Direct answer
To assess pair session behaviour, you need inputs that (1) describe what “session” means in time and timezone, (2) capture market activity during those times using timestamped observations, (3) record where the data came from and how it was processed, and (4) allow quality checks that separate stable mechanics from variable conditions like costs, execution, and changing market regimes.
Mechanism and definition: what “pair session behaviour” is
“Pair session behaviour” refers to how a currency pair’s observed behaviour varies by trading sessions—typically the major market time windows used by traders and liquidity providers. In practice, you assess it by grouping timestamped observations into session buckets and comparing key features across those buckets (for example, typical volatility levels, average movement size, or activity intensity).
Because session boundaries are not universal, “what data is needed” starts with defining session rules:
- Session start/end timestamps and the timezone used.
- Whether boundaries use server time, local market time, or UTC.
- The data sampling interval (tick, 1-minute, 5-minute, etc.).
Then you need observations of the pair at the sampling interval you will analyze:
- Price series (at minimum, bid/ask or a consistent mid-price definition).
- Volume or an activity proxy if available.
- Corporate-event adjustments are usually irrelevant for FX spot, but you still need to document any discontinuities or data resets.
Evidence or example: data inputs and what to verify
A workable assessment dataset commonly includes these inputs.
1) Session metadata
- A session calendar: start/end times and timezone.
- Clear handling of daylight saving time changes if your timezone is not UTC.
- A mapping from each timestamp in your price series to a session bucket.
2) Market data series (and alignment)
- Timestamped bid/ask and/or mid prices for the chosen currency pair.
- For each timestamp, the observation interval is known (so returns and movement measures are computable).
- If you compute returns, define the formula and horizon (e.g., log-return from t to t+Δ). This must match your sampling interval.
3) Costs and execution context (often overlooked)
Even when you only analyze historical prices, you should still record cost-relevant details because bid/ask spreads and execution assumptions can change which “behaviour” you observe.
- Evidence of the price type used (bid, ask, mid).
- Bid/ask spread information if you have it.
- Any known data-source limitations (for example, whether prices are indicative vs executable).
4) Data provenance and quality checks
To independently verify results, you need documentation of:
- Source identity (data vendor, broker feed, platform export) and the retrieval method.
- Preprocessing steps: filtering, resampling, interpolation, missing-data handling, and timezone conversion.
- Completeness checks: missing timestamps, outliers, and duplicated records.
5) Reproducible calculations and baselines
Your analysis should include explicit assumptions:
- Baseline period used for comparison (e.g., “compare session A vs session B over the same date range”).
- Statistical measures you compute (means, medians, volatility proxies, frequency of large moves) and how they are computed.
- Rules for outlier treatment if you exclude extremes.
One material limitation example: if your price series has missing minutes during a session, volatility or activity estimates can be biased. A quality check is to verify coverage by session (for each session bucket, count how many observations exist and compare across sessions).
Limitations and risks: what can fail
Key limitations to treat as risks rather than afterthoughts:
- Historical relationships may not hold. Session-linked patterns can change across regimes, so historical comparisons cannot guarantee future behaviour.
- Data quality can dominate conclusions. Missing data, timezone mistakes, or inconsistent price definitions (mid vs bid/ask) can create apparent differences between sessions.
- Costs and execution effects can distort “behaviour” measures. If you analyze mid prices but real trading faces bid/ask spread and slippage, the observed behaviour you study may not match what outcomes would feel like under execution.
Verification and next question
To verify claims about pair session behaviour, you should be able to answer these “ready-to-check” questions:
- Can you reproduce the session bucket assignment from documented timestamps and timezone rules?
- Are price definitions consistent (bid/ask/mid) and documented?
- Do your session buckets have comparable data coverage and sampling intervals?
- Are your calculations fully specified (formulas, horizons, baseline windows)?