finance MCP Server
MCP server for Chart Library — visual chart pattern search engine. Find similar historical stock charts and see what happened next.
Discovered via unknown and last synced 3mo ago.
Install instructions not detected yet
Check the source repository for the latest setup steps.
`cohort(depth="compare", compare_with={...})`
Entry point. Find similar historical patterns for an anchor; returns a `cohort_id` you can chain. `mode=` supports `text` (default), `live_bars` (raw OHLCV), `similar` (cohort-level neighbors).
`context(target={"symbol": ..., "date": ...})`
What's interesting today. `mode="picks"` (cohort-ranked top picks), `mode="daily_setups"` (pre-enriched briefs in one call), `mode="risk_adjusted"` (Sharpe-ranked).
GitHub Copilot
Multi-holding weighted conditional distribution. Runs per-holding cohorts in parallel, weight-averages the distributions, ranks tail contributors.
50,000
5,000
What it does
News intelligence. `mode="pulse"` (single-symbol narrative-change score + FinBERT sentiment) or `mode="alerts"` (market-wide divergence anomalies).
Replacement
**The core primitive.** Conditional distribution analysis. `depth="basic"` returns kNN + outcome distribution; `depth="full"` adds Layer 3 feature importance + regime stratification + risk profile; `depth="compare"` pits two anchors side-by-side. Filters across regime / sector / liquidity / event.
File an error or improvement suggestion back to the project.
Calls/day
Slice/probe a stored `cohort_id` by ANY attribute (macro · technical · event) and get per-subset stats vs the full-cohort baseline. No kNN re-run. *"Of the 300 analogs, how do the post-earnings-week ones do?"*
Partition the cohort by one dimension (`vol_regime`, `sector_etf`, `momentum_5d`, …) → per-bucket outcome distributions vs baseline. The one-call "does this dimension matter?" primitive.
**The core primitive.** Layer 3 cohort intelligence for a `(symbol, date, timeframe)` anchor — calibrated outcome distribution + feature importance (which features separated winners from losers) + regime stratification + risk profile. Filters across regime / sector / liquidity / event.
The full cohort, one record per analog, with rich per-member metadata (forward outcomes, regime, anchor fundamentals, news, chart events). Slice and bucket it yourself.
Layer 5 memory — per-symbol feature reliability + achieved calibration across prior analyses. Ground a read in whether a feature has historically been reliable for this ticker.
Reorder the cohort by a weighted composite of member fields you name (e.g. `"ret_5d:1,distance:-0.5"`) — impose your objective on the analogs, fully auditable.
Situational data. `target=` accepts `"market"`, a ticker symbol (`"NVDA"`), `{"symbol": ..., "date": ...}` for lightweight anchor metadata, or `"system"` for DB coverage.
Narrative + rankings derived from a cohort. `style=` accepts `filter_ranking` (which filter shifts the distribution most), `prose` (plain-English summary), `position_guidance` (exit signals), `risk_ranking`.
200
Analytic metrics. `metric=` accepts `anomaly`, `volume_profile`, `crowding`, `correlation_shift`, `earnings_reaction`, `pattern_degradation`, `regime_accuracy`, `decompose` (slice winners vs losers), `clusters` (cohort-internal grouping).
`portfolio`
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