Automate your investment decisions with backtested models and real-time data analysis, designed for high-mobility profiles who cannot follow the markets continuously.
Ramure Patrimoire combines predictive models and risk management protocols to produce actionable allocation recommendations without constant supervision. The objective is not to predict the future with certainty, but to structure the decision based on verifiable data.
Every recommendation produced by the platform is traceable back to the data and hypotheses that generated it. No strategy is deployed without prior historical validation.
The models process financial news feeds and trading volumes to detect market signals before they are fully incorporated into prices.
Each strategy faces distinct market cycles, including correction phases, to verify its robustness outside of favorable conditions.
Exposure thresholds are calibrated to limit the magnitude of potential losses without proportionally reducing the potential gain.
| Module | Processing latency | Model accuracy* |
|---|---|---|
| Sentiment analysis | ≈ 180ms | 71.4% |
| Trend detection | ≈ 240ms | 68.9% |
| Risk alert | ≈ 95ms | 74.2% |
*Accuracy measured on internal backtesting sample; future performance may differ.
The platform is designed to limit active supervision time, without removing the user's control over their risk parameters.
Definition of target return and risk tolerance thresholds, used as a framework for each future recommendation.
Significant deviations are flagged by alerts processed by AI, viewable from any time zone.
Validated adjustments are executed and then summarized in an automated weekly performance report.
The metrics below make it possible to assess the robustness of a strategy beyond just the performance displayed.
| Indicator | Value |
|---|---|
| Annualized volatility | 11.8% |
| Sharpe ratio | 1.42 |
| Maximum drawdown | −9.6% |
| Cumulative return | +27.3% |
This data represents simulated historical performance and is not a guarantee of future results.
These answers are for users who want to understand the underlying mechanics before setting up an account.
The models recalculate their weightings at regular intervals by integrating recent variations in implied volatility. If the thresholds defined in the configuration are exceeded, the positions at risk are automatically reduced before any manual intervention.
The engine is based on historical prices, trading volumes and structured financial news feeds. Raw data is cleaned and normalized before being fed into backtesting models.
Exchanges between the user and the computing infrastructure are end-to-end encrypted. Access to linked brokerage accounts is limited to read and execute authorizations, with no transfer of funds possible from the platform itself.
Join investors who use Ramure Patrimoire to manage their capital remotely, with weekly reports and risk thresholds that they define themselves.