Sergey Scrap AI data-analysis platform interface displayed across a workstation

AI data analysis for traders who keep every pound they earn

Sergey Scrap applies predictive modelling and real-time pattern recognition to market data, then executes on your behalf. No subscription tiers tied to performance, no commission on trades — the output is yours in full.

Sergey Scrap engineering team reviewing model output on screen

Built for people who want their data working, not their time

Sergey Scrap was engineered to remove two obstacles from independent investing: the cost of platform commission and the time required to monitor markets manually. The system ingests structured and unstructured data continuously, scores opportunities against your risk parameters, and executes within the limits you set — without ongoing manual input.

It is not a signal-sharing forum or a tipster service. It is a decision-support engine with a direct execution layer, priced on a flat basis with no cut taken from your returns.

Every percentage point in fees compounds against your balance

Most trading and analysis platforms charge per trade, per withdrawal, or as a cut of profit. Over a year of regular activity, that commission removes a material share of total return — regardless of how accurate the underlying analysis is.

ModelTypical structure
Traditional platformsCommission per trade, often scaling with volume
Advisory servicesPercentage of assets under management, charged regardless of outcome
Sergey ScrapZero commission on trades and withdrawals

What zero fees changes in practice

Removing commission does not change whether a trade is correct. It changes how much of a correct trade you actually keep, and how often small positions remain worthwhile.

100%
of realised profit retained by the user
0
commission charged per trade or withdrawal
24/7
data ingestion and monitoring
1
flat account structure, no tiered commission

An analysis engine built on four technical commitments

Each component below operates continuously rather than on request, so recommendations reflect current conditions rather than a snapshot taken earlier in the day.

01

Predictive modelling

Historical and live data feed into models trained to identify recurring patterns ahead of price movement, flagging probability rather than certainty so you can size positions accordingly.

02

Real-time processing

Market data is processed as it arrives. Recommendations update continuously instead of refreshing on a fixed interval, reducing the gap between signal and action.

03

Risk mitigation

Exposure limits, volatility thresholds and drawdown rules are applied automatically at the account level, so a single model error cannot exceed your defined tolerance.

04

Scalable infrastructure

The same architecture supports a single account or many concurrent strategies, with processing capacity added as data volume increases rather than as a fixed ceiling.

From raw data to executed decision in four stages

The process is sequential and auditable — each stage produces an output that feeds the next, with your parameters applied at every step.

01

Data integration

Market feeds, historical price data and relevant external indicators are connected and normalised into a single structured dataset.

02

Pattern recognition

Models scan the dataset for statistically significant patterns, including arbitrage detection across correlated instruments.

03

Recommendation engine

Identified opportunities are ranked against your risk settings and presented with confidence scores, not blanket buy or sell instructions.

04

Automated execution

Approved recommendations are executed with latency-free routing, within the position limits you have configured in advance.

A single screen for signal, exposure and outcome

The interface is structured around three questions: what is the model recommending, how much risk does it carry, and what has it already returned.

Confidence scoring, not noise

Every recommendation carries a numeric confidence rating rather than a simple alert, so you can weigh it against your own judgement.

Fee line stays at zero

The account summary shows commission charged on every trade — it reads zero because there is no per-trade deduction to display.

Designed for infrequent checking

Automated execution means the dashboard is a record of activity, not a screen that requires constant attention to function correctly.

Questions on security, data and fee structure

What security protocols protect account and trading data?

Account access is protected with encrypted authentication, and trading instructions are transmitted over encrypted connections to the relevant execution venues. Data at rest is encrypted and access is logged for audit purposes.

How much latency exists between data arrival and a recommendation?

Market data is processed as it is received rather than on a scheduled batch, which keeps the gap between a pattern forming and a recommendation appearing to a minimum. Exact latency varies with data source and network conditions.

If there is no commission, how is the platform funded?

Sergey Scrap does not deduct commission from trades, profits, or withdrawals. The platform is funded through a flat account arrangement that is disclosed clearly before you connect any funds, with no hidden percentage-based charges.

Is API access available for custom integrations?

Programmatic access to account data and execution endpoints is available for users who want to integrate Sergey Scrap with their own tooling. Access is granted per account and governed by standard rate limits.

Start with an account that charges nothing on your trades

Connect your data sources, set your risk limits, and let the recommendation engine run. There is no commission to calculate at the end of the month, because there isn't one.

Create a free account

No trading commission. No withdrawal fees. UK-based support during business hours.