Acrobat Scrap dashboard concept illustrating AI-driven portfolio risk analysis

Protection-First Portfolio Intelligence

Algorithmic Stop-Loss Management for Portfolios That Cannot Afford to Guess

Acrobat Scrap applies continuous AI-driven analysis to your holdings, recalculating risk exposure around the clock and tightening or releasing stop-loss thresholds as conditions change. The objective is straightforward: mitigate drawdown before it compounds, rather than reacting after the fact.

View Methodology Review System Parameters

Drawdown exposure, illustrative model output

A simplified comparison of unmanaged exposure against stop-loss managed exposure during a simulated market decline.

Unmanaged
exposure
Stop-loss
managed
Unmanaged
exposure
Stop-loss
managed

Illustrative model output only. Not actual trading results and not an indication of future performance.

How it works

A stop-loss system that re-evaluates risk continuously, not just at month-end

Most stop-loss orders are fixed: set once, left static, and often triggered too late or too early. Acrobat Scrap's system recalculates thresholds using live market data, so the protection moves with the portfolio rather than sitting still.

The underlying logic

The model treats volatility, liquidity, and correlation between holdings as moving inputs rather than fixed assumptions. When volatility rises, thresholds tighten in proportion. When conditions stabilise, thresholds widen to avoid exiting a position on ordinary market noise. This reduces the two common failure modes of static stop-losses: being stopped out during short-lived dips, or holding on too long through a genuine decline.

Every adjustment is logged and attributable to a specific input change, so you can see why a threshold moved, not just that it did.

  • Re-evaluation frequencyContinuous, intraday
  • Primary inputsPrice action, volatility, liquidity depth
  • Threshold typeDynamic, volatility-adjusted
  • Execution routingAutomated, sub-second
  • Human overrideAvailable at account level
  • Audit trailFull adjustment history

Reading the data

The portfolio view presents threshold movements as a time-series alongside the underlying asset price, rather than a single percentage figure. This lets you trace each adjustment back to the market condition that caused it, which is particularly relevant when explaining decisions to a spouse, adviser, or beneficiary.

Why we built this

A consultative approach to automated risk control

Acrobat Scrap was built on the premise that capital preservation deserves the same analytical rigour usually reserved for growth strategies. We work with data-driven decision models used in institutional risk management and adapt them for individual portfolios, with an emphasis on transparency over complexity.

The platform does not aim to predict market direction. It aims to limit the damage when direction turns against a position, and to do so without requiring constant manual attention.

Acrobat Scrap team reviewing data-driven risk analysis models
Methodology

How the decision-optimisation process works, step by step

We set out the mechanics plainly, without relying on testimonials or unverifiable claims. The process below describes what happens between data arriving and a threshold changing.

  1. 01

    Data ingestion

    Market feeds, volatility indices, and portfolio holdings are pulled in continuously from licensed data providers.

  2. 02

    Risk scoring

    Each holding receives a risk score based on current volatility, liquidity, and correlation with the rest of the portfolio.

  3. 03

    Threshold calibration

    Stop-loss levels are recalculated against the risk score and the account's stated risk tolerance.

  4. 04

    Continuous monitoring

    Positions are monitored against calibrated thresholds without interruption, including outside market-standard hours where relevant.

  5. 05

    Automated execution

    When a threshold is breached, the order is routed automatically. No step in this sequence waits on manual approval.

  6. 06

    Post-trade review

    Every triggered exit is logged with the data conditions that caused it, available for your own review at any time.

Data inputs used in threshold calibration, and how frequently each is refreshed
Data category Source type Update frequency
Market price data Licensed exchange feeds Real-time
Volatility indices Derivatives market data Real-time
Liquidity depth Order book snapshots Intraday
Macroeconomic indicators Public data releases Daily
Portfolio-specific risk tolerance Client-defined settings On change

On model validation: the calibration logic is tested against historical market periods, including sharp downturns and prolonged low-volatility stretches, before being applied live. Backtesting demonstrates how the model would have behaved under past conditions; it is not a guarantee of how it will behave in future conditions, and we do not present it as one. Parameters are reviewed on a periodic schedule rather than left static indefinitely.

Strategic use cases

Practical application across different retirement circumstances

The appropriate threshold sensitivity depends on what the portfolio needs to do. The scenarios below illustrate how the same underlying system adapts to different objectives.

Conservative

Consolidating a workplace pension

A pre-retiree transferring several workplace pensions into a single managed account, with ten or more years until drawdown begins and a preference for limiting large single-year losses.

Expected outcome: wider thresholds during ordinary volatility, tighter thresholds only when correlated risk rises across the portfolio, reducing the frequency of premature exits.

Balanced

Drawing a regular income in retirement

A retiree taking a fixed monthly income from a portfolio that must remain liquid enough to meet withdrawals without forced selling during a downturn.

Expected outcome: thresholds calibrated to protect the capital base funding income withdrawals, with liquidity depth weighted more heavily in the risk score.

Cautious growth

Preserving a lump sum after a property sale

An individual holding a significant one-off sum, seeking measured growth above cash returns without exposing the full amount to equity-market drawdown.

Expected outcome: a smaller allocation to higher-volatility assets, each held under tighter stop-loss bands relative to the rest of the portfolio.

Questions we are asked most

Liquidity, security, and the limits of AI autonomy

Can I still access my money when the system is managing a position?

Yes. Automated stop-loss management does not restrict withdrawals or lock funds. Positions remain part of your account and can be adjusted or liquidated manually at any point, subject to normal market trading hours.

Does the AI ever act without any human oversight at all?

Execution of a triggered stop-loss is automated by design, since the purpose is to remove delay. However, the parameters the model operates within, including your overall risk tolerance, are set by you and can be reviewed or changed at any time. The model does not have authority to alter your stated risk settings on its own.

What happens during a flash crash or a sudden liquidity gap?

Liquidity depth is one of the model's live inputs. In conditions of unusually thin liquidity, the model can widen thresholds temporarily to avoid executing at distorted prices, rather than exiting into a gap. This behaviour is logged and visible in your account history.

How is my account and portfolio data kept secure?

Portfolio and account data are encrypted in transit and at rest, and access is restricted to the systems required to run the risk model. We do not sell portfolio data to third parties.

Will the system guarantee that I never experience a loss?

No system can guarantee against loss, and we would be cautious of any provider that claims otherwise. The stop-loss model is designed to limit the extent of a decline once risk conditions deteriorate; it does not eliminate market risk entirely.

Have a question that isn't covered here? Get in touch with our team.

A low-pressure way to see how the system would treat your portfolio

There is no obligation to transfer funds to review your settings. You can examine the methodology, set your preferred risk tolerance, and see how thresholds would have behaved under past market conditions before deciding whether to proceed.

Review System Parameters
  1. Set your risk tolerance and income or withdrawal needs.
  2. Review how the model would calibrate thresholds for your current holdings.
  3. Adjust settings, or leave them at the suggested defaults.
  4. Activate monitoring, with manual override available at any time.