Artificial intelligence at the service of your strategic decisions.
Starkelin transforms large volumes of market data into readable recommendations, intended for professionals who want to diversify their income without spending their evenings on it.
Starkelin transforms large volumes of market data into readable recommendations, intended for professionals who want to diversify their income without spending their evenings on it.
Between market flows, macroeconomic publications and contradictory analyses, infobesity makes arbitrage difficult for a professional who only has a few hours per week to follow his investments. The risk is not only to decide poorly, but to decide too late: this is what we call informational latency.
Each functionality responds to a concrete constraint identified among diversification investors.
Our models rely on historical and real-time data series — prices, volumes, macroeconomic indicators — to estimate likely market scenarios. They are subject to regular backtesting, that is to say tested over past periods to check their consistency before being applied to current situations. The goal is not to predict the future with certainty, but to reduce uncertainty at the time of decision.
A daily report summarizes relevant movements detected on the assets tracked. You know what changed, why the model reported it, and what action, if necessary, is suggested. This regularity allows you to maintain a clear vision without having to monitor the markets continuously.
Beyond opportunity detection, the system assesses the correlation between assets in a portfolio and alerts when the risk concentration exceeds a defined threshold. This approach aims to limit exposure to a single adverse scenario, rather than maximizing an isolated return.
Each recommendation results from a sequence of verifiable steps, with no algorithmic gray areas.
Market, economic and textual data (news, regulatory publications) are collected continuously from structured and unstructured sources, forming the raw basis of the processing.
Rigorous filtering removes statistical noise and duplicates, then normalizes the data to enable consistent comparison across assets and periods.
The models produce a signal accompanied by its confidence level and its synthetic reasoning, rendered in a format readable for a non-specialist.
Three common situations among professionals who are starting to structure their asset allocation.
Identify, before they become consensual, the sectors displaying growth dynamics supported by data on flows and volumes exchanged.
Adjust the allocation between asset classes when predictive signals indicate a change in the correlation between two existing positions.
Using natural language processing (NLP) applied to financial publications, assess whether the dominant tone around an asset is deteriorating or improving.
Rather than displaying aggregated results that are difficult to verify, Starkelin keeps a detailed logbook of each recommendation made.
Each signal sent is dated, motivated and followed until its closure. You can thus compare the initial recommendation with what actually happened on the market, without reformulation a posteriori. This rigor is at the heart of what we call decision-making serenity: deciding with verifiable elements rather than with the promise of a result.
The platform is designed to remain accessible to professionals who are beginning their diversification process, without a minimum commitment.
The data transmitted is encrypted and hosted on GDPR compliant infrastructures. Starkelin does not resell any personal data and limits access to information strictly necessary for the operation of the service.
The predictive models are retrained on a regular cycle to integrate new market data, with a consistency check at each iteration before going into production.
Access to Starkelin is by subscription, with no minimum commitment. Details of the plans and associated functionalities are presented when the account is created.
No. Starkelin produces signals and analyzes intended to inform your decision; the execution of orders remains entirely under your control.
Starkelin was designed around a simple principle: an investment decision is better when it is based on verifiable data and explicit reasoning, rather than on a promise of performance. Our team combines expertise in data science and knowledge of financial markets to build readable tools, including for people who do not come from finance.
