Generate investment analysis from live market data
The poster built a personal wealth advisor in Claude Code that feeds live market data, macro indicators and news into investment analysis. Task: Suggested implementation: Use Claude Code CLI to construct a personal wealth advisor workflow. Integrate live market data, macro indicators, and news feeds, then process them using a CFA-style system prompt. Implement a memory system to retain and reference historical analysis from previous sessions. Human role: Review generated insights for accuracy and relevance before acting on them.; tools: Claude Code. Human role: Source evidence is unavailable regarding specific human roles or oversight in the workflow.; limitations: Prerequisites include access to reliable live market data APIs and a persistent memory storage solution. The source reliability is low due to the 403 error and lack of verifiable evidence. Human approval is critical for any financial decisions derived from this workflow. Operating costs may vary based on data API usage and LLM token consumption..
Tools
Published on Agent Work Atlas:
Overview
The poster built a personal wealth advisor in Claude Code that feeds live market data, macro indicators and news into investment analysis.
Workflow
Suggested implementation: Use Claude Code CLI to construct a personal wealth advisor workflow. Integrate live market data, macro indicators, and news feeds, then process them using a CFA-style system prompt. Implement a memory system to retain and reference historical analysis from previous sessions. Human role: Review generated insights for accuracy and relevance before acting on them.
Reported result
Actual outcomes are unknown as the source is inaccessible (HTTP 403) and no verified performance metrics or investment results were provided in the available text. The described goal was to produce analysis deeper than generic stock tips. Proposed acceptance check: Compare the depth and consistency of memory-enhanced outputs against baseline responses without historical context.
Human role
Source evidence is unavailable regarding specific human roles or oversight in the workflow.
Limitations
Prerequisites include access to reliable live market data APIs and a persistent memory storage solution. The source reliability is low due to the 403 error and lack of verifiable evidence. Human approval is critical for any financial decisions derived from this workflow. Operating costs may vary based on data API usage and LLM token consumption.
Try it
Act as a financial data engineer. I need you to build a prototype for a personal wealth advisor using Claude Code. Inputs: 1. [Replaceable: List of 3-5 stock tickers or ETFs] 2. [Replaceable: URL or API endpoint for live market data] 3. [Replaceable: Source for macro economic indicators] 4. [Replaceable: News RSS feed or API] Steps: 1. Write a Python script using Claude Code CLI to fetch the specified market, macro, and news data. 2. Construct a system prompt that instructs Claude to act as a CFA charterholder, analyzing the data for investment insights. 3. Implement a simple JSON-based memory file to store previous analysis summaries. 4. Create a function that loads this memory and includes it in the context for new queries to ensure continuity. 5. Run a dry run with the provided inputs and output the analysis to [Replaceable: Local file path or console]. Constraints: - Do not execute any real trades. - Flag any missing data sources. - Ask clarifying questions if the data format is ambiguous. - Ensure the memory retrieval logic correctly references past entries. Deliverables: - The Python script. - The system prompt template. - A sample output showing how memory influences the current analysis. Success Metric: - The output must demonstrate integration of historical context from the memory file. - Human confirmation required before scaling to real-time monitoring.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.