Direct answer: the data inputs to assess USD CAD vs AUD USD
To assess how USD CAD compares with AUD USD, collect data that lets you (1) define each rate consistently, (2) measure movements in a comparable way, and (3) check that the numbers are trustworthy and timely. Because pair “comparisons” depend on your method, the minimum workable dataset includes: the raw exchange-rate series for both currency pairs, the exact definition of each quoted rate, the timestamps/timezone and sampling frequency, and the method you will use to transform prices into returns or differences. Also gather cost and execution-relevant details if you will evaluate investable outcomes, such as bid/ask spreads and any conversion fees, but avoid treating them as constant.
You can keep the assessment non-real-time by using historical data, as long as you record the data vintage (when it was downloaded) and ensure both series cover the same date range and sampling rules. Historical relationships alone do not guarantee future behavior.
Mechanism and definitions: what data each currency pair needs
A “currency pair” is a quoted exchange rate. For pair comparisons, you must know what each quote means:
- USD/CAD (USD CAD): how many Canadian dollars (CAD) are paid for one U.S. dollar (USD), using your chosen data provider’s quote convention.
- AUD/USD (AUD USD): how many U.S. dollars (USD) are paid for one Australian dollar (AUD).
Even when the currencies overlap (USD appears in both), the quotes move in different directions and units. That means the same market event may appear as different “strength” patterns depending on quote orientation.
Core data to collect (for each pair):
- Raw rate series (bid/ask or mid, depending on what you will use). Record whether the series is mid-market, bid, ask, or last trade.
- Timestamp information: timezone, sampling frequency (tick, 1-minute, daily), and whether timestamps represent the close, open, or an average.
- Rate-definition metadata: how the provider handles holidays, missing prints, rounding, and whether values are adjusted.
- Return or change methodology (your own, stated explicitly). For example, you might compute percentage changes from consecutive observations, but you must apply the same method to both pairs.
Comparable transformation choices:
- If you compute returns, specify formula and whether you use simple or log returns.
- If you compute differences, specify the units (e.g., “percentage return spread” over the same timestamps).
- If you compute correlations, specify the window length and whether you test stability over subperiods.
Evidence and example checks: how to validate the data you gathered
With no real-time assumptions, you can still run meaningful, verifiable checks. Use a consistent workflow for both pairs.
1) Alignment check (time coherence):
- Ensure both series use the same date range and sampling rule.
- If one series has missing observations, document the handling method (e.g., dropping or carrying forward). Different choices can change comparison results.
2) Unit/orientation check:
- Confirm that you are comparing like-with-like. Because USD/CAD and AUD/USD have different quote orientations, compare either (a) their rate changes after using consistent formulas, or (b) their implied effects in USD terms only if you first translate units.
3) Quality check (data continuity):
- Look for outliers caused by bad ticks, stale quotes, or rollover events.
- Confirm there are no silent regime changes in the provider’s methodology (for example, switching from bid/ask to mid).
4) Calculation reproducibility:
- Save your computation settings: formula, window lengths, and any filtering thresholds.
- Record data source identifiers and the download time, so another person can repeat your exact steps with the same vintage.
Limitations and risks: what can fail in USD/CAD vs AUD/USD assessments
Several material limitations can reduce confidence in comparisons:
- Non-stationarity: Relationships between currency pairs can change across regimes. Correlation or “relative strength” in one period may not persist. - Provider and quote convention differences: Two datasets may label “the same pair” but use different pricing definitions (mid vs last, bid vs ask, session close vs intraday snapshot). This affects measured returns. - Timeframe sensitivity: Daily data may show different behavior than intraday data. Even with the same formula, sampling frequency changes noise and correlation. - Costs and execution frictions (if you extend to action): Spreads, fees, and slippage are not constant.