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Analyze portfolio risk and validate targets from your local stock and ETF logs.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "asset-management" yet — see the docs or source repo.
Read my local stock and ETF transaction log, compute drawdown-first risk from the records, and summarize the key findings in plain language.
A risk analysis based on the transaction log, with emphasis on drawdown-related results.
Using my historical transaction records, validate whether my current investment targets pass a walk-forward check and explain the reasoning.
A verdict on whether the targets pass validation, along with a brief explanation.
Based on my transaction log, describe the portfolio risk range using bootstrap confidence intervals and explain what those numbers mean in natural language.
Risk findings with confidence intervals, accompanied by human-readable explanations.
Investors or analysts can read their local stock and ETF transaction logs to review drawdown-first risk results. It fits situations where data should stay local and the original records should remain unchanged.
When users want to test whether their targets are defensible, they can run walk-forward validation on historical transaction records. The tool provides a verdict to help assess whether those targets are more realistic.
For users who do not want to interpret raw figures directly, the assistant can narrate the risk analysis in more understandable language. The underlying computation remains deterministic while the model focuses on explanation.
It is a local, read-only MCP server and CLI for your own stock and ETF transaction log. It performs deterministic calculations focused on drawdown-first risk and validates investment targets.
According to the description, it is read-only. That means it is intended to read and analyze your transaction log rather than write back to or modify the original data.
The description says the core computation is deterministic and uses a 'number fence' that structurally prevents the model from producing figures on its own. The assistant handles narration while the underlying computation constrains the numbers.
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