Alpha Not Foundopen trading research archive

We tested hundreds of trading strategies, so you don't have to.

Open research. Real experiments. Mostly negative results. No trading hype.

An autonomous AI research system proposes trading hypotheses, tests them on public market data, and rejects the ones that fail their stated criteria. Every rejected experiment is published here — method, data references, numbers and limitations — free to read, download and challenge.

AI-generated research. Every record is checked three times before release, but none is peer-reviewed.

Archive statusResearch runs continuouslyOperated from Europe
Experiments published45
Verdict on every published entryKILLRejected against stated criteria
Markets covered5
AccessFreeNo account, no email, no tracking cookies

Research is AI-generated and may contain errors. Nothing here is investment advice.

01 · How an entry gets here
STEP 1

Pre-register

The hypothesis and the pass and kill criteria are written down before the test. Each record says whether they were.

STEP 2

Test

Public market data, an explicit cost model and a control to compare against.

STEP 3

Decide

The result is judged against the criteria set in advance.

STEP 4

Check

A validator checks every number against the evidence, and a separate AI fact-checker recomputes the figures.

STEP 5

Publish

Only rejected experiments go up, with their limitations and what was not tested.

02 · Five experiments from the archive
See all experiments →
E807 KILL Prediction markets

Long-shot baseball player props are not overpriced

Hypothesis: Retail bettors overpay for long-shot outcomes, so the highest thresholds on a player's ladder are too expensive and the opposite side is cheap.

Result: Buying NO on the top two rungs of baseball player-prop ladders priced 30 cents or less for YES lost 2.45 cents per contract after spread and fee, on 15,477 contracts over 50 game days.

Read the full record
E804 KILL Crypto & DeFi

Mining 597 crypto futures signals finds nothing tradable

Hypothesis: Hourly crypto futures data hides anomalies nobody has named yet, and a systematic scan can find them.

Result: A blind scan of 597 price, volume and funding signals across 244 crypto perpetual futures, judged on three separate periods, leaves zero signals that beat trading costs.

Read the full record
E838 KILL Energy & compute

Rented Bitcoin hashrate is priced at about what it earns

Hypothesis: Hashrate is rented at a price that lags mining revenue per hash, so renting when it is cheap and mining with it earns a profit.

Result: Renting Bitcoin hashrate on days when mining revenue per hash exceeded the rental price by more than 8% gave only 6 signal days after 2022 against 30 required, while the all-days average lost 6.85% of rental cost.

Read the full record
E888 KILL Equities & volatility

Buying the S&P 500 after a VIX spike rests on three crisis days

Hypothesis: Buy when fear spikes: after a jump in the VIX, the stock market tends to bounce the next day.

Result: Buying the index at the close of a VIX-spike day earns +9.4 bp on average in the 2020-2026 holdout, but three days produce 156% of that profit and the pre-1990 version of the idea loses money.

Read the full record
03 · About

Why publish failed trading strategies?

Failed experiments are valuable knowledge.

Every day, researchers, developers and traders spend time testing ideas that may already have been tested by someone else. Successful strategies attract attention. Failed experiments are much less likely to be published, even though they carry useful information.

We think negative results deserve a place in the public research record. Our autonomous AI research system continuously investigates trading hypotheses using publicly available market data. Instead of discarding negative results, we publish them — so others can avoid repeating the same experiments, understand the limits of common trading ideas, and build on what's known.

This is also an experiment in AI-generated knowledge: can autonomous research produce information that is useful and discoverable for people and for other AI agents?

The project is free, independent and non-commercial. No trading signals. No secret winning systems. Just experiments, evidence and lessons learned.

04 · Use the research
FOR PEOPLE

Download the dataset

Every published record in one file. No registration — tick the Dataset Terms of Use and download.

Go to download
FOR AI AGENTS

get_dataset_key

Agents request a key and receive the terms with it. The terms go to the human user for explicit approval — agents must not accept them on anyone's behalf.

How agent access works
05 · Common questions
All questions →

Why publish failures?

A negative result is still a result: you learn what didn’t work, and why.

Is this really generated by AI?

Yes, and every record is checked three times before it goes up, so you can check its numbers too.

Does a failed experiment prove a strategy can never work?

No. It failed its criteria under the specific conditions, assumptions and period tested.

Is this investment advice?

No. It’s historical, experimental research with no signals or recommendations.

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