State of AI market data 2026

TL;DR

The 2026 Q2 baseline for AI-in-markets infrastructure at retail and small-team scale:

• Data: Polygon.io (now Massive) Stocks Advanced ($199/mo) and Alpaca Algo Trader Plus ($99/mo) are the two reasonable defaults for real-time SIP equities. Tiingo Power ($30/mo) covers long-horizon EOD research, with fundamentals as a separate add-on. Databento wins tick + L2, on a $199/mo flat Standard plan or metered billing that scales aggressively. (Market-data figures re-checked 2026-10-11.)
• MCP (re-checked 2026-10-18): Alpaca MCP V2 and the Massive (formerly Polygon.io) MCP are still the only grade-A servers. Interactive Brokers, Tradier, Robinhood and TradeStation now run official hosted servers and tastytrade publishes an official self-hosted one (grade B), but none of them documents idempotent order submission; community servers for Databento, IBKR, Tradier and Tiingo grade C to F.
• LLMs on finance tasks (prices verified 2026-10-24): the same small-operator research loop costs from $1.96 a month on Claude Haiku 5.5 to $196 on GPT-6 Astra, about 100× apart, so the model choice moves the bill far more than any other input. Caching half the input trims it further on every model that prices cache reads.
• Retail trader community signal: overfitting is still the #1 failure mode; the newer failure class is "LLM-built bot the operator doesn't understand." MCP security baselines are not published by any vendor; schema drift and missing idempotency on community servers produce real fills today.

Methodology is open and source tools and articles are linked inline. No bundled CSV/JSON artifact ships with this release; reproduce figures by running the linked tools against the documented methodology.

Methodology

All vendor pricing was pulled from the respective vendor pricing pages on 2026-04-20; the market-data table below was re-checked against the same pages on 2026-10-11, which corrected the April Tiingo and FMP rows (Tiingo Power is $30/mo, not $50; FMP's ladder is $29 / $69 / $139). The MCP table was re-checked on 2026-10-18 against the GitHub API and the vendors' own documentation. Enterprise rates are out of scope. MCP server grades follow the 7-point rubric published at /finance-mcp-directory/. LLM cost modeling follows the formula at /token-cost-optimizer/ using published per-1M-token rates from Anthropic, OpenAI, and Google AI.

The dataset for this release covers:

1. Market-data vendors (6 vendors, tier pricing, asset-class coverage, resolution support)
2. Finance MCP servers (11 servers as re-checked on 2026-10-18; scope, auth, idempotency, transport, schema quality, license)
3. LLM models on finance research loops (13 current models across 3 providers, per-call / per-month / per-validated-trade cost, computed from the model catalog)
4. Retail operator adoption signals (aggregated from Reddit r/algotrading + r/ClaudeAI + r/quant public posts)

Market-data vendors (Q2 2026)

Ranked by monthly list price for a medium-universe workload (~500 symbols, 1-min bars), re-checked 2026-10-11.

Sources:

• Alpaca market data plans
• Tiingo pricing
• FMP pricing
• Databento pricing
• Massive (formerly Polygon.io) pricing
• Alpha Vantage premium

The live Market Data API Cost Calculator models tier selection for your exact scenario including options, futures, and history depth.

Finance MCP coverage (re-checked 2026-10-18)

11 servers tracked, rendered from the Finance MCP Directory catalog and graded by its rules. The April table listed a NautilusTrader MCP that does not exist in the NautilusTrader repository and missed Tradier's official server, live since December 2025; both are corrected here, and the official Interactive Brokers, Robinhood and tastytrade servers are added. Repository dates and licences now come from the GitHub API rather than estimates. TradeStation's official connection (January 2026) was added on 2026-10-19.

Key findings.

1. The idempotency gap. Only one of the seven servers that can trade, Alpaca's, documents idempotent order submission. tastytrade gates orders behind a dry-run confirmation token and TradeStation asks for confirmation before any order is sent; Tradier's official and community servers, Robinhood's Trading MCP and ibkrctl document no safeguard at all, so a retry-on-error loop against any of them is one flaky network call away from a duplicate fill.
2. Official no longer means open. Four of the seven official servers (Interactive Brokers, TradeStation, Tradier, Robinhood) are hosted services with no code to audit, and Interactive Brokers, Robinhood and TradeStation do not publish their tool schemas.
3. Read-only is the sweet spot. Massive's read-only server and IBKR's draft-only connector carry the least execution risk and deliver most of the research value. Defer execution MCP until a server documents idempotent order submission for your workload.

LLM models on finance research loops (prices verified 2026-10-24)

Modeled against one small-operator loop: 10 ideas a day × 5 calls an idea × 15% retries × 22 trading days = 1,265 calls a month, each with 8,000 input and 1,500 output tokens; 30% of ideas validate, so 66 validated trades a month. The cached column reads half of each call's input from the prompt cache at the cache-read price and ignores the cost of writing the cache. Prices come from the shared model catalog, as in the token cost optimizer.

Key finding: model choice dominates the bill. GPT-6 Astra costs about 100× as much as Claude Haiku 5.5 for the same loop. These are cost figures only; whether a pricier model earns its cost on your prompts is something to measure on your own task, not something this table shows.

Adoption signals (Q2 2026, qualitative)

Recurring themes from public discussion in r/algotrading, r/quant, r/ClaudeAI, r/OpenAI finance threads, and HN posts tagged with MCP + finance. We do not publish quantified post counts because the underlying samples are not reproducible without sharing each contributor's identifying URL; the items below are observed patterns, not measurements.

1. Overfitting remains the dominant declared failure mode for retail algo strategies. PBO-style cross-validation is rare among posters describing their workflow.
2. "My AI built this and I don't understand it" is the fastest-emerging failure class — operators shipping strategies from LLM suggestions without running cross-validation or paper-trading.
3. MCP onboarding friction. STDIO vs HTTP transport selection and schema quality are the most common pain points.
4. Data vendor confusion. "Databento vs Polygon" is a recurring comparison thread across retail forums with no canonical reference (a gap our Market Data API Cost Calculator fills).
5. Token cost anxiety. Multiple threads describe unexpected four-figure monthly bills from Claude research loops with no per-prompt cost visibility.

Methodology page

The reproducibility recipe and raw data are published at /data-vendor-tco/, /finance-mcp-directory/, and /token-cost-optimizer/. Each vendor row, grade, and dollar figure in this report traces back to those pages.

Downloads

This Q2 flagship was compiled from the live tool datasets linked above, which are themselves authoritative for the data they model. A frozen CSV + JSON snapshot is not published for this release; readers can reproduce the specific figures by running the referenced tools against the methodology documented on each tool's page.

Corrections to this release are logged at /corrections/.

Funding disclosure

No sponsors, affiliate links, or paid placements are present in this release. Current funding state for AI Fin Hub is documented at /sponsor-disclosure/. Editorial independence statement at /editorial-standards/.