What are the best alternatives to Chart Library?

The best alternatives to Chart Library include Perplexity, Exa. Each offers unique features for different use cases.

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Chart Library

Auditable historical-analog API and MCP server giving trading agents base rates, not forecasts

freemiumDR 2Free tier $0 with keyless hosted MCP access. Pro $15.83/mo billed annually at $190/year (21% saving) or higher monthly. Production plans from $29/mo, with published tiers up to $99/mo and $299/mo for higher volume, workflow tools, and support.View full pricing →

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chartlibrary.io

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About Chart Library

Chart Library is a historical-analog retrieval engine for markets, exposed to both humans and AI agents. Anchor a symbol, date, and timeframe, and it returns comparable historical situations from an index of more than 25 million patterns spanning ten years and 19,000 symbols — then shows what actually happened next in each, split into outcome modes such as clean continuation, choppy uptrend, sideways drift, and failed breakout, with sample counts, median forward returns, and up-rates per mode. The framing is deliberately empirical rather than predictive: it reports the distribution of what analogs did, not a forecast. What makes it credible for agent use is the auditability. The vendor publishes trust receipts, including a held-out coverage figure of 80.8% against a nominal 80% forward-return band across 310,592 audited cases, and a PIT/rank histogram that stays flat across 311,000+ cases with a maximum deviation of 0.5 percentage points — meaning realized outcomes landed where the served distributions said they would along the whole shape, not just at one band. For agent builders, there is a hosted MCP endpoint that needs no account, plus a pip-installable local MCP server, and a canonical loop of search, pull_comps, introspect, and track_record. An /llms.txt file is published for model consumption. The intent is to give a trading agent base rates and auditable evidence instead of another opinion.

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Key Features

✓25M+ indexed patterns across 19,000 symbols and 10 years

✓Outcome-mode split with sample counts, median forward returns, and up-rates

✓Held-out coverage receipt: 80.8% against a nominal 80% band over 310,592 audited cases

✓PIT / rank histogram published, flat to within 0.5pp across 311K+ cases

✓Hosted MCP endpoint requiring no account, plus a local pip-installable MCP server

✓Canonical agent loop: search → pull_comps → introspect → track_record

✓/llms.txt published for model consumption

✓Daily rebuild with 1-hour bars and configurable forward windows

Chart Library Pros & Cons

✅ Pros

+Publishes calibration evidence rather than backtested returns, which is the honest version of this claim
+Free keyless MCP access means an agent can use it before any signup
+Explicitly frames output as empirical distributions, not predictions
+Well suited to agents, which need base rates far more than they need another signal