In brief
Eye of Profit is a public product created by ACG participants to analyze market opportunities through Telegram. It collects funding-rate and futures-spread data, compares instruments across centralized exchanges and helps users notice situations that may require attention.
We deliberately describe this area without promises of returns. For us, the product’s value is not in loud predictions, but in the engineering layer: stable data collection, filtering, a clear interface, observability and a practical user workflow.
The task behind the product
Market data becomes outdated quickly. When a trader or analyst manually checks multiple exchanges, dozens of instruments and several parameters, it is easy to miss a funding-rate change, a spread movement or the timing of the next funding event.
That is why the useful layer is not a raw data stream, but a prepared interface: a place to see pairs, filter noise and move to one’s own risk assessment. Telegram works well here as a practical operating layer because it is already part of many users’ daily workflow and supports timely notifications.
What matters in the engineering
Externally, the service may look like a bot and several channels. Internally, it is an engineering task involving exchange integrations, data normalization, API reliability, update frequency, error handling, filters and careful result presentation.
There is also a product constraint. An analytical tool must not become a promise of profit. The user should understand that the service shows data and potential market situations, while decision-making, risk and execution remain with the human.
Why this fits the ACG approach
Eye of Profit reflects a recurring ACG pattern: taking a complex data flow and turning it into a working interface. The same principle appears in production reporting, AI contours, Telegram automation and operational bots.
The engineering task does not end with connecting APIs. The product must be convenient, maintainable and honest with the user. That is what separates an applied FinTech tool from a loose set of integrations.
What comes next
A reasonable next step for this direction is improving filters, notification scenarios, data quality and semi-automated workflows. The core principle remains the same: less noise, more verifiable data and a clear interface for independent decisions.