WIN RATE
73%
MCP-native · 25M+ indexed patterns
The archive of what happened next.
Anchor any chart. Get back the 500 historical days that look like it — split into outcome playbooks by what they actually did next. Empirical analogs, not predictions. The grounding layer for AI agents in markets.
25M+
indexed patterns
<300ms
cohort retrieval
50–0
vs ungrounded agents
9
canonical MCP tools
$0
free tier
NVDA · 1h · anchored 2026-05-28live
Uptrend 28%Sharp choppy 10%Sideways 44%Downtrend 18%
Live example · NVDA · as of 2026-05-28
300 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. Hover a mode to read its distribution.
Outcome moden5d medianwin rate
Uptrendclean continuation84+5.2%73%
Sharp choppy uptrendvolatile, net up31+2.8%67%
Smooth sidewaysrange-bound drift132+1.1%62%
Downtrendfailed breakout53-2.1%43%
Forward-return distribution · 5d
Uptrend
IQR (25–75)
+1.8 / +8.9%
A new way to query markets
Pattern search has always been mathematical. We made it shape-based.
Old way · indicator search
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 — the same query for everyone.
- Limited to patterns someone bothered to formalize.
TradingView · ThinkOrSwim · Finviz · Trendspider
Chart Library · pattern search
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 them into 3–4 outcome modes — what they actually did next.
- Each mode reports count, median return, win rate, and the IQR.
25M+ patterns · 19K symbols · 10 years
How a query flows
From anchor to actionable read in one MCP call.
01 / ANCHOR
Anchor
Your agent picks any (symbol, date, timeframe). NVDA · today · 1h — the chart you want to understand.
02 / MATCH
Match
The system finds the 500 historical days that look most like your anchor as raw price + volume shape. No indicators. No formulas.
03 / STRATIFY
Stratify
Cluster the 500 analogs into 3–4 outcome modes by what they did next. Each reports count, median, win rate, and distinguishing features.
How the agent talks to you
We don’t script the agent. 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. Numbers stay in parentheses.
decision_brief returned · summary.* flags
verdict
verdict=coin_flipedge=trivialregime=neutralconviction=low
swing_factors
sector_laggingnarrative_passiveearnings_near
caveat_flags
soft_in_regime_sampleregime_was_derived
sample
quality=okn_in_regime=21n_total=132
CLagent reads the flags and writes in its own voice ↓
“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 analogs, 21 printed under a regime like today’s. Their 5-day median was +0.09% with a 52% hit rate — small enough that the regime isn’t doing much work.
What I’d watch
- Sector lagging hard. XLK 60-day RS is −10.4 — skews tech reads lower until it turns.
- News without conviction. Pulse +0.29 — looks like vol repricing, not a narrative change.
- Earnings in 16 sessions. Pattern reads weaken this close to the print.
POST /api/v1/decision_brief · NVDA · 2026-05-11 · 1h● 1.4s · not financial advice
Every read is grounded in tools your agent can also call directly: search cohort discover analyze context narrative explain portfolio and the new decision_brief orchestrator.
Independent evaluation · n=50 · blind LLM judge
50–0
Two identical Claude Haiku agents. 50 out-of-sample trading scenarios. A blind judge scored both on 6 dimensions of reasoning quality. The agent with Chart Library tools won every single scenario — paired t > 10 on every dimension.
Read the full methodology →For developers
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.
# Install$ pip install chartlibrary-mcp # Register with Hermes Agent$ hermes mcp add chartlibrary \ --command "python -m chartlibrary_mcp" # Also works with Claude Desktop & Cursor✓ 9 tools registered · ready
Free
Generous limits for experimentation
$0
Builder
Real agent workloads · faster rate limits
$29/mo
Scale
Production deployments · priority support
$99/mo
Read the API docs
9 canonical tools, calibration report, methodology
What’s next
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 got smarter; the grounding didn’t catch up. Chart Library is the layer.
Now
MCP-native retrieval
9 canonical tools. 25M+ patterns. Calibrated forward distributions, regime stratification, and Layer 5 memory that compounds with every query.
Soon
Live + multi-timeframe
Real-time intraday updates. One anchor, simultaneous matches across 5min / 15min / 1h / 1d.
Next
Cross-asset
Crypto first — the active agent-trading market. Then futures, then international equities. Same architecture, broader universe.
Chart Library
Build an agent that knows what happened before.
Anchor a chart, retrieve the cohort, and let your agent reason from what the market actually did — not from what it imagined.