Majetrix combines live market data with predictive modelling to help students and young professionals evaluate crypto positions with more rigour, before any capital is committed. Every signal is logged publicly, so you can review the model's history rather than take it on trust.
Representative only — not actual performance data. Live figures are available to beta participants.
Majetrix was set up around a simple observation: most retail crypto research is manual, time-consuming, and difficult to verify after the fact. Our platform applies statistical and machine-learning techniques to market data continuously, so that research time is spent interpreting results rather than collecting them.
We are UK-based and designed with students and early-career professionals in mind — people who want to apply data science thinking to their own portfolios without needing a trading desk's worth of tools or capital.
Crypto markets trade continuously, across dozens of venues, with price-relevant information arriving faster than most individual investors can reasonably track. Manually cross-referencing charts, order books, and on-chain activity takes hours and is easy to get wrong under time pressure.
Majetrix is built to absorb that volume of data and translate it into a smaller number of clearly reasoned, risk-scored signals — leaving the final decision with you, informed rather than guessed at.
Exchange order books, on-chain activity, and public sentiment indicators are pulled in around the clock, not refreshed on a manual schedule.
The model looks for recurring volatility and momentum patterns across assets, rather than analysing each coin in isolation.
Every recommendation is paired with a risk flag, reflecting volatility and liquidity conditions at the time the signal was generated.
Each signal is timestamped and retained, so its outcome can be checked later against what actually happened in the market.
The beta dashboard presents model confidence against subsequent price movement over time, broken down by asset and by the holding period the signal assumed. Rather than a single headline number, you can see how the model performed across different market conditions, including periods of high volatility.
Every signal generated by the model is timestamped and retained from the point it is issued, so its later outcome can be reviewed rather than recalled from memory.
Beta members can review recent entries before they are archived, flagging anything that looks inconsistent with the published methodology.
Signals are categorised by exposure level — low, medium, or high — based on the volatility and liquidity conditions present when they were generated.
Performance logs are published as they occur, not retrospectively curated. We do not remove or restate entries once they are recorded, which is part of what the community review process is designed to check.
You don't need a statistics degree to use Majetrix, but it helps to understand roughly what's happening behind each recommendation.
The system continuously pulls order book data, on-chain transaction activity, and aggregated sentiment feeds from multiple sources, normalising them into a common format for analysis.
Statistical and machine-learning models look for correlations, shifts in volatility, and momentum patterns across this data, comparing current conditions against historical analogues.
The output is translated into a ranked, risk-adjusted recommendation with a suggested position size range and holding horizon, along with any relevant caution flags.
The scenarios below are illustrative examples of how the platform's output might be used. They describe a way of applying the data, not a guaranteed outcome.
A student with a modest amount set aside for crypto might use Majetrix's risk flags to avoid entering a position during a high-volatility window, instead waiting for a lower-risk signal before committing a smaller, pre-defined amount.
Illustrative scenario only. Individual outcomes will vary and are not represented by this example.
A young professional with a larger, more established position might use a medium-risk signal as a prompt to reduce exposure ahead of anticipated volatility, rather than to increase it — treating the model as one input among several in a broader strategy.
Illustrative scenario only. This is not investment advice and should not be read as a recommendation.
Our core USP is that performance claims are not self-reported in the abstract — they are recorded as individual, timestamped entries that beta members can inspect directly. The methodology documents how each signal is generated, scored, and archived, so that reviewers are checking the same process we use internally.
View the Full Methodology| Date | Asset | Signal | Risk Flag | Status |
|---|---|---|---|---|
| Day 1 | BTC/GBP | Hold | Low | Closed |
| Day 1 | ETH/GBP | Reduce | Medium | Closed |
| Day 2 | SOL/GBP | Watch | High | Open |
Join the waiting list to get early access to the dashboard, including live performance logs, risk-scored signals, and the full methodology documentation.
Join the BetaCryptoasset investments are not regulated in the same way as mainstream financial products in the UK and can fall as well as rise in value. Logged signals and past model performance are not a reliable indicator of future results. Majetrix provides analytical tools to support your own research and does not provide personal investment advice. Please ensure you understand the risks before making any investment decision.