io.github.arhancanli/canli-validation-mcp
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Publisher-provided title
Canli Validation
Publisher-provided description
Deflated Sharpe and PBO checks. A verdict is not admission to anything and is not a forecast.
Summary by lookupd
Runs statistical validation on trading strategy backtests and price series, including overfitting, data-snooping, leakage and stress tests.
Install
Claude Code, remote (streamable-http)
claude mcp add --transport http canli-validation https://canlicapital.com/mcp
Claude Code, npm package (stdio)
claude mcp add canli-validation -- npx -y canli-validation-mcp
Codex, remote (streamable-http)
codex mcp add canli-validation --url https://canlicapital.com/mcp
Codex, npm package (stdio)
codex mcp add canli-validation -- npx -y canli-validation-mcp
Cursor, remote (streamable-http) → .cursor/mcp.json
{
"mcpServers": {
"canli-validation": {
"url": "https://canlicapital.com/mcp"
}
}
}Cursor, npm package (stdio) → .cursor/mcp.json
{
"mcpServers": {
"canli-validation": {
"command": "npx",
"args": [
"-y",
"canli-validation-mcp"
]
}
}
}Other clients: docs. Or tell your agent: Set up lookupd for me: read https://lookupd.com/agent.md and follow it.
Tools (18 tools)
Publisher-provided text
validate_deflated_sharpeDeflated Sharpe ratio: the probability (0 to 1) that the selected strategy's Sharpe beats the best that luck gives across the variants tried, with the probabilivalidate_overfittingProbability of backtest overfitting (0 to 1) by CSCV: how often the in-sample best variant falls below the out-of-sample median. Needs every variant's returns (validate_reality_checkData-snooping tests on every variant a search tried: Hansen's SPA p-value that the best beat the benchmark only by luck, White's Reality Check, and the variantsvalidate_paper_evidenceChecks paper-evidence.v0 structure and disclosures. With CANLI_LOCAL=1, record_file reads an export bundle and journal_file verifies signatures, source hashes avalidate_breadthBook Sharpe ceiling from adding sleeves of this quality and correlation, the Sharpe at a sleeve count, and the sleeves a target needs. For portfolio constructiovalidate_track_recordMinimum track record length (observations and years) for an observed Sharpe to beat a benchmark at a confidence level; with observations, the record's probabilivalidate_backtest_lengthMinimum backtest length (years) before the best of N independent trials is not expected to reach a target Sharpe by luck; with backtest_years, the most trials tvalidate_haircut_sharpeHaircut Sharpe for multiple testing (Harvey and Liu 2015): the Sharpe a single test would have needed, by Bonferroni and independent tests, and with the other tvalidate_luck_trialsHow many skill-less strategies a search would need for its best to reach this Sharpe by luck (Monte Carlo), and with a trial count, the chance it did. States luaudit_backtestOne-call audit of a strategy's returns. Headline: one test whose false-positive rate was measured on nine return shapes (White's Reality Check with variants, luverify_receiptVerify a receipt offline: its Ed25519 signature against the bundled canlicapital.com key, its output hash and its id. Send an id to fetch it first, or the receiservice_statusWhether the validation API is up, with its quotas; check after a timeout before resubmitting. No key. This verdict is about the series exactly as submitted. Thebacktest_strategyBacktest a rule on your prices over a whole parameter grid, then validate the best with the count of variants actually run: deflated Sharpe, overfitting probabisummarize_seriesA long price or return series in about 100 words plus fields: growth, risk, dated drawdowns, trend, volatility regime, tails, jumps, stale data. Use instead ofstress_testStress a strategy's returns: bootstrap histories (how often the drawdown limit breaks or Sharpe turns negative) and named crash, volatility, repeat and stuck-pocheck_feasibilityCheck a trading plan before it trades: broker order-rate and minimum-order limits, 2026 US day-trading rules, square-root market impact with crowding, and the ccheck_leakageDoes a signal look ahead? plan gives cuts; rerun your code on the first cut rows for each, then compare. Rows that changed used later rows; the result names theplacebo_testDoes your pipeline find edges in noise? plan writes placebo files (your data's returns in random orders, so nothing is predictable); run the whole pipeline on r
Details
- Version: 0.16.0
- Transports: streamable-http, stdio
- Uptime (7 days): 100%
- Median latency: 685 ms
- First indexed:
- Last seen in registry:
- Repository: https://github.com/arhancanli/canlicapital
- Website: https://canlicapital.com/developers#ai-assistant
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