Execution quality cannot be judged from one fast fill or one frustrating order. A useful assessment requires a series of records showing requested prices, completed prices, timing, rejections, and market conditions. Without that evidence, claims about speed or fairness remain difficult to test.
A cfd broker should provide enough transaction detail for clients to reconstruct what happened. Five records are especially useful when comparing providers or investigating a result that differs from the chart.
Requested and Filled Prices Reveal Slippage
The order record should distinguish the price requested or triggered from the final fill. Positive and negative slippage both matter. A provider that reports only unfavorable differences gives an incomplete view, while a strategy review that ignores favorable fills may exaggerate cost.
Group the results by order type and market condition rather than averaging every trade together.
Millisecond Timestamps Clarify the Sequence
Second-level timestamps can be too coarse during fast markets. More precise records help establish whether the order arrived before, during, or after a rapid repricing. Client-device time, server-receipt time, and execution time may represent different points in the process.
Latency should be measured across the full route, not assigned automatically to the platform or provider.
Rejection and Requote Reasons Need Categories
An order may fail because price moved, volume was unavailable, the market closed, margin was insufficient, or the instruction violated a minimum distance. Each cause suggests a different remedy. A generic “off quotes” message is difficult to evaluate without server detail.
Repeated failures around the same session or symbol can reveal a product-specific limitation.
Volatile Conditions Test the Published Policy
Imagine a stop order on a European index activating as the cash market opens after an overnight political surprise. The first available bid is below the trigger, and the order fills there. The account history records the fill but the simplified chart opens with a candle that hides several quote changes.
The cfd broker should be able to provide the relevant quote and execution sequence. A worse fill is not automatically improper, but it should be explainable under the written policy.
Aggregate Statistics Show Whether the Event Is Typical
Median execution speed, rejection rate, price-improvement frequency, and slippage distribution are more informative than one testimonial. The tail of the distribution matters because the costliest outcomes often occur during volatility.
Testing should cover more than one connection and device. A mobile order routed through cellular data may produce a different delay from a desktop order on wired internet. That difference does not automatically belong to the provider. Logging local connection quality, platform confirmation time, and server records helps allocate responsibility accurately. Repeat the test during ordinary liquidity before drawing conclusions from a volatile session. Reliable comparison depends on consistent order size, symbol, time window, and instruction type.
Order size should be varied carefully during testing. A small ticket may fill at the top quote while a larger one reaches several price levels. That is ordinary market impact, but it means results from minimum-size orders cannot be scaled mechanically. Compare sizes that remain reasonable for the displayed and typical liquidity, and separate provider behavior from the natural cost of demanding more volume.
Before funding a larger account, place a controlled sample of market, limit, and stop orders. Export every record, calculate slippage by order type, and ask for written clarification of any rejection. Choose the provider whose data can be reconciled with its policy, not the one making the broadest speed claim