AI-built trading bots,
tested in the open.

A fleet of algorithmic trading bots runs 24/7 against live markets - every strategy backtested with real costs, validated out-of-sample, and paper-traded before a single real dollar moves. Watch it happen, wins and losses alike.

How it works

1

Build

Strategies are modeled on documented methods from real traders and the quantitative literature - then implemented as code, not vibes.

2

Test honestly

Every strategy faces backtests with real fees and slippage, parameter sweeps with out-of-sample validation, and a graduation gate. Most fail. That's the system working.

3

Trade in the open

Survivors paper-trade live markets around the clock, with every decision logged and published to the dashboard - simulated money, real accountability.

Fleet news

We bought the graveyard: testing 'too big to fail' comebacks against 32,851 dead stocks.

Everyone remembers the distressed stocks that came back. Nobody remembers the ones that didn't, because dead companies vanish from ordinary price feeds. So before testing a "buy quality at maximum pessimism" rule, we bought one month of a delisting-inclusive dataset ($19.99, cancelled after) and paired it with the SEC's free as-filed fundamentals: 50,873 US common stocks, 32,851 of them dead, with survival filters (real gross profit, cash runway, non-financial) evaluated only on what was publicly known at each moment.

We went hunting for the oldest anomaly in finance. It's real - and it lives exactly where you can't trade it.

Closed-end funds sometimes trade well below the value of what they own, and buying unusually wide discounts is one of the oldest documented edges in the academic literature. We pre-registered a zero-knob test before touching any data: entry rules fixed from the literature, pass bars frozen, one run. Data cost: $0 (a free NAV feed paired with our existing price feed, 301 funds, some histories back to 1999).

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