What data is needed to assess MT5 Orders?

Direct answer

To assess MT5 Orders, you need a complete set of order and execution data, plus information about where that data came from, when it was recorded, and whether it is consistent enough to support your calculations. Without these inputs and checks, any conclusions can be misleading because order intent, fill details, and market context may not align.

Mechanism and definition (what “assess” means)

An MT5 “order” can be understood as the combination of (1) the instruction you send (intent) and (2) the results the trading platform records (execution). Assessing an order usually means you can answer questions such as:

• What instrument and direction were requested?
• What quantity and order type were used?
• When was the order submitted and when did it become active?
• What fill price(s) and timestamps were actually recorded?
• What costs or fees were applied, and where are they shown?
• Did the platform later modify or cancel the order, and why (if available)?

To do this reliably, you collect both the static fields (how the order was defined) and the dynamic fields (how it was executed over time). Stable mechanics are the structure of these fields; variable conditions include execution quality, spreads, commissions, slippage, and account-specific reporting.

Evidence and example (data checklist)

Use this checklist to assemble the minimum dataset needed for independent verification:

1. Order intent data (what you asked for)

• Instrument or symbol identifier
• Side/direction (buy or sell)
• Order type (for example, market-like vs. pending-like)
• Requested volume (lot size/quantity)
• Requested price (if the order requires one)
• Time conditions (good-til-time/expiration if present)
• Stop/limit parameters, if applicable

2. Execution and lifecycle data (what actually happened)

• Order submission time (as recorded by the platform)
• Activation time (if the order waits for conditions)
• Execution or fill time(s)
• Fill price(s)
• Status changes (placed, partially filled, filled, canceled, rejected)
• Any recorded reason codes or text notes that indicate why an action occurred

3. Provenance and account context (where the data came from)

• Which account the order belongs to (account identifier)
• Which platform or terminal logged the data (and whether multiple terminals were used)
• The timezone used in timestamps (platform-local vs. UTC)
• The exact data source for “costs” (platform statements, order journal, deal history, or trade reports)

4. Timeliness and consistency assumptions (how you will compute) Make assumptions explicit before calculating outcomes. For example:

• Assume timestamps are comparable across logs only if the same timezone basis is used.
• Assume fill prices correspond to the recorded execution/deal entries, not the requested price.
• Assume costs are taken from the account report fields shown for that execution.

Material example limitation: If you compute profit using the requested price while the order filled at a different price, your result will not match what the platform recorded. The correct data to use is the recorded fill/deal data.

Limitations and risks (what can fail)

At least one material failure mode is common: data mismatch between “intent” and “execution.” For instance, an order can be accepted but filled at a different price due to execution conditions, or it can be partially filled, producing multiple fills that must be aggregated.

Other limitations to account for:

• Missing fields: Some cost details may not be visible in the order view and may only appear in a separate report view.
• Timeline ambiguity: Different logs may use different timezones or include latency; this affects any attempt to align order events with market moments.
• Historical relationships: Past execution behavior does not guarantee future results; execution quality can change.
• Provider/account differences: Reporting formats vary by account setup and broker configuration, so a field name in one terminal layout may not match another.

Because these issues are structural, you should not treat a single number (like a “profit” snapshot) as proof of correctness unless you can trace it back to the order and execution fields used in your calculation.

Verification and next question

To verify that your dataset is sufficient, you should be able to reproduce the main computed outcomes from the same fields the platform reports: instrument, direction, volume, fill price(s), and the applicable costs. If you cannot trace each component to a specific recorded value and timestamp, treat the assessment as incomplete.