Luvor Fyrvo combines predictive models with publicly available logs so you can follow and verify the results before basing your own decisions on them.
Instead of asking you to take our word for good, we publish methodology and outcomes in a common log format. Other users can see when a model has hit and when it has failed.
| Model ID | Time stamp | Status |
|---|---|---|
| PM-014 | 2024-11-02 09:14 | Verified |
| PM-022 | 2024-11-02 11:02 | Verified |
| PM-014 | 2024-11-03 08:47 | Verified |
Excerpt of the log format. Each post associates a model version with a timestamp and a verification status, set by independent reviews from the community.
When a model makes an assessment, the input, time and result are recorded in the log. Other users in the community can compare the recorded rating with the actual outcome and mark the record as verified or rejected.
The purpose is not to promise certain returns, but to make the history of the models transparent, so that you can assess for yourself how much you want to lean on them.
The platform is built to support decisions, not to make them for you. Each tool provides concrete input that you can add together with your own knowledge of the market.
The models analyze historical and current data patterns to point to likely developments. They are updated continuously as new data comes in, and their previous ratings can always be seen in the public log.
Each recommendation is followed by a risk flag based on volatility and data quality, so you can quickly see whether an insight is based on a solid or thin data base.
Data is collected and processed continuously, so recommendations reflect the latest information available, rather than a snapshot that is days old.
We avoid technical promises we cannot deliver on. Here are the four steps your data goes through, from collection to recommendation.
Relevant market data and historical patterns are collected and structured so that they can be included in the models' calculations.
The predictive models process data and form an assessment with associated risk level.
The result is recorded in the public log, where it can be followed and checked by other users over time.
You receive a clear recommendation with justification and risk marking, which you can choose to act on or ignore.
Your own account information and transaction history are treated separately from the public log and are never shared with third parties without your consent. Only anonymized model results appear in the log files.
The platform can be accessed via an API, so you can pull recommendations and log data directly into your own follow-up tools, without having to log in manually every day.
Luvor Fyrvo was developed with gig economy participants and independent investors in mind — people whose income varies from month to month and therefore need decision support they can trust over time, rather than one-off guesswork.
We emphasize that the methods behind the recommendations can be explained and verified. This is the approach that underlies both the predictive models and the public log.
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Usage patterns vary, but most use the platform as a fixed point of reference in an otherwise irregular working week.
An independent investor checks the daily risk flag and the updated recommendations before the market opens and uses them as one of several inputs in the decision.
A freelancer with varying workloads uses the platform's logs to track how previous recommendations have fared before devoting time to their own analysis.
An analyst compares his own calculations with the platform's public log to see if there are systematic deviations that should be investigated further.
The questions we most often get are about where the data comes from and how the verification actually takes place.
Data is gathered from publicly available market sources and historical datasets. We do not compile new raw data ourselves, but structure and analyze existing sources so that they can be included in the models.
This means that a model assessment has been compared with the actual outcome of other users in the community and marked as confirmed or rejected in the public log. It is not a guarantee of future results, but a record of what has historically happened.
A sample of the log is publicly available so you can get an idea of the method before creating an account. Full access to all historical records and ongoing updates requires login.
No. The recommendations are decision support based on data and historical patterns, not a promise of a specific outcome. The risk marking helps to show how safe or unsafe the data base is.
Your account information and any transaction history is kept separate from the public log and is not used to identify you in the logs. See the Methodology section for more details.
Have a question not answered here? See the full FAQ.
Review the public log, read the method description, and judge for yourself whether the predictive models make sense as part of your decisions. There is no obligation to look first.