What data is needed to assess Multi Day Holding?

What to collect first

Multi Day Holding means keeping a position across more than one trading day (for example, from one day’s close until later days). To assess it, collect data that describes (1) the mechanics of holding, (2) the inputs and costs that affect results, and (3) the quality and timing of the information used to make decisions.

A practical way to think about the “assessment” is: you should be able to explain what would have happened under clearly stated rules, using documented inputs, without relying on unverifiable claims.

Mechanism and core data inputs

Start by defining the horizon and the rules that determine whether an outcome belongs to Multi Day Holding.

Holding definition and time stamps

Date/time boundaries: when the position is opened, when it is considered closed, and whether you measure from broker/server time or a different time standard.
Observation points: what you treat as “decision times” (for instance, at day boundaries or at specific session opens).

Position and trade-rule data To avoid mixing assumptions, record the exact rule set used to decide entries and exits:

Entry condition definition (what variables trigger the opening).
Exit condition definition (profit target, stop level, time-based exit, or discretionary exit).
Any rules for adjustments during the holding period (for example, moving stops or partial closes).

Cost and execution data Across multiple days, costs and execution quality often matter as much as price movement:

Transaction costs: spreads/commissions where available, and any funding-related costs or charges that can accrue while holding.
Execution details: fill timing (near open/close vs intra-day), and whether fills are assumed at quoted prices or actually recorded.

Market context data (non-predictive) Collect enough context to interpret results without claiming future accuracy:

Market regime labels you choose yourself (for example, “high volatility” vs “low volatility”) and how those labels were computed.
Event markers you include consistently (such as major scheduled news) if you use them for interpretation.

Evidence and example of a checkable assessment

A checkable assessment can follow this structure:

Assumptions: state what you assume about fills (e.g., fills occur at the observed price at the recorded time), and state how you measure entry/exit timing.
Example workflow (no live data required):
1. Pick a set of hypothetical trades defined by your entry/exit rules.
2. For each trade, compute whether it meets the Multi Day Holding definition using time stamps.
3. Apply the recorded cost inputs and compute net results (price movement minus costs).
4. Summarize outcomes by holding length buckets (for example, 2 days, 3–5 days, 6+ days) to see where results cluster.

Important: historical relationships do not guarantee future results. Use summaries to understand sensitivity to assumptions, not to promise accuracy.

Limitations and failure modes to consider

At least one material limitation should be part of your assessment.

Provenance and survivorship issues: if your trade list or provider data was filtered, re-labeled, or modified after the fact, the assessment can become non-reproducible.
Timeliness problems: using stale price data, mismatched time zones, or inconsistent server time can make “multi day” membership wrong.
Execution mismatch: backtests that assume fills at mid prices or at the exact moment a condition becomes true may not reflect realistic execution.
Regime dependence: if your holding works only under specific volatility, liquidity, or session conditions, mixing regimes can hide that dependency.
Cost underestimation: ignoring commissions, spreads, or day-to-day charges can materially distort net outcomes.

These failure modes are reasons you should verify inputs and assumptions rather than trust the final numbers.

Verification and next questions

To independently verify Multi Day Holding claims, ask for reproducible evidence that ties every outcome to documented inputs:

Can you show the timestamps and the rule set that makes a trade qualify as “multi day”?
Are costs and execution assumptions recorded in a way that another person can replicate?
Do the results depend heavily on one or two assumptions (for example, fill price timing)?
Are there explicit, testable conditions for exits (including time-based exits) and are they applied consistently?

If you are comparing approaches, also clarify how the “multi day” horizon differs from related concepts such as shorter intra-day holding or longer swing-style holding.