Sklondavru combines predictive models and retroactive tests on historical data to identify strategies whose robustness has been verified over several market cycles, with risk management integrated into each recommendation.
Sklondavru's models continuously process market data, macroeconomic indicators and sector signals to establish probabilistic scenarios. Each recommendation is accompanied by a confidence score and an estimate of the associated risk, so that the final decision remains informed and documented.
The objective is not to predict a single result, but to reduce the margin of error by comparing several hypotheses with real data before any capital allocation.
Each strategy proposed by Sklondavru is first built on historical data, then validated over periods distinct from those used for its design. This separation between training sample and control sample limits the risk of overfitting to past data.
Each strategy is evaluated over periods of bulls, bears and high volatility, rather than a single favorable streak.
The regularity of results is favored over one-off maximum performance, in order to limit sudden valuation deviations.
The models are verified on data not used during their design, before being made available.
Metrics are recalibrated as new market data becomes available.
Past performance, including that from retroactive testing, is no guarantee of future performance. The strategies presented are based on the analysis of historical data and constitute neither personalized investment advice nor a guarantee of return.
Sklondavru adapts to different needs: monitoring an investment portfolio or managing operational decisions for a developing activity.
A portfolio manager uses Sklondavru to monitor the risk exposure of their positions and receive alerts when models detect a significant change in correlation between assets. The arbitration recommendations are accompanied by their statistical justification.
A manager uses Sklondavru to cross-reference his internal data (sales, margins, cash flow) with external indicators in order to anticipate cash flow tensions or identify the growth levers least exposed to risk. The analyzes are presented in the form of actionable priorities.
The deployment of Sklondavru follows a structured process, designed to limit technical friction and guarantee the confidentiality of the data transmitted.
Secure connection to your data sources (market accounts, internal flows, sector indicators) via standardized connectors, without manual duplication.
Predictive models process data to produce scenarios, risk scores, and recommendations ranked by relevance.
The observed results are fed back into the models in order to refine future recommendations and adjust alert thresholds.
The models provide probabilistic estimates, accompanied by a confidence score, not certainties. Their reliability is measured by out-of-sample validation, but no analysis method can eliminate the uncertainty inherent in financial markets.
Each strategy is first built on a period of historical data, then verified on a separate period not used when it was designed. This separation limits the risk of overfitting to past data.
Transmitted data is encrypted in transit and at rest, hosted within the European Union, and processed in accordance with the GDPR. Access to data is limited to processing strictly necessary for analysis.
No. Backtesting results describe behavior observed in past data and are not a guarantee of future performance. Each investment decision remains your responsibility.
Results are presented in the form of scores, alerts and ranked recommendations, without requiring data science skills. However, the underlying methodology remains documented for users wishing to examine it in detail.
Sklondavru models are recalibrated every day: the more up-to-date the data transmitted, the more the recommendations reflect current market conditions.
Discover our strategiesPlatform designed for investors and managers attentive to risk management.