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AI Fin Hub

Tools and research for putting LLMs into markets.

45 tools that run in your browser and 224 dated articles with the method and every source shown: model costs, market data, backtest validation and agent risk.

Worked example. A year of 1-minute US stock bars with five years of history, by vendor
Universe

Medium (~500 symbols)

VendorPlanPer year
Alpaca MarketsFree (IEX feed)$0
TiingoTiingo Power$360
DatabentoDatabento metered (typical retail)$1,500
Financial Modeling PrepUltimate$1,668
Massive (formerly Polygon.io)Stocks Advanced$2,388
Alpha VantageNo plan fits—

What is AI Fin Hub?

A free, independent set of tools and articles for engineers who build with LLMs in trading and finance. The tools work out model and data costs, validate backtests and test agents in a browser tab; the articles carry their formulas and sources, so any number can be checked. It is education, not investment advice, and it has no sponsors or affiliate links.

Start with the decision you need to make

  • Plan your agent stack

    Estimate first-year cost for an LLM agent — token budget, vendor selection, MCP servers.

  • Validate your strategy

    Pressure-test a quant or LLM-augmented strategy before paper-trading or production.

  • Audit your pipeline

    Catch hallucinations, prompt injections, and regression drift before they ship.

  • Size your bets

    Compute Kelly fraction, drawdown bounds, and correlated exposure across a book.

  • Explore market structure

    Inspect order books, options payoffs, and cross-venue arb in a sandbox before going live.

Calculators 16

Inputs to numbers you can act on

Comparators 5

Vendors and models side by side

Playgrounds 19

Test agents, prompts and models

Generators and catalogs 5

Blueprints, configurations and directories

Research

All 224 articles
  • Reading Financial Filings With LLMs: 2026 Playbook

    A map of eight filing tasks — extraction, summarization, peer comparison, Q&A, classification, sentiment, forecasting input, compliance — with model.

    14-minute read, April 23, 2026

  • Model Selection Framework for Finance Tasks

    A task × latency × cost × context decision tree for finance LLM workloads. Ten concrete scenarios mapped to tier bands. Grounded in published pricing, not.

    12-minute read, April 23, 2026

  • State of LLM Pricing for Finance Q3 2026

    Vendor pricing for 21 LLM tiers across six providers — Anthropic Opus 4.8 + Fable 5, OpenAI GPT-5.5, Google Gemini 3.5 Flash, xAI Grok 4.3, DeepSeek V4, Mistral.

    18-minute read, June 21, 2026

Pricing benchmarks are dated and re-checked against vendor pages: all benchmarks.

Vendor and API pricing

Reference

  • Glossary

    Risk, backtesting and market-structure terms, defined with an example each

  • Formulas

    The equations the tools run, with worked examples

  • Worked examples

    Real inputs through the calculators, reproducible

  • Guides

    Task-first how-tos for AI in markets

  • Checklists

    Pre-flight sign-off lists

  • Comparisons

    Side-by-side decision tables

  • Statistics

    Sourced AI-in-finance figures

  • Reference grids

    Lookup tables computed by the tools

  • Alternatives

    Switching options for each data and broker vendor