Built because most market commentary has zero accountability
Newsletters, analysts, pundits. They all show you the wins. The misses disappear quietly. We decided to do the opposite: expose LLMs to public market data and log every hypothesis they form, hits and misses alike.
The problem we are studying
Market commentary online is full of survivorship bias. Hypotheses get made, some work out, those get highlighted. The ones that do not work out get forgotten. There is no accountability, no research history, no way to verify whether the source is actually identifying real patterns.
We built Observed Markets to expose this. Every research entry is recorded the moment it is generated, with the exact entry reference and date. Every observed outcome, hits and misses alike, is tracked publicly. You can study the full research history before drawing any conclusion about its analytical value.
How it works
The core of the project is an AI research agent that runs daily. It collects data from over 10 public sources: Yahoo Finance for prices across 236 subjects, FRED for US macro data, the ECB for European macro data, RSS feeds from major financial news outlets, and more.
Every week, it scans this data looking for analytical patterns across individual stocks, ETFs, crypto, and commodities. When it finds something compelling, it records a research entry with an entry reference and scenario analysis for base, bull, and bear outcomes. Then it tracks that subject daily and logs what actually happens.
The agent uses Claude (Anthropic) for analysis and a second pass from OpenAI for review and fact-checking. Both models have to agree before a research output gets published. Subscribers are buying access to that research output — not investment advice.
A framework, not a stock tip service
The real work here is a framework for transparent, accountable AI-driven research. Public markets are the testing ground because the data is public and the outcomes are measurable — every hypothesis can be scored against reality in days or weeks.
The same methodology generalises to any domain where inputs are observable and outputs can be scored. Markets are the stress test. Transparency is the product.
The operator
This project was built by an individual who got tired of not being able to verify the analytical track of the market commentary he was consuming. The operator may hold positions in the subjects the agent studies. That is fully disclosed so you can judge the research with complete context.
That alignment of incentives is intentional. We are not running ads. We are not paid to promote anything. The only revenue comes from subscriptions, which means our only incentive is to produce research that is honest about what it observed, not research that sells a story.
What we study
We do not try to cover the entire market. We maintain a curated universe of 236 subjects updated daily.
30
Indexes
S&P 500, DAX, Nikkei, Bovespa, Hang Seng, Sensex
60+
ETFs
Broad market, sectors, bonds, commodities, thematic
120+
Stocks
US, Europe (ASML, LVMH, SAP), Asia (TSM, Sony, Samsung), LatAm
8
Crypto
BTC, ETH, SOL, BNB, XRP, ADA, AVAX, DOT
14
Commodities
Gold, oil, gas, copper, wheat, coffee
Our principles
Full transparency
Every research entry and observed outcome is public. We never delete or edit a published research output after the fact.
AI with guardrails
Two separate AI models review every output before it is published. No single model has the final word.
Verifiable research history
Hit rate, average observed delta, and all closed research subjects are shown publicly. Not just the highlights.
No conflicts hidden
100% subscription revenue. No ads, no affiliate links, no sponsored content. The operator's own positions are disclosed.