Probabilistic market intelligence for AI agents and financial applications
Use the Stock Trends API to discover ST-IM forward return distributions, identify STIM Select opportunities, evaluate symbols, construct portfolios, and add market regime, breadth, and leadership context to agent-driven workflows.
Built for structured financial decision systems, the API combines weekly Stock Trends market intelligence with pricing discovery, workflow metadata, subscription access, x402 per-request payment, and MPP funded sessions.
Why this API matters
The Stock Trends API is not a raw market data feed. It exposes decision-ready market structure, probability-aware selection intelligence, and workflow endpoints designed for AI agents and financial systems.
Built on Decades of Stock Trends History
Stock Trends is not a shallow market-data wrapper. Its historical classification record extends back to 1980 and includes 16M+ observations encoded through a consistent Stock Trends doctrine.
Agents and developers can use this long-horizon history as research provenance for probabilistic interpretation, regime analysis, sector rotation research, portfolio construction research, and agentic market-intelligence workflows.
- 1980+ historical coverage
- 16M+ structured observations
- Weekly classification cadence
- Consistent signal semantics across decades
- Trend classification, trend persistence, relative performance, relative performance direction, volume activity, market breadth, sector leadership, and regime structure
- Research provenance - not investment advice or guaranteed future performance
Stock Trends outputs are not investment advice, price targets, direct buy/sell commands, or guarantees of future performance.
See the Evidence Behind the Framework
The Evidence & Validation page documents why the Stock Trends framework is worth evaluating: 30+ years of weekly market observations, 156K+ mature realized ST-IM Select outcomes across three forward horizons, Monte Carlo process analysis for Picks of the Week, and portfolio return history endpoints for every live Stock Trends portfolio.
- 156K+ mature ST-IM Select realized outcomes
- Avg returns exceed base-period means at 4w, 13w, and 40w horizons
- Monte Carlo simulation characterizing the Picks of the Week process
- Portfolio return history and strategy provenance endpoints — evaluate portfolios without treating them as a black box
Historical evidence supports framework evaluation and research — not investment advice, guaranteed outcomes, or price targets.
Recommended agent discovery flow
Agents should begin with machine-readable workflow discovery, then inspect context, pricing, symbols, and premium endpoints before execution.
GET /v1/ai/tools— primary agent discovery and workflow guidanceGET /v1/ai/context— indicator definitions and dataset groundingGET /v1/pricing/catalog— live endpoint pricing rulesGET /v1/cost-estimate— workflow cost planningGET /v1/agent/screener/top— recommended first premium endpoint
Forward return intelligence
Access probabilistic return distribution data for individual instruments and use it in screening, decision, and portfolio workflows.
GET /v1/stim/latest
GET /v1/stim/history
STIM Select opportunities
Discover instruments meeting Stock Trends probability and confidence criteria across forward return periods.
GET /v1/selections/latest
GET /v1/selections/published/latest
Portfolio workflows
Construct, evaluate, and compare portfolios using structured decision outputs and Stock Trends market intelligence.
POST /v1/portfolio/construct
POST /v1/portfolio/evaluate
Understand ST-IM
Learn how the Stock Trends Inference Model uses structured market classifications, randomness, large historical populations, normal distributions, and confidence intervals to support probabilistic market intelligence.
Understand what the model attempts to do, its strengths, limitations, portfolio applications, and how agents should interpret ST-IM probabilities.
Interpret probabilities correctly
ST-IM probabilities are not guarantees or deterministic predictions. They are probabilistic estimates derived from large populations of historically similar Stock Trends classifications.
The framework is designed to support better systematic decision-making under uncertainty — not eliminate uncertainty itself.
Designed for portfolio workflows
ST-IM becomes more useful when applied systematically across diversified portfolios, repeated decisions, and broader market-regime workflows.
Agents can combine ST-IM with regime, breadth, leadership, and portfolio-construction endpoints to create adaptive decision systems.