Sévélune — artificial intelligence market data analysis interface

Predictive analysis · Crypto-assets

Predictive intelligence for performance

Sévélune aggregates continuous market data and applies stochastic models trained on historical cycles. The proposed strategies are backtested on several years of data, for structured access to crypto markets adapted to a student audience with limited capital.

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6 years Backtesting depth

1.42 Average Sharpe ratio (historical)

−18.3% Maximum drawdown observed

Methodology

How the model filters market noise

The technical base is based on three sequential steps: collection, risk weighting, then signal. Each step is documented so you understand the logic, not just the result.

Data aggregation

Price feeds, trading volumes, order book depth and on-chain indicators are collected across multiple platforms and continuously normalized.

Stochastic modeling

Probabilistic models estimate the distribution of returns in the short and medium term, isolating structural movements from erratic variations.

Controlled risk entry points

The system only signals an opportunity when the expected return/estimated volatility ratio exceeds a threshold set in advance.

Component	Specification	Frequency
Market Sources	Aggregated order books, multi-platform	Continue
Processing latency	Vectorized compute pipeline	≈ 400ms
Backtesting window	Historical data 2018–2024	6 years
Model review	Retraining on new data	Weekly

Sévélune — team of analysts working on data models

Operation

An approach based on data, not intuition

Sévélune makes no absolute predictions. The platform restores probabilities, confidence intervals and risk scenarios, it is up to you to decide according to your horizon and your tolerance.

Each recommendation is accompanied by its performance history and known limitations, so that the decision remains informed rather than delegated.

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Historical returns tested

Backtesting results, without promises about the future

The figures below come from simulations based on past data. Historical performance is no guarantee of future results, particularly in a market as volatile as that of crypto-assets.

Simulated evolution of the typical portfolio (base 100)

Simulated portfolio, rebalanced monthly according to model signals, over the period 2018–2024.

Sharpe ratio 1.42

Maximum drawdown −18.3%

Annualized volatility 24.1%

Winning signal rate 58.6%

Strategy (backtest)	Annualized return	Max drawdown	Sharpe
Passive portfolio (reference)	31.2%	−42.7%	0.71
Model Sévélune — conservative profile	19.8%	−11.4%	1.58
Model Sévélune — balanced profile	27.5%	−18.3%	1.42

The above data comes from retrospective simulations (backtesting) and does not constitute a guarantee of future performance. The crypto-asset market remains exposed to capital losses, including in controlled risk scenarios.

Tools available

A set of instruments to manage risk exposure

Each module answers a specific operational question: when to act, how to distribute capital, and what maximum loss to accept.

Monitoring

Real-time alerts

Notification as soon as a tracked asset crosses a statistical threshold defined by the model, with the confidence level associated with each signal.

Allowance

Portfolio Optimizer

Capital allocation calculated based on the correlation between assets and estimated volatility, for real-time optimization of overall exposure.

Risk management

Risk simulator

Testing adverse market scenarios on your current allocation, with estimation of the maximum likely loss over a given horizon.

Democratized access

From financial theory to supervised practice

Sévélune was designed for an audience who discovers market analysis alongside their studies, with often limited starting capital.

01 Introductory modules to the concepts of volatility, Sharpe ratio and correlation between assets.
02 Guided reading of backtesting results, to understand the statistical limits of a short history.
03 Practical exercises on real data, without capital commitment, before any simulation.
04 Data-driven learning: each recommendation is accompanied by an explanation of the underlying signal.

Platform accessibility

No minimum capital is imposed by Sévélune to consult modules and historical signals. Fees for order execution depend on the exchange platform chosen by the user.

The objective remains educational as well as operational: to understand the mechanics of a decision before applying it with real amounts.

Why choose Sévélune

Frequently asked questions

Scope and limits of artificial intelligence assistance

Direct answers on data sources, latency, and capital protection logic.

What data sources does Sévélune use?

Flows come from order books of centralized exchange platforms and public on-chain indicators. No personal transaction data is required to view the signals.

What is the latency between a signal and its broadcast?

The entire processing, from collection to signal generation, takes place in approximately 400 milliseconds. This delay does not eliminate execution risk in markets with high volatility.

Does the model guarantee capital protection?

No. The system aims to reduce exposure to historically adverse patterns, but cannot negate the risk of loss inherent in cryptoassets. The maximum drawdown observed in backtest remains a retrospective indicator.

Are past performances representative of the future?

The results displayed come from simulations on historical data. Market conditions change, and a model that performs well in one period may underperform in another.

Do you need prior knowledge of finance to use the platform?

No, but the integrated educational modules are designed to fill in the missing concepts before using the risk simulation tools.