Stock chart pattern search, built for AI agents.
Anchor any chart. Get back the 500 historical days that look like it, split into 3-4 outcome playbooks by what they actually did next. That’s the edge — empirical analogs, not predictions.
300 NVDA analogs from 10 years, split into 4 outcome paths.
Not a forecast. The empirical split of what historical analogs actually did over the next 5 trading days.
Pattern search has always been mathematical.
We made it shape-based.
Search by formulas someone wrote.
- • Define a “rising wedge” with a math expression.
- • Scan the universe for charts matching the rule.
- • You query in indicator space. Same query for everyone.
- • Limited to patterns someone bothered to formalize.
Search by raw chart shape.
- • Take an actual chart — a real stock on a real date.
- • Get back the 500 historical days that look like it as raw shape.
- • Cluster the 500 into 3-4 outcome modes — what they actually did next.
- • Each mode reports count, median return, win rate, and the IQR. Empirical playbook, not a forecast.
From anchor to actionable read in one MCP call.
Anchor
Your agent picks any (symbol, date, timeframe). NVDA · today · 1h. The chart you want to understand.
Match
The system finds the 500 historical days that look most like your anchor as raw price + volume shape. No indicators. No formulas.
Stratify
Cluster the 500 analogs into 3-4 outcome modes by what they actually did next. Each mode reports its count, median return, win rate, and distinguishing features. The playbook, not a forecast.
We don’t tell the agent what to say. We give it labels.
The MCP tool returns deterministic classification flags — verdict, edge, regime alignment, swing factors. Your agent reads them and writes the answer in its own voice. No scripted prose. Numbers stay in parentheses.
Honestly, this one reads like a coin flip. NVDA’s in-regime analogs printed about the same as everything else — there’s no real edge in either direction here.
Of the 132 closest historical analogs, 21 printed under a regime like today’s. Their 5-day median was +0.09% with a 52% hit rate. The other 111 returned +0.27% on the same hit rate — the gap is small enough that the regime isn’t doing much work here.
- ·Sector lagging hard. XLK 60-day RS is −10.4 — that skews historical reads on tech setups lower until it turns.
- ·News without conviction. Pulse is +0.29 with the tape active — looks like vol repricing, not a real narrative change.
- ·Earnings in 16 sessions. Pattern reads tend to weaken this close to the print — keep that on the radar.
Bottom line: no meaningful edge, low conviction. The in-regime sample is soft (n=21) and the vol regime was derived — treat this as a lean, not a thesis.
Every read is grounded in the underlying tools your agent can also call directly: search, cohort, discover, analyze, context, narrative, explain, portfolio, and the new decision_brief orchestrator that bundles them.
AI agents using Chart Library beat identical agents without it 50–0.
Two identical Claude Haiku agents. 50 out-of-sample trading scenarios. Blind LLM judge scored both on 6 dimensions of reasoning quality. The agent with Chart Library tools won every single scenario. Paired t- statistic greater than 10 on every dimension. Full methodology public.
Drop-in chart pattern intelligence for any AI agent.
MCP-native. Works with Claude Desktop, Cursor, Hermes Agent, or any MCP-capable client. Under a minute to install.
The next ten years of markets × AI runs on grounded retrieval.
Every AI agent that talks about stocks today is making up cohort statistics. The interfaces are getting smarter; the grounding hasn't caught up. Chart Library is the layer.
MCP-native retrieval
9 canonical tools. 25M+ patterns. Calibrated forward distributions, regime stratification, Layer 5 memory that compounds with every query.
Live + multi-timeframe
Real-time intraday updates. One anchor, simultaneous matches across 5min / 15min / 1h / 1d.
Cross-asset
Crypto first (active agent-trading market). Then futures, then international equities. Same architecture, broader universe.