Direct answer: verify liquidity by session information
Information about “Liquidity By Session” can be verified by separating (1) stable definitions and measurement choices from (2) variable market behavior and (3) variable data-provider rules. Use a source hierarchy: start with official or methodological documentation for how a dataset labels sessions and measures liquidity; then reproduce the computation locally on the same dataset using the same time windows; finally test whether conclusions persist when you change reasonable assumptions (time zone, session boundaries, and cost filters).
Because no single computation is universally agreed, verification is less about “proving” one truth and more about demonstrating that a claim is consistent, reproducible, and limited to the right assumptions.
Mechanism and definition: what “liquidity by session” usually means
“Liquidity” generally refers to how easily assets can be traded with limited impact. In practice, different proxies are used, such as:
- Volume: how much trading occurs.
- Bid–ask spread: the difference between buy and sell quotes (narrower can indicate better immediate trading conditions).
- Order-book depth: how much trading interest sits near the best price.
- Market impact proxies: changes in price after trades.
“By session” usually means grouping activity by time blocks (for example, regional trading hours). Session labeling is the critical input: one source might define sessions by a specific time zone and exact start/end times, while another might use different boundaries.
To verify information, you need to confirm three things:
- Which liquidity proxy is claimed (volume, spread, depth, or another metric).
- How sessions are defined (time zone, boundaries, and how dates are handled around midnight).
- What dataset and quote/trade timestamps are used (including whether data is bid/ask quotes, executed trades, or broker-aggregated streams).
Evidence and reproducible verification steps
Use a reproducible workflow that you can rerun when assumptions change. One approach:
- Lock definitions and assumptions
- Choose a liquidity proxy (e.g., volume per session, or average spread per session).
- Fix the session timetable: start time, end time, and time zone.
- State whether you use all data points or apply filters (for example, excluding outlier spikes).
- Recompute on the same dataset
- Download or access the same underlying time-stamped dataset referenced by the claim.
- Convert timestamps into the agreed time zone.
- Assign each record to exactly one session using your fixed boundaries.
- Compute the liquidity metric per session using the same aggregation rule (mean vs. median; raw vs. normalized).
- Check stability across reasonable variants
- Shift session boundaries by a small amount (for example, start time changes) and re-run.
- Compare results using median vs. mean if the distribution has spikes.
- If the claim focuses on one session, test adjacent sessions too, to avoid “cherry-picked” contrast.
- Document reproducibility outputs
- Record the metric values per session and the exact code or calculation steps.
- A verified claim should be explainable in terms of these steps, not just asserted.
If the original information cannot be reproduced from the stated definitions and dataset rules, treat it as unverified or conditional on hidden assumptions.
Limitations and risks (material failure modes)
Several limitations can make “liquidity by session” information unreliable if not handled explicitly:
- Session-boundary ambiguity: different time zones or start/end rules can shift records and change per-session averages.
- Provider methodology differences: one source may compute liquidity using quotes, another using executed trades; these proxies can disagree.
- Cost and execution effects: spreads and effective execution conditions can vary with commissions, order types, and execution quality; a quoted spread proxy may not equal realized trading conditions.
- Non-stationarity: historical relationships may not persist due to changing market structure, volatility regimes, or liquidity supply/demand.
A material failure mode is concluding a future tendency from an old pattern without rechecking whether the same measurement rules and market conditions still hold.
Verification checklist and next question
Use this checklist to decide whether a claim is sufficiently verified:
- Does it clearly state the liquidity proxy used?
- Does it define session boundaries and time zone precisely?
- Can you reproduce the grouping and the metric on the same dataset?
- Do results remain similar under reasonable assumption changes?
- Are limitations acknowledged (especially differences between proxies and non-stationarity)?