Allenton's models read market data continuously and surface risk-adjusted signals in plain language, so you don't have to watch charts between lectures.
Hundreds of trading pairs move at once. News, social sentiment and on-chain activity shift by the hour. Reading all of it properly takes time most students don't have between lectures, deadlines and part-time work.
Allenton's models process this data continuously and reduce it to a short, specific signal: what the data shows, how confident the model is, and what a sensible position size might look like.
No trading jargon required. Each signal is built the same way, every time.
Allenton pulls price, volume and on-chain data from multiple exchanges at short intervals, so the models always work from current conditions rather than stale snapshots.
Statistical models compare current patterns against historical outcomes to estimate a risk-adjusted probability for each asset being tracked.
You get a short summary: what the data indicates, the model's confidence level, and a suggested position size based on your risk settings.
Allenton publishes a running record of signals and their outcomes. Nothing is removed after the fact, and the format below reflects how each entry appears in your account.
| Date logged | Asset pair | Signal type | Model confidence | Outcome recorded |
|---|---|---|---|---|
| 04 Mar 2025 | BTC/GBP | Reduce exposure | Moderate | Logged after 7-day window |
| 06 Mar 2025 | ETH/GBP | Hold | High | Logged after 7-day window |
| 11 Mar 2025 | SOL/GBP | Accumulate | Moderate | Logged after 7-day window |
Illustrative example of the log format used across the platform. Live entries and full history are available in your dashboard once you register.
Allenton is designed for people who want a data-driven starting point, not a second job watching charts.
Set a maximum position size and risk tolerance once. Every signal is scaled to those limits automatically, so you're not guessing how much to commit.
The models track your selected assets continuously in the background. You get notified when conditions change, instead of needing to check prices yourself.
Allenton suggests rebalancing when your holdings drift from your target risk profile, with the reasoning shown alongside the suggestion.
It means position sizes and signals are scaled to a risk limit you set, and the model favours capital preservation over chasing short-term gains. It does not mean the risk of loss is removed. Crypto assets remain volatile, and any position can lose value.
No. Signals are written in plain language with a confidence level and a suggested position size. You don't need to read raw charts or understand the underlying statistical models to use the output.
Each signal is timestamped when it's issued and again when the outcome is recorded, typically after a fixed review window. Entries are not edited or removed after the fact, and registered users can review the full history.
Price and volume data from major exchanges, on-chain activity, and aggregated sentiment indicators. The model weighs these against historical patterns to produce a probability estimate for each signal.
Yes. Position sizing is based on percentages and risk settings you define, not fixed amounts, so it works whether you're starting with a small sum or scaling up gradually.
It's logged as part of the public record along with everything else. The rolling accuracy rate reflects both correct and incorrect signals, so the figure represents actual performance over time.
Free analysis access includes signal summaries and the full public performance log. No card details required to look.