Ample Gagévoire combines predictive modeling and automated risk management to adjust your allocations based on your profile, regardless of time zone or connection quality.
The model does not seek to follow a generic market average. It starts from a risk profile defined by you, then adjusts its parameters with each analysis cycle based on observed data and your past decisions.
Each account has its own calibration trajectory. Two profiles that are identical at the start can diverge if their reactions to market variations differ.
The engine aggregates public and market data feeds and then converts them into actionable indicators for your profile. It does not produce absolute predictions: it weights scenarios according to their probability and their compatibility with your risk thresholds.
Stocks, currencies, bonds and commodities, continuously updated from multiple data sources.
Analysis of time series and macroeconomic indicators to identify significant differences.
Estimation of the impact of market movements on the current allocation, before any decision.
Time-stamped summaries, viewable from a mobile or laptop, without dependence on a landline.
Automated risk management does not depend on continuous presence in front of a screen. Rebalancing decisions follow predefined rules and are executed even if you are offline at the time of the signal.
Consult the decision report at the end of the local day. The system has already adjusted the exposure during the night, within the set limits, without requiring immediate validation.
Rebalancing instructions are queued locally and synchronized upon connection return, without loss of parameters.
The models are based on known statistical methods — variance constrained optimization, volatility cluster analysis — applied to your profile parameters. No model guarantees a result; the limits of each approach are documented in the customer area.
The recommendations produced reflect a weighting of probable scenarios, not market certainty. The system explicitly flags periods when model confidence is reduced.
| Parameter | Function | Calibration range |
|---|---|---|
| Maximum loss tolerance | Defines the threshold for automatically removing exposure in the event of a drop. | 2% to 25% per cycle |
| Investment horizon | Adjusts the model's sensitivity to short-term fluctuations. | 6 months to 10 years |
| Rebalancing frequency | Determines the minimum interval between two automatic adjustments. | 1 hour to 30 days |
| Volatility alert threshold | Triggers a notification when observed volatility exceeds the benchmark. | 1% to 8% (daily standard deviation) |
Data pipelines are designed to ingest heterogeneous feeds — market prices, macroeconomic indicators, foreign exchange data — without relying on a single source. Each model update goes through a validation phase before deployment to active accounts.
The objective is not to maximize the complexity of the model, but its ability to remain consistent with the defined risk profile, including when market conditions change rapidly.
Connections between your device and the platform are encrypted. Account data is hosted within the European Union and access is protected by two-factor authentication.
The analysis takes into account the main market currencies. The account reference currency is defined when configuring the profile and can be modified afterwards.
The defined risk settings remain active on the server side. Pending instructions are retained and applied, or synchronized, as soon as the connection is reestablished.
No. The model weights likely scenarios according to your risk profile, but no statistical method can guarantee a market outcome.
Yes. The profile can be revised at any time; the model recalibrates its parameters in the next cycle.
Initial setup takes a few minutes. It determines the starting parameters; continuous learning then refines the model based on your decisions and market data.
Encrypted connections, data hosted in the European Union, access protected by two-factor authentication. No account data is shared for commercial purposes.