Feri- Radar v6 Market Regime AI

• The description states that Feri Radar is a local, read-only Streamlit market-monitoring dashboard.
• It monitors 19 public Yahoo Finance macro and commodity markets.
• It provides:
  • A bounded 0–100 Feri Score
  • BUY, WAIT, or SELL guidance
  • Trend, RSI, market regime, risk mode, and confidence
  • Plain-language signal explanations
  • Deterministic signal IDs
  • Exact 24-hour signal evaluation
  • A Learning Dashboard
  • Paper trading using virtual capital only
• The application is built with:
  • Python, Streamlit, Yahoo Finance data, pandas, NumPy, and Plotly
• It uses Codex with GPT-5.6 Sol for auditing and validation.

Inference The tool appears to be a prototype or proof-of-concept dashboard for analyzing market signals using historical data and simulated trading logic.

• The description states that Feri Radar is a read-only AI market radar.
• It explains signals and tests them safely, with no live trading or real-money exposure.
• It is positioned as an analytical tool, not financial advice.
• The project was submitted to the OpenAI 2026 hackathon, suggesting it may be a hackathon submission or prototype.

Inference The positioning is that of a research or educational tool for market signal analysis and simulation. There is no indication of commercial intent, product-market fit, or monetization strategy in the description.

• The description does not state who the target customer is.
• It is described as a dashboard for monitoring 19 public Yahoo Finance markets.
• It includes a Learning Dashboard, suggesting it may be aimed at users interested in learning or simulating trading strategies.
• No specific user persona, industry, or use case is defined.

Inference The ICP is not clearly defined. The tool could be for traders, analysts, or students interested in market regime analysis and simulation, but this is inferred from the product features.

• The description states that Feri Radar uses Yahoo Finance data in read-only mode, with no live orders or real-money trades.
• It includes paper trading using virtual capital only.
• There is no mention of pricing, monetization, or revenue streams.
• No evidence of a commercial business model beyond the author’s own local testing.

Inference There is no evidence of a business model or pricing structure. The tool appears to be non-commercial in nature.

• Built with:
  • Python, Streamlit, Yahoo Finance data, pandas, NumPy, and Plotly
• Uses Codex with GPT-5.6 Sol for auditing and validation.
• Includes:
  • CSV logging
  • Duplicate protection
  • Worker health checks
  • Transparent paper-trading results
• The application is local-only, with no live trading or API integrations.
• The author states that no OpenAI API call or live trading logic is required at runtime.

Inference The tool is a self-contained, local prototype built for demonstration and testing. It does not appear to be production-ready or scalable beyond the author’s own use case.

• The project was submitted to the OpenAI 2026 hackathon.
• Local results include:
  • 19/19 markets loaded
  • 0 Streamlit execution exceptions
  • 0 duplicate signal IDs
  • 26/26 automated tests passed
  • 454 virtual BUY/SELL trades evaluated
  • Virtual capital changed from €2,000.00 to €1,966.10 (a loss of -€33.90)
• The author states that the results are shown honestly as a small virtual loss, not as evidence of profitability.

Inference The project is early-stage, likely a prototype or hackathon submission. There is no evidence of customer adoption, revenue, or commercial traction beyond the author’s own testing.

• The description does not mention any competitors.
• It is built to monitor 19 public Yahoo Finance macro and commodity markets.
• It provides signal explanations, risk mode, and confidence metrics.
• No evidence of a competitive analysis or positioning against existing tools in the market.

Inference There is no evidence of a competitive landscape. The tool may be positioned as a novel or niche solution for signal analysis, but this is not substantiated.

• The tool is read-only and local, with no live trading or real-money exposure.
• It uses Yahoo Finance data only — no API integrations or third-party data sources.
• No evidence of commercial viability, revenue, or customer adoption.
• The project is a hackathon submission, suggesting it may not be intended for production use.
• No mention of scalability, security, or long-term maintenance.

Inference The tool is not commercially viable or scalable as described. It appears to be a proof-of-concept or prototype, not a product ready for market.

1. What is the intended commercial use case for Feri Radar?
2. Are there plans to monetize the tool or expand beyond the current prototype?
3. How does the tool plan to scale beyond local execution and 19 markets?
4. Is there any interest from users or partners in adopting this tool?
5. What are the long-term plans for data sources, signal accuracy, or model updates?