AI-assisted asset management
Cygne Fondoire combines a periodic investment program (DCA) with predictive modeling of entry points, to structure exposure to digital assets within a framework of controlled volatility.
Technology
Each allocation decision is based on quantitative, documented and repeatable analysis, rather than on directional market anticipation.
01
Feeds of prices, volumes and sentiment indicators are continuously aggregated to feed decision models without significant delay.
02
Statistical models identify price gaps likely to constitute favorable entry points, before any execution.
03
Scheduled buy orders are adjusted and executed on a set schedule, without manual intervention or emotional decisions.
Technical specification: the infrastructure processes multi-source market data (order books, aggregated volumes, on-chain indicators) at a frequency adapted to the investment window selected by each allocation profile.
Methodology
The system does not attempt to anticipate a market direction. It identifies statistical anomalies in price formation to optimize the execution time of an already planned purchase.
Continuous collection of market data and sentiment indicators relevant to the asset under consideration.
Crossing of volatility, liquidity and short and medium term trends to qualify the state of the market.
Identifying statistically significant deviations from usual price behavior.
Adjustment of the allocation size according to the level of risk measured at the time of the decision.
The DCA schedule is maintained; only the precise time of execution within the planned window is optimized.
The order is executed and recorded, with a searchable history of the parameters that motivated the decision.
The model never decides between “investing” or “not investing”: periodic allocation remains the rule. It only determines the best time, within a given window, to execute each slice.
Who builds Cygne Fondoire
We design and evolve the models that support each allocation decision. Our work focuses on the quality of the signals used, the statistical robustness of the anomalies detected and the consistency of the risk framework applied to each investor profile.
Each update of the model is documented and tested on historical data before being deployed, in order to limit gaps between expected behavior and actual behavior in market conditions.
Risk management
Managing the drawdown and preserving capital constitute the structuring constraints of the model, before any search for performance.
Cygne Fondoire operates in compliance with the obligations applicable to service providers working on digital assets in France. The risk framework is periodically reviewed and documented for each allocation profile, in order to allow ex post control of the decisions taken by the system.
Use cases
The methodology remains the same; only the horizon and frequency of allocation differ depending on the profile chosen.
A family office or corporate treasury wishing to build progressive exposure, without mobilizing a team dedicated to daily monitoring of the markets.
A senior executive or informed investor seeking to integrate digital assets into a broader wealth allocation, with limited monitoring over time.
Frequently asked questions
The answers below cover the sticking points most frequently raised by our interlocutors.
Yes. Automation is about entry decisions, not availability of funds. Withdrawals follow the usual deadlines of the market infrastructures used.
Assets are stored according to security protocols separating operational funds from invested funds, with documentation of the storage arrangements communicated to each investor.
Downside management thresholds are automatically tightened when measured volatility exceeds typical levels, reducing the size of executed tranches rather than interrupting the program.
The model is designed for periodic allocations over several months to several years. It is not suited to short-term speculation.
The pricing structure depends on the allocation profile chosen and is presented in detail during discussions with our team, before any implementation.
