Direct answer
To assess “Spread By Session” in a self-contained way, you need inputs that let you (1) define what “session” means, (2) measure the spread consistently inside each session, and (3) validate provenance and timing quality. Because spreads depend on varying market conditions and provider execution, you also need to document assumptions and limitations so others can reproduce your results.
If you want to explain or verify the concept independently, the core dataset is the set of spread observations paired with timestamps, plus metadata describing how those spreads were calculated and how session boundaries were chosen.
Mechanism and definition
“Spread” usually means the difference between a quoted buy price and a quoted sell price at a given moment. “Spread By Session” groups those spread measurements into time windows (sessions) and compares how the spread behaves across those windows.
To make the grouping meaningful, you need:
- Session definition (stable inputs): what time windows you call each session (for example, by clock time or by a named market period). Pick one reference clock (including time zone) and keep it consistent.
- Measurement timestamps (stable inputs): the exact timestamp associated with each spread observation, with an explicit time zone or conversion rule.
- Spread calculation method (stable inputs): how spread was computed (e.g., using bid/ask at the same timestamp, whether mid-price was involved, and whether values were rounded).
- Instrument and quote consistency (stable inputs): the specific currency pair(s), contract type, and quote source so the spread observations represent the same underlying market concept.
Variable factors that must be separated from the mechanics include market liquidity changes, event-driven volatility, and provider-specific quoting or execution behavior. Your data should record enough context to distinguish measurement rules from changing conditions.
Evidence or example with explicit assumptions
A typical way to structure the required data is to build a table where each row is one spread observation:
- Fields needed per observation: (1) timestamp, (2) bid price, (3) ask price, (4) computed spread, and (5) instrument identifier.
Then you apply the session logic:
- Assumption: “Session A” and “Session B” are defined by fixed start/end times in the same time zone.
- Assumption: each observation uses bid and ask from the same timestamp (or within a narrowly defined tolerance).
After grouping, you compute summary statistics per session, such as an average or median spread and a dispersion measure. Historical patterns can be described, but the key is that everyone can repeat the same steps using the same session boundaries and the same spread-definition rules.
At least one material limitation to plan for: if timestamps are misaligned (for example, mixed time zones, different daylight-saving handling, or delayed feed timestamps), the grouping into sessions becomes wrong even if the spread numbers look plausible.
Limitations, risks, and what can fail
Several failure modes commonly undermine Spread By Session assessment:
- Inconsistent session windows: if one dataset uses different time zones or moving boundaries, comparisons become unreliable.
- Missing or stale quotes: sessions may appear to have “tight” spreads simply because quotes were unavailable or late; you need to track quote completeness.
- Outliers from bad ticks: sudden spikes can come from data errors or sparse quoting. Robust checks (like outlier handling rules) should be documented.
- Provider-specific quoting behavior: different quote sources can show different spreads for the same nominal instrument, so provenance matters.
Also, outcomes vary with market conditions, costs, execution, and jurisdiction. Even a well-measured historical pattern does not establish future results.
Verification and next questions
To independently verify claims about Spread By Session, you should be able to answer:
- What session boundaries were used? Provide start/end times and the time zone (and conversion rule).
- How was spread computed? State the formula and rounding/normalization rules.
- What is the provenance of quotes? Identify the data origin and whether timestamps reflect quote time or receipt time.
- How were quality issues handled? Document missing data rules, outlier treatment, and any filtering.
If you share these details, others can replicate the grouping and re-check the results using the same assumptions.