Orgreave analyses market data with predictive models built for people managing income outside a conventional salary. Every recommendation is logged publicly and checked by the community that relies on it, not just by us.
Orgreave is built to process more data than any one person can review between shifts, then present the result in plain terms.
Problem: markets generate far more data than an individual can track alongside other work.
Orgreave processes historical and live data sets to surface patterns that are easy to miss when you only have an hour between jobs to check the market. The models are retrained on new data continuously, so the signal reflects current conditions rather than last month's trend.
Problem: supplemental income can't absorb large, unexpected losses.
Each recommendation is accompanied by a risk assessment based on volatility and exposure, not just projected upside. The aim is consistency across weeks and months, rather than a single large return that is hard to repeat.
Problem: by the time most people spot an opportunity, the window has often closed.
Recommendations update as new data arrives, with timestamps so you can see exactly how current a signal is. You decide when and whether to act; Orgreave simply narrows the decision down to the moments that matter.
Instead of testimonials, we publish the record. The same data that trained the model is used to score it afterwards, and the community checks our working.
Every recommendation issued by Orgreave is timestamped and stored before the outcome is known. Once the relevant period closes, the predicted outcome is compared against the actual result and the comparison is published alongside the original recommendation, whether it performed well or not.
Members of the community can audit any entry in the log, flag discrepancies, and request clarification on the reasoning behind a given recommendation. We treat this scrutiny as a feature of the platform, not an inconvenience.
Orgreave was put together on the premise that independent earners deserve the same calibre of analysis available to institutional investors, without requiring a background in data science to use it. The platform translates model output into recommendations that state the reasoning, the risk, and the timeframe clearly.
We do not take a position on any single outcome. Our role is to narrow a large data set down to a short, explainable list of options, and leave the final call with you.
Orgreave is designed to act as an assistant that does the data processing, while you retain the final strategic decision.
Link the accounts or markets you already follow. Orgreave reads the relevant data feeds and begins building a baseline picture within minutes.
Define how much risk you're willing to carry and how often you want updates. These settings shape which recommendations reach you.
Review plain-language recommendations with the supporting data attached, then decide whether, when, and how to act.
Three situations where independent earners commonly use the platform to manage supplemental income more deliberately.
For those trading around a primary job, timing matters more than volume. Orgreave flags periods of unusual volatility and suggests whether to reduce exposure, wait, or proceed, based on recent pattern behaviour.
Supplemental income can't easily absorb a bad month. The platform models correlated risks across a portfolio and recommends adjustments to limit the impact of a single underperforming position.
Shifts in demand or sentiment often show up in data before they're widely reported. Orgreave highlights early movement in tracked sectors so you can evaluate it before it becomes common knowledge.
Account connections use read-only access wherever the data source allows it, meaning Orgreave can analyse activity without being able to move funds or place trades on your behalf. Data is encrypted in transit and at rest, and you can disconnect any linked source at any time.
We don't publish a single accuracy figure, because performance varies by market condition and recommendation type. Instead, every recommendation and its outcome is recorded in the public log, so you can review accuracy for the specific categories relevant to you rather than relying on an averaged claim.
No. Each recommendation includes the logic that produced it — the data points considered, the risk factors weighed, and the timeframe used. You can disagree with a recommendation and still understand exactly why it was made.
Access is billed on a recurring basis with no long-term tie-in, and you can cancel from your account settings at any point. Full current pricing is confirmed during sign-up, since it can vary by the data feeds you choose to connect.
Create an account, connect your data, and see your first set of recommendations alongside the public performance log.