WEETU applies predictive modelling to market and operational data, then publishes the resulting recommendations and outcomes so you can review the reasoning before you rely on it.
Built for professionals and side-hustle investors who want evidence before allocation, not promises.
WEETU was built on a simple premise: recommendations generated by an algorithm should be checked the same way any analyst's work is checked. Every model output is timestamped, explained in plain terms, and tracked against what actually happened afterward.
We work with structured and unstructured data — pricing, filings, operational metrics, and macro indicators — to surface patterns that are difficult to track manually across a portfolio or a growing business.
The platform is built around three connected functions: it reads data continuously, scores decisions by risk and expected return, and adjusts recommendations as new information arrives.
Market feeds, financial statements, and operational metrics are processed on an ongoing basis rather than in periodic batches, so recommendations reflect current conditions.
Each recommendation is weighted against volatility, historical drawdown, and concentration limits, not just projected upside, before it is surfaced to you.
As new data arrives, prior recommendations are re-scored, and adjustments are flagged rather than silently rewritten, so you can see what changed and why.
Our USP is community-verified results: model logic and outcomes are visible to users, not sealed inside a black box.
Raw inputs are normalised and checked for gaps or anomalies before they reach any predictive model.
Each output is paired with the variables and confidence range the model used, written in plain language.
The recommendation, its rationale, and the date are recorded publicly before the outcome is known.
Once results are available, they are appended to the same record, so accuracy over time is checkable, not asserted.
Members can review the reasoning behind past calls and compare it against what followed. Nothing is edited retroactively once a recommendation is logged.
The same modelling approach applies across three recurring decision types, each with a different risk and time horizon.
Business leaders use scenario models to compare the projected impact of pricing changes, expansion timing, or resource allocation before committing budget, with assumptions stated alongside each scenario.
Exposure across positions or business units is monitored against volatility and correlation thresholds, with alerts raised when concentration moves outside the limits you set.
Allocation recommendations are rebalanced against changing market data, aiming for a risk-adjusted return profile rather than the highest possible short-term gain.
This is where the side-hustle promise gets tested against evidence: every recommendation we publish is tracked here, whether it worked out or not.
Each entry in the log carries a date, the model's stated rationale, and the eventual result. Nothing is removed once posted, including calls that underperformed expectations.
We do this because passive-income claims are easy to make and hard to verify. A log that only shows the wins is not a track record — it is marketing.
Straightforward answers on reliability, privacy, and how risk is handled.
No model is correct every time. We publish accuracy history in the performance log rather than a single headline number, so you can judge reliability across different market conditions rather than taking a claim at face value.
We collect the information required to generate and personalise recommendations, such as stated risk preferences and portfolio parameters you provide. We do not sell personal data to third parties.
Every recommendation includes a stated risk band and the assumptions behind it. Risk mitigation logic monitors concentration and volatility continuously and flags positions that move outside agreed thresholds.
Yes. Each logged entry includes the rationale in plain language alongside the data points the model weighted most heavily at the time.
It is designed for professionals and side-hustle investors who want a documented, evidence-based approach rather than day-to-day trading signals. We recommend reviewing the performance log before allocating any capital.
Have a question not covered here? Contact our team.
Access the current recommendations, model rationale, and the full public performance log to evaluate whether WEETU's approach fits your long-term allocation strategy.
View Performance LogNo lock-in period. Logs remain visible whether or not you continue as a member.