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You can automate multi-asset investment analysis by using a profile-driven workflow that detects asset classes, fetches price data, and dispatches six specialized analysts in parallel to synthesize a final cross-asset report.

Yes, cross-asset investment analysis supports stocks, cryptocurrencies, and private companies. It handles multi-listing detection for stocks and applies currency normalization to consolidate venue recommendations.

Generating multiple investment theses is best achieved by dispatching six renowned analysts in parallel to produce independent research, which is then synthesized into a structured final recommendation with explicit summaries.

Profile-driven asset analysis handles data gathering by using MECE-based WebSearch for macro and fundamental data, while enforcing strict output formats and currency normalization to ensure accurate cross-market comparisons.

Yes, fetching financial data for cross-market analysis requires Python libraries like akshare and yfinance to retrieve price history, and mplfinance to generate charts for the comprehensive asset profile.

A limitation of parallel multi-agent workflows for asset profiling is that final synthesized reports are generated strictly in Chinese, which may require additional translation for non-Chinese speaking users.