Artificial intelligence at the service of your financial decisions
Analyze more than 500 trading pairs in real time with Sweet Scrap. A decision-support platform designed to turn market complexity into structured opportunities.
Explore the methodologyReal-time predictive analysis
An overview of the volatility indicators and confidence scores generated by our proprietary models.
| Asset | Confidence score | AI trend | Calculated risk |
|---|---|---|---|
| BTC / USDT | 78,4 | +2,1 % | Moderate |
| ETH / USDT | 71,2 | +1,4 % | Moderate |
| SOL / USDT | 54,9 | +0,2 % | High |
| XRP / USDT | 62,7 | -0,8 % | Moderate |
| ADA / USDT | 48,3 | -1,6 % | High |
Values shown for illustrative purposes to describe the analysis engine's output format. Actual scores vary according to market conditions at the time of calculation.
How the model structures the analysis
Three sequential steps turn a volume of raw data into an actionable recommendation, with no manual intervention in the calculations.
Large-scale aggregation
Data collection across 500+ trading pairs simultaneously, including price, volume and liquidity feeds.
Algorithmic filtering
Removal of market noise and detection of weak signals from the collected time series.
Decision support
Generation of personalized recommendations based on your risk profile and portfolio constraints.
Risk management at the core of the algorithm
Unlike speculative approaches, Sweet Scrap uses advanced correlation models to assess your portfolio's exposure to systemic volatility before making any recommendation.
- Cross-correlation analysis between assets and asset classes
- Early detection of trend reversals
- Stress-scenario simulation based on historical data
A structured approach designed for the prudent investor
Sweet Scrap builds predictive models intended to reduce uncertainty rather than promise returns. The goal is to provide a clear reading of the tracked trading pairs, with confidence scores recalculated continuously.
The platform operates from Mauritius and is aimed at investors who want to add a layer of quantitative analysis to their decision-making process without replacing their own judgment.
Questions about the methodology
Precise answers on how the analysis engine works, rather than promises of performance.
How does the AI handle unexpected market movements?
Our models self-adjust by incorporating liquidity data within milliseconds to recalculate risk exposure.
Which data feeds are analyzed?
We process order books, historical volumes and global macroeconomic indicators.