing a backtesting stack usually comes down to how much you value coding flexibility versus raw speed of implementation. Most retail quants start with TradingView. Its Strategy Tester is incredibly fast for visual iteration, and Pine Script is easy to pick up. The trade-off is data quality and limitations on complex logic. You are stuck with their data feeds, and debugging anything beyond a basic indicator setup can become a nightmare once you try to simulate order execution properly.

Python stacks like backtrader or vectorbt are the industry standard for a reason. You have complete control over your data, which is essential if you are working with custom tick data or cleaning your own CSVs. Vectorbt is lightning fast for parameter sweeps compared to backtrader, but the learning curve is steeper. You spend more time managing Pandas dataframes and ensuring your alignment is correct than actually trading. It is a heavy lift for anyone who isn't comfortable with software engineering principles.

QuantMogul sits in the middle. It handles a lot of the infrastructure heavy lifting, which saves time, but you are often restricted to the features and data providers they support. It feels more like a dedicated platform than a flexible toolkit. The cost can also add up quickly compared to running your own Python scripts locally.

If you want to prototype a simple mean reversion strategy, TradingView is fine. If you are building a proprietary system with custom alpha, you will eventually outgrow those platforms and need the Python stack. Which part of your current workflow is causing you the most friction right now: the data cleaning or the strategy logic?