The missing gold alerts may matter more than the size of the archive

The archive is a collection mechanism

Imagine studying a gold alert service by downloading every row visible on its website. The download may be complete relative to that page, yet incomplete relative to the alerts the research question intends to study. Some messages may never have arrived, some may have failed validation, and some may have been excluded by a display filter. A database row count cannot settle which population it represents.

This question differs from an open trade whose outcome is not yet known. Here, an entire record or required field is absent from the research file. It also differs from duplicate delivery, which adds copies. Before estimating anything, I would draw a simple inventory of the stages at which an eligible event can disappear: generation, transmission, acceptance, storage, display and extraction.

A statistical principle worth borrowing carefully

Statistics Canada distinguishes random from systematic non-sampling errors and explains that a larger sample does not remove systematic bias. Its methodological guidance also separates coverage, measurement, nonresponse and processing problems. These concepts were developed for statistical data collection, not as an assessment of any gold signal provider. Applying them to an alert archive is my research interpretation.

The agency's archived household-spending quality guide makes another useful distinction: the nonresponse rate alone does not measure the bias in an estimate. The relationship between missingness and the characteristic being studied matters. For gold research, a modest gap concentrated in turbulent intervals can be more consequential than a larger gap unrelated to the study's outcome. Whether that concentration actually exists must be tested.

Source notes: [1] Statistics Canada · Non-sampling error · [2] Statistics Canada · Survey of Household Spending 2008, nonresponse

Ask what could make missingness selective

One hypothesis is that a receiver becomes overloaded during bursts of market activity. Another is that a format change rejects a particular event type, while ordinary messages continue to pass. A third is that an export contains only currently visible setups and omits expired ones. These are candidate failure mechanisms, not claims about the present CY66 feed.

TradingView explicitly warns that webhooks may fail to reach their destination and provides delivery status in its alert log. That supports checking the sender as well as the receiver. It does not establish a loss rate, a direction of bias or the outcomes of missing events. If the sender log itself is unavailable, the denominator of expected records remains uncertain and should be reported that way.

Source notes: [3] TradingView · How to configure webhook alerts

Build a coverage table before a result table

My proposed worksheet groups scheduled observation windows by indicator version, timeframe and session. For each group, record expected eligible events when independently known, sender events, unique accepted events, rejected messages and records with complete evaluation fields. Keep technical rejection reasons separate from an explicit strategy cancellation. Both can remove a row from a screen, but they answer different questions.

Compare coverage around preidentified busy intervals with quieter intervals, using the same definition throughout. Do not choose the supposedly important periods after inspecting favourable results. A gap that coincides with volatility is a reason to investigate the collection system; it is not proof that missing signals lost money. Recoverable original logs should take priority over modelling guesses.

A bounded calculation, explicitly hypothetical

Suppose a fictional experiment has exactly 100 eligible signals, established independently of the receiving database. Eighty have completed, verified binary evaluations under a fixed rule; forty of those satisfy the rule. Twenty have no usable outcome. The observed subset's proportion is 40 divided by 80, or 50%. It is not automatically the proportion for all 100 eligible signals.

If every missing evaluation failed the rule, the full-population proportion would be 40%. If every missing evaluation satisfied it, it would be 60%. The 40%–60% interval is a sensitivity bound under this deliberately simplified binary setup, not a confidence interval and not an estimated CY66 win rate. It assumes the denominator and observed classifications are correct. It says nothing about position sizes, payoff magnitudes, costs or account returns.

What to publish when recovery is incomplete

Report the collection interval, the eligible population if known, the number and type of missing records, and the conclusions that depend on an assumption about them. Weighting the observed rows can be useful only with a defensible model of the missingness process; it cannot conjure absent evidence into existence. Label any model-based result separately from directly observed counts.

My publication threshold is narrower than a claim of completeness: the reader must be able to see what is observed, what is missing and how sensitive the conclusion is to that gap. This edition is a research-method article because no newly verified result population is available. A selected success can teach a mechanism, but it cannot repair the missing denominator of a performance claim.