FX Radar
Live Markets & Forex Data and News
After months of development, we finally managed to connect our AI directly to the live Forex and Gold markets.
This has probably been the biggest milestone since I started building FX Radar.
The first version relied on traders uploading chart screenshots. It worked, but it always depended on the quality of the image. Different layouts, missing candles, cropped charts, random indicators, poor screenshots. The AI could only work with what it was given.
So we decided to rebuild the entire workflow.
Now the AI receives live market data directly, analyzes multiple timeframes, understands the current market structure, and builds a complete trade plan in seconds.
Each trade plan includes:
• Market bias
• Entry zone
• Stop loss
• TP1 and TP2
• Confirmation trigger
• Invalidation criteria
• Risk to reward context
What I'm trying to build isn't another "AI signal generator".
The goal is to create an AI trading assistant that encourages discipline. Instead of telling traders to blindly buy or sell, it presents a structured plan, explains what needs to happen before entering, and defines when the idea is no longer valid.
One feature I'm particularly happy with is that after the plan is generated, users can continue the conversation with Axel, our AI assistant. They can ask why a setup was chosen, what could invalidate it, how recent market news might affect it, or simply dig deeper into the reasoning behind the plan.
There's still a long way to go, and we're improving the model every week, but seeing the AI transform raw live market data into a structured trade plan has been incredibly rewarding.
I'd genuinely love to hear your thoughts.
Do you think this approach makes more sense than traditional screenshot based AI analysis?
Or, more broadly, would you trust an AI trade planner built from live market data more than one built from uploaded screenshots?
3 Comments
Connecting directly to live market data is a meaningful improvement, but what caught my attention is that you're generating a decision framework instead of a trade recommendation.
That distinction matters because it shifts the product from predicting the market to helping traders make more disciplined decisions under uncertainty.
Thanks, I really appreciate that.
That was actually one of our main goals. We didn't want to build another tool that claims it can predict the market because, in my opinion, nobody can do that consistently.
Instead, we focused on creating something that helps traders follow a structured process. The AI provides the context, the entry conditions, the invalidation level, stop loss, and take profit levels, but the trader still has to wait for confirmation and execute the plan with discipline.
We've found that this approach leads to much more realistic expectations and, hopefully, better trading habits over time.
That's the distinction I found interesting.
The harder problem may not be generating the analysis, but understanding whether users actually change their behavior because of the framework.
The long-term value will likely come from whether it improves decision discipline, not just whether the output looks useful.