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A timing mark and a price response need not occur in the same bar. MacKinlay's event-study framework indexes returns relative to an event and adds abnormal returns across a declared window. That supplies a useful research structure, but it does not supply a Gann cycle or a first-response rule. The following latency worksheet is an editorial extension. Before examining outcomes, define the mark, the expected-return benchmark, and a qualifying response. For this example, require cumulative abnormal return to reach positive 1 percent within three sessions. Start the clock at the scheduled mark. A window with cumulative values of 0.2, 0.7, and 1.1 percent first qualifies on session three. Another with 0.4, 0.6, and 0.8 percent never qualifies within observation. Keep the second window in the record as no response by the deadline. Do not assign it a delay of zero or drop it from the denominator. Report the fraction responding by each session alongside the delays of responders. Otherwise, an average of fast successes can conceal many failed windows. The result describes when a specified response appeared, rather than whether a later chart contains a convenient turning point.

Try it

Synthetic study: first qualifying responses occur at sessions 1, 2, 3, and not within 3, across four windows. What fractions respond by session 2 and by session 3? What is the median delay among responders?

Limits

A threshold crossing is not causal attribution. Serial dependence, an estimated benchmark, and overlapping windows need separate treatment. This worksheet is not a survival-model estimator or a technique attributed to Gann.

Source

A. Craig MacKinlay, Event Studies, Section 5, aggregation through time and equation 10, printed p. 21; event-time definition on p. 18. Peer-reviewed Journal of Economic Literature 35(1), 1997, pp. 13-39. Article reproduced in a public court exhibit; checked against the Boston University scan. Its earnings illustration covers January 1989 through December 1993. Timing-rule adaptations below are editorial, not Gann methods.

Two identical numbers do not prove that two ephemerides describe the same thing. Horizons separates the target body from the observing center, and its quantities can refer to different coordinate frames and correction models. A saved longitude without these settings is an incomplete measurement. Keep the full request together with the response header and data rows. Record the target identifier, center or site coordinates, time scale, reference frame, quantity numbers, and step size. Retain the solution information printed by the service because its underlying data are updated. If a separate package computes lunar nodes, add that package's version and whether it uses a true or mean node; a Horizons planet query does not document those choices for it. Consider a failed reproduction. Both files name Venus and the same timestamp, but one uses an Earth-center observer and the other a Sun-center observer. A numerical agreement to two decimals would not repair this mismatch. First compare the meaning of each column; only then apply a numerical tolerance to matching measurements. This order catches errors that a checksum or rounded-value comparison cannot.

Try it

Two files match target, timestamp, and displayed longitude to two decimals, but one uses Earth center and one Sun center. Does this reproduce the same observer-based measurement? Name the decisive mismatch.

Limits

This is an astronomy provenance procedure. It does not validate a node model, astrological interpretation, or market hypothesis merely because the request can be repeated.

Source

JPL Horizons manual, Introduction, underlying-data update policy; General definitions, Center; observer-table output and header settings. Primary institutional astronomy reference. Worked examples are explicitly hypothetical, not observed market effects.