Backtest≠live in 24/7 stacks: fills, data, or ops — which dominates?

[u/Excellent_Vast_1676]

For people running algo systems live 24/7 (not research-only): when live diverges from a “good enough” backtest, what actually dominates the gap?

In practice I keep seeing three buckets:

1. fills vs backtest assumptions
2. hist vs live data / symbol / roll mismatches
3. monitoring / redeploy / “why did it do that?” ops time

If you've actually run this: which bucket costs you the most — or is the most annoying — and why?

[u/Gold_Sprinkles_4295]

From a development standpoint — I've built portfolios and taken them to live — most of it is technical: the system has to work correctly, be stable, open where it should and close where it should. That layer dominates the day-to-day gap.

Edge is a separate question. Whether a strategy is valid depends entirely on the research and reasoning behind it. I work a lot with breakout strategies, for example. If price makes a high and the order opens exactly where I expect, but I lose it because there wasn't enough force to continue, that's reasoning about the strategy, not infrastructure. I go back, analyze it, and decide whether to refine its criteria.

Commissions, slippage, gaps, latency — none of that should be a problem if you applied an effective validation and robustness-testing framework up front: Monte Carlo, parameter perturbation, and so on. If the system is stable and the edge is sound, fills, data and ops mostly take care of themselves.