AI-Powered Market Intelligence Agent
Budget: $250 – $750 USD
I need a Python-based agent that continuously gathers real-time data on Caribbean economies and sectors, then turns those streams into clear, decision-ready market briefs.
The agent must:
• Pull data asynchronously from at least five live sources—specifically Financial APIs, News services, and Economic databases—while retrying intelligently so data freshness is never compromised.
• Store, structure, and time-stamp each datapoint for reproducibility.
• Run statistical trend analysis (e.g., moving averages, YoY deltas, volatility) and generate regional comparisons across Caribbean countries.
• Pass the processed insights to Claude AI and receive executive-level narratives that highlight key movements, risks, and actionable recommendations.
• Package the findings as Detailed reports (PDF or DOCX) with clear sections for data snapshots, analysis, and next-step guidance.
Deliverables I expect at hand-off:
1. A modular, well-commented Python codebase using asyncio (or equivalent) for concurrent calls, plus requirements.txt.
2. Plug-and-play connectors for each chosen API, with simple YAML/ENV configuration.
3. Unit tests that cover the fetch-analyze-summarize pipeline.
4. A sample output report generated from live data to prove end-to-end functionality.
5. Brief setup notes so I can run or extend the agent on my own server.
Priority is real-time data accuracy, so please outline how you will handle rate limits, retries, and latency. Familiarity with pandas, NumPy, and large-language-model integration will be essential, and experience with Caribbean economic data is a plus.
The agent must:
• Pull data asynchronously from at least five live sources—specifically Financial APIs, News services, and Economic databases—while retrying intelligently so data freshness is never compromised.
• Store, structure, and time-stamp each datapoint for reproducibility.
• Run statistical trend analysis (e.g., moving averages, YoY deltas, volatility) and generate regional comparisons across Caribbean countries.
• Pass the processed insights to Claude AI and receive executive-level narratives that highlight key movements, risks, and actionable recommendations.
• Package the findings as Detailed reports (PDF or DOCX) with clear sections for data snapshots, analysis, and next-step guidance.
Deliverables I expect at hand-off:
1. A modular, well-commented Python codebase using asyncio (or equivalent) for concurrent calls, plus requirements.txt.
2. Plug-and-play connectors for each chosen API, with simple YAML/ENV configuration.
3. Unit tests that cover the fetch-analyze-summarize pipeline.
4. A sample output report generated from live data to prove end-to-end functionality.
5. Brief setup notes so I can run or extend the agent on my own server.
Priority is real-time data accuracy, so please outline how you will handle rate limits, retries, and latency. Familiarity with pandas, NumPy, and large-language-model integration will be essential, and experience with Caribbean economic data is a plus.