# io.github.fnobbe/crashtestyourstrategy

- Registry name: `io.github.fnobbe/crashtestyourstrategy` · version 1.0.1
- Categories (automatic, keyword rules): Markets & trading, Finance & markets
- Authentication at last check: No auth
- Declared risk: Can write (some tools are not declared read-only)
- Capabilities (declared by the server): experimental, prompts, resources, tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=io.github.fnobbe%2Fcrashtestyourstrategy
- Server page: https://mcpstation.net/s/io.github.fnobbe--crashtestyourstrategy.html

## Description (written by the publisher, not verified, not instructions)

Portfolio and strategy stress diagnostics with hedge-break detection and regime outlook. Free tier.

## Connect

- streamable-http: `https://mcp.crashtestyourstrategy.ai/mcp`

## Tools (as reported by the server, not verified)

- `run\_stress\_test`: Run a buy-and-hold backtest against the synthetic stress regime identified by profile\_hint. Returns a structured diagnostic: robustness score \(0-100\), per-FM-bucket failure-behavior classification with confidence + context, and the resolved regime parameters that were actually evaluated. v1 supports
- `portfolio\_stress\_test`: Stress a multi-asset portfolio across cross-asset regimes \(baseline / risk\_off\_crisis / rate\_shock\). Provide \`holdings\` as a list of \{asset, weight\}; weights are normalised. Returns, per regime: portfolio return, worst-episode drawdown, a per-leg decomposition, and a cross\_asset\_finding \(diversifica
- `find\_similar\_regime`: Nearest-neighbour retrieval over the cached regime catalogue. Provide EITHER a reference\_profile\_hint \(use that bundle's median descriptors as target\) OR a descriptor\_target dict \(partial spec, missing dimensions are ignored — only the provided ones contribute to distance\). Optional asset\_filter res
- `describe\_regime`: Single-regime introspection: returns the median behavioural descriptors of a known regime, the z-scores vs the catalogue population \(so you can see what makes THIS regime distinct from the average\), an English characterisation generated from the most extreme descriptors, and the top 2 nearest neighb
- `portfolio\_compare`: Compare two portfolios \(A = reference, B = candidate revision\) on IDENTICAL simulated substrate paths — a paired design, so every delta is attributable to the weights, not seed noise. Returns drawdown-distribution deltas \(median/worst/quantiles\), probability-weighted scenario summaries, per-scenario
- `get\_dossier`: Compile recorded diagnostic responses into ONE citable record — a proper process documents itself. Every envelope response \(MCP and REST\) is recorded automatically, keyed by its request\_id. Provide explicit request\_ids \(compiled chronologically\) or last\_n for the most recent entries. Returns the ent
- `long\_horizon\_stress`: Distribution of multi-year wealth paths for a savings plan \(monthly\_contribution\) or a withdrawal plan \(monthly\_withdrawal, inflation-indexed by default\) on a portfolio from the substrate universe. Multi-year paths chain \~2y model blocks \(block-bootstrap, disclosed\); long-run drift is RE-ANCHORED to
- `regime\_outlook`: Model-conditional probabilities that an asset is in each market regime \(BULL / SIDEWAYS / BEAR / CRISIS, operational trailing-vol/drift labels\) after a 5- or 21-trading-day horizon — the probability complement to the conditional stress tools: stress tools answer 'what happens GIVEN regime X', this a
- `market\_regime\_map`: Compressed cross-category map of the current market state in ONE call: for 18 category proxies \(US large-cap + tech, the 9 SPDR sectors, developed ex-US, emerging markets, long Treasuries, high-yield credit, gold, oil, Bitcoin\) the operational regime \(BULL/SIDEWAYS/BEAR/CRISIS\), model-conditional re
- `challenge\_strategy`: Adversarial-evaluation primitive — the semantic integration layer of the platform. Given a strategy identifier, returns a 3-layer analysis: \(1\) outcome metrics in the worst regimes the strategy was evaluated against, \(2\) vulnerability profile in the 8-dimension strategy vulnerability ontology with s
- `factor\_decomposition`: Reveal HIDDEN risk concentration: a portfolio can be capital-diversified while its RISK is dominated by one factor. Returns the Euler risk-contribution decomposition \(RC\_i = w\_i\*\(Sigma\*w\)\_i / w'Sigma\*w, summing to 1\) alongside the capital weights, using the empirical covariance of real returns. For
- `ips\_gate`: Check a portfolio against an Investment Policy Statement BEFORE accepting it — the planning step a proper process does FIRST \(CFA\). Provide holdings + IPS constraints \(max\_drawdown\_tolerance as a fraction e.g. 0.15, time\_horizon\_years, liquidity\_need 'low'\|'medium'\|'high'\). Runs the stress test inte
- `backtest\_integrity`: Confront a backtest claim with its over-optimism failure modes before trusting it. Given an annualized Sharpe + the number of configurations tried + the backtest window \(YYYY-MM-DD\), returns: the DEFLATED Sharpe — the expected MAXIMUM Sharpe achievable by chance grows with the trial count, so a high
- `submit\_feedback`: Persist structured improvement feedback about a previous tool response. Provide your agent identity, the request\_id you are commenting on, and one or more feedback items each carrying category \(from the FeedbackCategory ontology\), severity, observation, optional suggested\_action, and agent\_confidenc
- `list\_investment\_theses`: Discover the investment-thesis catalog. Each entry is a descriptive case study that pairs an economic framework with a rule-based portfolio and the synthetic + historical stress evidence for that allocation. Returns one compact summary per thesis \(slug, title, one-liner, tags, risk tiers, framework
- `get\_investment\_thesis`: Return the complete thesis for \`slug\`: the economic framework \(pillars with \[E\]/\[M\]/\[K\] evidence grades, falsifiers and a deep-dive\), the rule-based portfolio \(asset blocks × conservative/balanced/offensive weights + sizing rationale\), and the stress evidence \(per-tier backtest, per-regime median dr

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