What Data Is Needed to Assess “USD Reaction”?

Define “USD Reaction” and the measurement target

“USD Reaction” is an outcome concept, not a single built-in indicator. To assess it, first define what you want to measure. Common ways to define it include: the USD’s short-term move after a specific stimulus (such as an announcement), the USD’s relative strength versus a basket, or the sensitivity of USD changes to particular macro or policy variables. Without a clear definition, the data choice becomes ambiguous.

Also define the unit of measurement and directionality. For example, you may measure reaction as an absolute USD change, a relative change versus another currency, or an index move. State your horizon (intraday, multi-day, or longer). Reaction can look different depending on the horizon, so the same dataset may produce conflicting conclusions.

Data inputs: what to collect

To assess USD reaction in a way you can independently verify, collect four categories of inputs.

USD price data (outcome series) Pick a consistent USD representation: a USD index-like series, a set of major USD exchange rates, or another transparent benchmark. Gather prices at a defined frequency (for example, minute-level or daily). Keep metadata about market hours, trading calendars, and any data gaps.
Event or driver data (what triggers the reaction) If your definition is event-based, collect the event timestamps and a standardized description of the stimulus. If it is driver-based, collect the underlying variables you believe matter (for instance, inflation readings, labor market indicators, policy communication measures). Ensure the data has a documented release time and revision history.
Timing and alignment data (so you compare the same window) Reaction is sensitive to “when.” You need time-zone information and a method to align timestamps across datasets. Event windows (for example, from a few minutes before release until a few minutes after) must be stated explicitly.
Cost and execution context (to avoid confusing price moves with frictions) Costs and market microstructure effects can change observed moves without reflecting the underlying driver. Collect information you can support: bid/ask spreads if available, data-source methodology, and whether you are using mid prices versus trade prices. If you cannot obtain costs, treat that as a limitation.

Provenance and quality checks

You should treat provenance as part of the “data.” For each input, record: the provider name, the publication method, the update schedule, and any known revisions.

Perform quality checks that are independent of your hypothesis:

Missing data and outlier handling: Identify gaps around event times and check for abnormal spikes unrelated to the stimulus.
Timestamp consistency: Confirm that “event time” and “price time” are in the same time basis after timezone conversion.
Method transparency: Prefer sources that document how the series is constructed (for example, how an index is calculated).
Revision awareness: If macro data is revised later, your historical “driver” values may change. Note whether you use the first release or the latest revised figure.

Evidence or example you can reproduce (without assuming outcomes)

Suppose you define USD reaction as the USD’s percentage change from t-5 minutes to t+30 minutes around a public release time. The evidence you need is not a claim of “expected profit,” but a reproducible mapping:

Collect USD price series at a matching intraday frequency.
Collect the release timestamp and the driver value used for that release.
Compute the reaction metric exactly as defined (for instance, percent change over the window).
Compare reactions across multiple events that are similar in structure (same category of release) to see whether patterns exist.

This approach can reveal whether your measured reaction is consistent and how sensitive it is to window length or data frequency.

Limitations and failure modes to expect

Several limitations can invalidate conclusions even when data is carefully collected.

Confounding factors Many macro events cluster in time, and other market information may move USD simultaneously. A measured “reaction” could reflect a different contemporaneous driver.
Regime changes The relationship between drivers and USD moves can shift across periods. Historical co-movement does not guarantee future similarity.
Selection bias in event windows Choosing a window after seeing results can overstate apparent effects. Pre-specify the window and sensitivity-test it.