Direct answer: the minimum data inputs
To assess scalping risk, you need data that supports four things: (1) a precise definition of “scalping risk,” (2) the inputs and assumptions used to measure it, (3) the provenance and timeliness of those inputs, and (4) quality checks that reveal when results are not reliable.
A practical way to think about it is to separate stable mechanics from variable conditions. Stable mechanics include how short holding times interact with fees and execution frictions. Variable conditions include market liquidity, trading costs, and how your orders are filled.
Mechanism: define the risk and the metrics you will compute
Start by defining what you are calling “scalping risk.” The same term can mean different failure modes, so you need to choose measurable components. Common components are:
- Cost sensitivity risk: how much small price moves are reduced by bid–ask spread and commissions.
- Execution uncertainty risk: how fills deviate from expected prices due to slippage or partial fills.
- Liquidity/volatility mismatch risk: how the market’s ability to move quickly without large adverse impact aligns (or fails to align) with the scalping time horizon.
- Operational/process risk: how latency, order handling, or platform behavior can change outcomes.
Next, map each component to the data you will use. For example, if you assess cost sensitivity, you need consistent information about spreads and fees; if you assess execution uncertainty, you need fill quality data such as realized price versus intended price.
Evidence and example inputs: what to collect and how to validate it
Collect data in three layers.
1) Core market and trading-cost inputs (measured or documented)
You need inputs that represent the trading environment you are actually exposed to:
- Bid–ask spread observations for the instruments you trade, over a period that matches your typical trading session timing.
- Commission/fee schedule details and any per-order charges that apply to your execution style.
- Swap/financing effects only if your holding times sometimes extend beyond “same-day” boundaries relevant to your use case.
Validation step (quality check): ensure that the spread and fee data come from the same setting (account type, instrument specification) you would use to execute trades.
2) Execution and fill-quality inputs (realized outcomes, not expectations)
To assess execution uncertainty, you need data recorded from the trade or order system:
- Intended price vs. realized fill price (to quantify slippage).
- Fill timestamp sequence (to align results with market states).
- Partial fill frequency and how those partials are handled.
- Order rejection/cancellation rates, because scalping often relies on timely order placement.
Validation step (provenance/timeliness): use records generated by the execution system, and confirm timestamps reflect the same time basis you use for market observations.
3) Assumptions and scenario parameters (what you assume when you calculate)
Any calculation or comparison needs explicit assumptions. Include:
- Holding-time window definition (for example, your “typical scalping duration” measured in seconds/minutes).
- Trading cost model (how you combine spread and commissions, and whether you assume spread is constant within a window or variable).
- Data sampling rule (which times you sample, how you handle missing observations, and whether you exclude abnormal outliers).
Validation step (limitations): document assumptions so others can replicate your setup and see how changes alter the result.
Limitations and risks: at least one failure mode to expect
A material limitation is non-stationarity: relationships seen in historical data often change during different liquidity regimes or news-driven volatility. In practice, that means:
- Spread and slippage can widen suddenly, making historical “typical” values underestimate risk.
- Short holding times can amplify cost effects; a model that ignores execution uncertainty can look stable until fills degrade.
- Platform or broker execution rules can vary by conditions, meaning expected fills may not hold across sessions.
Another failure mode is data-quality bias. If you only measure conditions when trades succeeded, you may ignore the moments when orders were delayed or poorly filled—exactly when scalping risk tends to increase.
Verification and next question: how to make the assessment independently checkable
To verify your assessment, check whether a reader could repeat your logic with new inputs. That requires:
- Provenance: identify where each data element comes from (account statements, execution logs, instrument specifications).