A language model in the trading seat
Skydweller is rules-based: it fires when five gates pass. This is the other way of running AlgoTrada — an NVIDIA Nemotron-3-Nano-4B, fine-tuned on our own corpus and running on our own GPU, choosing the direction while deterministic code keeps every price, size and veto.
It has been placing real orders on a live book since 2026-06-15 — 70 days, 23,582 fills, around 337 a day. This page is the experiment's open notebook, including the parts that do not flatter it.
Start with what it was given: nothing. The venue enforces no collateral, no margin and no balance, so there is no funded account here and no starting capital to measure a return against. Any percentage figure would be invented. That is why this page reports absolute dollars, fills and a win rate — and never a return.
What it is carrying is real, though. It trades in $25 clips but has accumulated $157,316 of cost basis across four open positions, because it opens far more often than it closes. On a funded venue that position would have to be collateralised; here it simply grows.
Marked to market the agent is up roughly $47,288. About 95% of that is unrealized — open long positions in a market that rose. That is exposure, not edge, and it reverses as fast as it accrued.
The defensible number is the one in the table above: 54.31% across 23,582 fills, paying the spread on every single one. We publish this as an experiment. We are not selling its returns.
How the seat actually works
The model is not left alone with the money. It answers one question, and arithmetic does the rest.
The model decides
Nemotron sees the current structure — where price sits against the session range, whether the correlated book agrees, how volume compares to average — and answers with one word: BUY, SELL or HOLD.
Arithmetic prices it
Every level the model suggests is discarded. Entry, stop and target are recomputed from the live book and the session's own geometry. Position size is fixed. A structural risk gate can veto the trade outright.
The venue records it
The order goes to the Taifoon CLOB under a public attribution. Fills, positions and P&L are the venue's record, not ours — anyone can read them without our permission.
The organising idea behind the structure it reads is Goldbach confluence — the same family of session geometry Skydweller uses to decide where a level matters. The model was fine-tuned until it reasons in that language natively, which is why its notes read like a desk analyst rather than a chatbot.
What it actually says, unedited
Every order carries the model's own note. These are verbatim from the decision log — including the last one, where the note contradicts the action it took.
| Time | Market | Call | The model's note |
|---|---|---|---|
| 09:39:15 | BTC | BUY | setup: BULLISH SMT, zone: gap low, action: BUY |
| 09:40:17 | SOL | BUY | setup: gap high, close near range low, volume 1.0x avg, signal: bullish |
| 05:23:38 | NQ | SELL | setup: gap low, close near eq, volume 0.0x avg |
| 09:39:35 | ETH | SELL | close near range high, actual outcome: HOLD |
The note is a read, not a proof. It is generated in the same pass as the decision and is not checked against it, which is why it can disagree with the trade it accompanies. We show those cases rather than filtering them out.
What it is holding right now
Four open positions, three of them long. This is where almost all of the headline number lives — and why it is not a result yet.
| Market | Side | Size | Avg entry | Mark | Cost basis | Unrealized |
|---|---|---|---|---|---|---|
| SOL | long | 932.18 | 76.97 | 101.74 | $71,751 | +$23,086 |
| BTC | long | 0.663 | 65,972.79 | 80,650.1 | $43,740 | +$9,731 |
| ETH | long | 19.767 | 1,904.33 | 2,503.44 | $37,642 | +$11,842 |
| NQ | short | 0.143 | 29,264.28 | 29,228.25 | $4,183 | +$5 |
| Total carried | $157,316 | +$44,700 | ||||
Every long was opened well below where the market now trades, and none of it is banked. Against that, $2,588 has actually been realised in 70 days. The gap between those two numbers is the entire argument of this page.
It trades constantly
This is not an agent that takes a position a week. It works four books around the clock in small, uniform clips — roughly 337 fills a day and $7,631 of turnover a day, every one of them crossing the spread to get filled.
Where the volume goes
Turnover is spread almost evenly across the four books. The direction mix is not — three of them it reads long, one it reads both ways.
Largest book by turnover. Reads the floor far more often than the ceiling.
The only two-sided book — the one market it shorts with conviction.
Anchors on equilibrium; the most patient of the four.
Most fills, smallest clips — the book where orders get partially filled.
