Hello everyone,

I’m trying to better understand how people structure the workflow around an automated trading strategy, especially after the strategy logic itself is written.

In QuantConnect, the core algorithm is code, but there are still many surrounding steps that need to be handled carefully:

deciding which data is required

preparing and validating the data

running backtests

comparing backtest vs paper/live behavior

tracking why trades did or did not happen

managing risk rules

reviewing logs and monitoring live behavior

I’m not asking anyone to share their strategy, alpha, or proprietary details.

I’m more curious about the operational side of the process:

For you, is the bigger pain writing the strategy logic itself, or managing the workflow around testing and operating it properly?

How do you currently keep track of the full strategy lifecycle — research, backtest, paper trading, live monitoring, and revisions?

Would a more visual representation of the strategy workflow be useful for debugging and review, or do you prefer keeping everything purely in code?

Which part tends to be the most painful: data preparation, backtest validation, paper/live comparison, execution logs, monitoring, or risk checks?

I’m asking because I’m studying how systematic traders think about strategy operations, not just strategy logic.

Any feedback from people who actively use QuantConnect would be really helpful. Thank you!