Direct answer
To assess AUD USD (the exchange rate between the Australian dollar and the US dollar), you need four groups of information: (1) definitions and calculation mechanics, (2) the specific data inputs you plan to use for AUD and USD, (3) provenance and timeliness of those inputs, and (4) quality checks and limitations that explain what your assessment can and cannot support.
This article focuses on non-time-sensitive, general guidance for building a self-contained assessment. It does not assume live prices, and it does not provide trade recommendations.
Mechanism and definition
AUD USD is usually quoted as the number of US dollars per one Australian dollar (USD per AUD). Before you assess anything, define the exact quote convention you are using (base/quote currencies) and how you will compute or compare values.
Next, separate stable mechanics from variable conditions:
- Stable mechanics: the identity of the currency pair, consistent units, and how you transform one data series into another (for example, converting rates into index changes).
- Variable conditions: market regime changes, differences in bid/ask and spreads, execution timing, and provider-specific data formatting.
Finally, state assumptions explicitly for any example you compute. For instance, if you use “daily change,” specify the timestamps (close-to-close vs. open-to-open) and whether you treat missing observations as removed, forward-filled, or interpolated.
Evidence and example: what data to collect
A practical way to organize “what data is needed” is to collect inputs for AUD side, USD side, and the exchange rate itself.
1) Exchange-rate data (the target you assess)
Collect:
- AUD USD time series you plan to analyze, including the timestamp definition (daily close, intraday bar times, etc.).
- Quote convention (e.g., USD per AUD) and the data field (mid, bid, ask), because these produce different values.
Provenance matters: if your dataset comes from a specific vendor, platform, or feed, record how they timestamp and format observations.
2) Macro and policy variables (context for AUD and USD)
Collect the categories of indicators you can justify and explain:
- Interest-rate expectations or policy signals that affect both currencies.
- Inflation and growth indicators relevant to Australia and the US.
- Employment and consumption indicators that may influence currency sentiment.
When you gather these, document:
- Release dates (publication time, not just the date label).
- Revision history (some datasets revise past values).
- Units and transformations (percent vs. index level, seasonally adjusted vs. not).
3) Market data proxies (cost and execution context)
Even if you are not trading, you need to understand what changes your observations:
- Bid/ask spread information (or at least whether you only have mid prices).
- Liquidity or volatility measures if your assessment depends on variability.
- Trading-session definitions if your series is intraday.
This helps you avoid mistaking provider artifacts for market moves.
4) Data-quality and transformation metadata
To prevent silent mistakes, track:
- Missing data handling rules.
- Outlier treatment (winsorization, removal, or separate flagging).
- Currency/unit consistency across series.
Limitations and risks (material failure modes)
There are several limitations that can make an “assessment” misleading:
- Non-stationarity: historical relationships between AUD and USD drivers may change when regimes shift.
- Timeliness mismatch: using macro data that was released after the timestamps of your exchange-rate observations can create false alignment.
- Quote-field mismatch: comparing mid prices from one source to bid/ask-derived estimates from another can produce systematic differences.
- Provider differences: two feeds can use different conventions (rounding, sampling frequency, or time zone).
A clear failure mode is assuming that correlations imply predictive power. Even strong historical association does not establish future results, especially under changing costs, execution conditions, and market sentiment.
Verification and next question
To independently verify your assessment, you should be able to answer these checks without needing “live” data:
- Can I reproduce the mapping from raw inputs to the values I analyze? (units, timestamps, quote convention)
- Do I know exactly where each input came from, and when it was valid? (provenance, revision dates)
- Did I document assumptions for any transformation (daily change, log returns, rolling windows)
A useful next step question is: Which exact quote convention and timestamp definition are you using for AUD USD, and do the macro inputs share a compatible timing reference?