Fixed Valuer — an interface for risk analysis and market prediction
Data analysis for investment decisions

A solid foundation for decisions that are often based on emotions today

Pevný Hodnotár processes market and on-chain data using predictive models and assigns risk scores to them. The result is an overview that can be referred to, not an impression of the last discussion on a social network.

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Risk score Volatility, liquidity, concentration
Prediction model Update based on new data
Public log Prediction recorded before result
Firm Valuer — a team analyzing market data and risk models
Problem and approach

Decision-making influenced by hype costs students more than meets the eye

A large number of crypto beginners enter positions based on social media sentiment or short-term price growth. Without a systematic view of risk, it is difficult to distinguish a random fluctuation from a true trend change.

Pevný Hodnotár replaces this type of decision making with data. Predictive models work with historical prices, on-chain metrics and market depth to assign a risk score to each asset being tracked, which is updated as new data becomes available.

Sample risk score

Low risk High risk
How it works under the hood

Three layers on which each recommendation stands

The platform does not combine one magic indicator, but three interconnected layers of data processing that complement each other.

Prediction

Predictive models

The models estimate likely price scenarios based on historical data and market behavior. The output is a range of scenarios with probability, not a single fixed number.

Risk

Risk engine

Each asset is rated according to volatility, liquidity and concentration of holdings. The score is recalculated with each new batch of data, not once a day.

Data

Real time data

The input is exchange API, on-chain metrics and order book depth. The delay between data entry and score update is in the order of minutes.

Verifiability

Results that can be back-checked

Instead of references, we publish the prediction log and their actual result. Each entry is timestamped from when the prediction was made, not post-edited.

Illustrative representation of the comparison of predicted and actual development in individual periods of the monitored log.

  • 1
    The prediction is written before the result.

    The model generates an estimate and it is stored in the log before the actual market development is known.

  • 2
    The log is publicly available for viewing.

    Anyone can compare historical predictions with what actually happened in the market.

  • 3
    The methodology is described, not hidden.

    We describe what data the model uses and how the risk score is calculated to verify the logic, not just the result.

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Start with no strings attached

Three steps from registration to the first overview

Entering the platform is designed so that the first orientation takes minutes, not an afternoon.

01

Data connection

You select tracked assets or connect a stock exchange account in read-only mode. No resource disposition permissions are required.

02

Model analysis

The predictive model processes historical and current data and calculates a risk score for each monitored asset.

03

Specific overview

You will receive a brief report with an estimated scenario and risk level. The decision of what to do with him remains up to you.

Frequently asked questions

What do students ask us most often?

Where does the data the model works with come from?

We combine an exchange API for price and volume data, on-chain metrics directly from the blockchain, and historical price series. None of the sources are internal estimates with no verifiable provenance.

How accurate are the predictions and what if the model gets it wrong?

Predictions are probabilistic estimates, not guarantees of outcome. The model can be wrong, and the log also shows cases where the actual development differed from the estimate. Therefore, we always recommend reading the risk score as one input to decision-making, not as a prompt for immediate action.

Is it affordable for the student?

A demo version with a limited number of monitored assets is available for free. Full access to the models and logo is made available in the form of a subscription, the terms of which can be found after signing up for the demo version.

Experience what data-driven decision making looks like, not gut feeling

The demo version will show you the risk score and prediction range for the selected assets. Access to the public log is open to anyone who wants to check the results for themselves.

Try the demo

No payment card when registering for the demo, no hidden promises of income.