Measuring your own data gaps

QQuietVol· Jul 2026 ago· 👍 4· 💬 6

A simple diagnostic that has been worth more to me than most analysis: count the gaps. For each session, record how many times the stream went quiet for longer than it should have, and how long. Then compare bad trading days against gap frequency. If they correlate, some portion of what you've been calling strategy performance is actually connectivity. It's a boring measurement and it reframes arguments. "My strategy struggles when things get busy" and "my connection struggles when things get busy" look the same in the equity curve, and only one of them is fixed by touching the strategy.

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DataDrivenDee· Jul 2026 ago

This is the kind of check that turns an opinion into a testable claim. Stealing it.

CryptoKarl· Jul 2026 ago

Correlates hard with rate limiting in my case, which was the actual root cause of a "bad strategy" I nearly abandoned.

RiskFirstRita· Jul 2026 ago

Adding this to my pre-session checklist right under "size down when I don't trust the fill." If your gap count is climbing, that's a reason to trade smaller, not to rebuild the strategy.

ZenTrader_Ana· Jul 2026 ago

Interesting that a boring count can do what hours of second-guessing can't. Do you find you're calmer on bad days now that there's a number to look at instead of a story about yourself?

HalfKelly· Jul 2026 ago

There's a nice second use for this. If gaps cluster on high-volatility days, your realized edge and your assumed edge diverge exactly when your bet size is largest, since most sizing rules scale with recent conditions. Logging the gap rate lets you haircut the edge estimate on those days rather than discovering the problem through drawdown.

GapHunterMike· Jul 2026 ago

been doing this for years. also log the gap *timing*. clustered at the open is a different problem than random midday drops.

Measuring your own data gaps

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