Predictive analysis for independent investors
BTC-Amélion n9.4 analyzes massive data streams in real time to secure your investments and optimize your strategic returns thanks to rigorous backtesting models.
Simulation of capital growth on historical data, gray area = observed volatility amplitude.
Each component of the system performs a distinct function in the analysis chain, from data collection to final recommendation.
Instant processing of market signals and macroeconomic indicators. Price, volume and economic data feeds are ingested continuously, without manual intervention.
Proprietary algorithms designed to identify correlations that are difficult to detect with the naked eye. Parameters are recalibrated at regular intervals based on new market data.
Automatically adjusts recommendations to limit capital exposure during periods of high volatility, with no action required on your part.
The rigor of the process determines the reliability of the recommendations. Here are the three steps applied before any signal is broadcast.
Market data, trading volumes and economic indicators are collected and then normalized before any analysis, in order to eliminate outliers.
Each strategy is simulated over past market cycles, including phases of high volatility, in order to assess its robustness before any application in real conditions.
Each signal generated is accompanied by a confidence score and an indicative time horizon, to facilitate an informed decision.
Technical metrics measured continuously, communicated without favorable rounding or staging.
Analysis latency between receiving a market signal and generating a recommendation.
Historical directional accuracy measured on the ten-year backtesting sample.
System availability measured over the last twelve months of continuous operation.
Past performance, including that from backtesting, does not guarantee future results. These indicators describe the historical and technical behavior of the system, not a promise of performance.
Two common uses among self-employed workers who wish to structure an investment activity in parallel with their main activity.
Use case 01
The AI adjusts the weighting of assets held based on measured correlation and volatility, with the aim of reducing the maximum portfolio drawdown without increasing manual operations.
This approach is suitable for a profile who consults their portfolio occasionally, outside of working hours, rather than continuously.
Use case 02
The engine identifies temporary market inefficiencies based on machine learning models, and generates a signal accompanied by a confidence score rather than an automatic instruction.
The final decision remains in the hands of the user, who evaluates each signal according to their own risk tolerance.
Direct answers to the questions most often asked by users without training in quantitative finance.
No. The interface translates model outputs into readable recommendations, with a confidence score and an indicative horizon. Technical concepts like drawdown or volatility are explained directly in the interface, without unnecessary jargon.
Each strategy is simulated over a ten-year period of historical market data, including phases of high volatility and correction. This simulation makes it possible to estimate the robustness of a strategy before its application on real-time data.
Version n9.4 integrates dynamic risk management which automatically adjusts the recommended exposure level according to the measured volatility. Standard tools are generally limited to a static signal, without continuous recalibration of parameters.
Access the BTC-Amélion n9.4 interface and start managing your strategy based on calculated, scored and backtested signals, rather than decisions taken in a hurry.