This post serves as an announcement of an important milestone.
For quite some time, we have been frequently asked whether there are plugins or integrations available for platforms such as TradingView and MetaTrader 5. However, to be completely transparent — our priority has never been to launch something quickly for the sake of monetization.
Our primary goal has always been something we would want for ourselves: confidence — as much stability and clarity as possible when working with our AI.
At this stage, we can say that we have achieved a certain (and we prefer to remain conservative here) level of confidence.
With that in mind, we are ready to share what we have been developing over the past few weeks, carefully observing ORA’s trading behavior.
As we have mentioned before — and will emphasize again — the most valuable insights often come from complex trades (those that fall outside the main stream of successful outcomes). These trades provide critical information about what deviates from expectations and why certain outcomes become statistical outliers.
And now, we are pleased to share the following update.
We have started developing an indicator — effectively a commercial version of our AI.
After analyzing a substantial dataset of both successful and complex trades, we are now building an additional analytical module that works alongside the core AI. This module is designed to further enhance the overall probability framework of the trading process.
The indicator will provide extended statistical context for current market conditions, which may either increase or decrease the overall probability before executing a trade.
Now, moving to the most interesting part — and honestly, even at this early stage, it already appears to be a highly promising component that could make the combined system (AI + indicator) something truly distinctive.
We will begin with a simplified demonstration. The full system will present a wide range of structured and interpretable data, but for now, we focus on a core concept:
A numerical value — a single aggregated metric — that carries meaningful information. When applied, it has the potential to significantly improve outcome reliability.
The chart displays two screenshots:
one before applying our new aggregated metric, and one after.
Each bar represents our proprietary metric in its aggregated form.
The higher the bar, the higher the probability of a successful trade outcome.
A key observation: prior to aggregation, the distribution appears chaotic.
However, once the aggregation is applied, the structure becomes significantly more interpretable.
Can you identify which trades performed better and which performed worse?
Yes — exactly as the chart suggests 🙂
We have also highlighted the complex GBPAUD cases that were previously discussed in our public channels and are visible in our verified trading statistics.
What you see here is an initial example and the first results of a comparative analysis, demonstrating how the metric relates to actual outcomes.
There is a clear correlation:
the higher the bar, the higher the probability of a favorable result.
In other words, in addition to already strong baseline probabilities, we introduce additional contextual indicators that further reduce potential risk.
Perhaps the most notable aspect is that this relationship is visibly consistent and stable. For instance, the lower bars in the middle correspond to previously identified complex trades from earlier periods.
Naturally, versions of this solution for platforms such as TradingView and MetaTrader 5 will be offered as paid products. Determining a fair market price — given the current performance characteristics — is, admittedly, not a trivial task.
However, we are confident that working with this indicator in combination with the AI will provide a smooth and highly reliable experience.