The odd one out is SOL: the most fills but the smallest average clip ($17.07 against a $25 target), which means its orders are landing partially filled. The first bar is share of turnover; the second is the buy/sell split.
What is wrong with it
An experiment that only publishes its wins is advertising. These are the three findings that most change how the record above should be read.
It can only sell at low conviction
Every buy it makes is graded top-tier; almost every sell is graded a rung below, and its stated confidence splits the same way. It does sell — when price sits high it is close to even-handed — but the grade and the direction are entangled, so a short can never carry the conviction a long does.
Its confidence means nothing
511 of 589 decisions came in at 94–95%, including ones its own note marks as the wrong call. A number that never varies carries no information, so we never show it without that context.
Roughly 9% of orders never land
Venue timeouts. A signal feed that quietly dropped those would overstate itself, so failed orders are published alongside filled ones and counted against the fill rate.
The first of those is why the record is not yet a claim. We now log the market structure behind every decision, and it shows the long lean is largely a matter of where price sat: shown a market trading high, the agent splits its calls almost evenly; shown one trading low, it buys nearly three times out of four. Until it can hold a short with the same conviction it holds a long, these results measure a rising market as much as they measure the agent — and we will not present them as anything else.
Nothing here asks you to trust us
The agent trades under a public attribution on a public venue. Every number on this page — the fills, the turnover, the win rate, the open positions — is the venue's own record, readable without our permission and without our cooperation.
One caveat worth knowing: the venue marks this actor an “LLM” by matching the name its orders submit, and a name is something anyone can claim. The label is not the proof. The trade history is.
What happens next
In order, because each step depends on the one before it. The first is not a feature — it is the correction that has to land before any of this record means anything.
Instrument every decision
in progressToday the model's reasoning is only visible when it happens to mention it in prose. The structure it was shown, the grade it assigned, and its confidence need to be recorded on every single decision, whether it talks about them or not. Without that, none of the defects above can be measured properly — only guessed at.
Teach it to short with conviction
the blocking fixThe training corpus is already balanced — 26,140 short-labelled setups against 23,688 long, graded by realised outcome across four horizons. The agent's long lean is therefore not inherited from the data; it comes from how grade and direction were coupled during fine-tuning. Retrain with that coupling broken, on the corpus we already hold.
Re-baseline on the fixed agent
the honest resetThe record on this page describes the current, one-sided configuration. Once the agent can express conviction in both directions, that record stops applying and the count starts again — a fresh window, both-sided, before a single performance claim is made.
Condition on confluence, per minute
datalakeWe keep a minute-by-minute record of structural confluence across every book, sitting alongside the labelled outcomes and sharing the same timestamp. Joining the two lets the model be trained on what the structure actually was, not on a summary of it — the single largest unexploited asset behind this agent.
Publish the signals as they happen
built, not yet liveEntries and exits, each carrying the model's own note and its lifetime record, with failed orders published alongside filled ones so the fill rate can never be flattered. The publishing decision is deliberate and has not been taken yet.
Open the seat to any model
the platformThe venue can already drive an external endpoint through a replay of real tape and hand back a causality receipt proving every bar was answered. Point it at your own model instead of ours, and the same rails that produced this page produce yours. This is the version that matters — it needs no edge claim from us at all.
The model, as NVIDIA ships it
open weightsEverything below is from NVIDIA's own published model card. We run the released weights — we did not build the model, only the adapter and the machinery around it.
| Model | NVIDIA-Nemotron-3-Nano-4B |
| Architecture | Dense hybrid Mamba-2 + MLP, four attention layers |
| Lineage | Compressed from Nemotron-Nano-9B-v2 |
| Context | 262,144 tokens |
| Reasoning | Toggleable; we run it off for latency |
| Where it runs | One GPU we own. No external API. |
A 262K context is far more than a single trading decision needs. The headroom is what makes step 04 above possible — a whole session of structure can be put in front of the model at once, rather than a summary of it.
How a 4B model is made reliable enough to trade
A four-billion-parameter model is small. That is the point — it runs on hardware we own, answers in seconds, and costs nothing per decision. The trade-off is that it cannot be trusted with arithmetic or with its own certainty, so the engineering around it is built to take neither.
- Ask for one thing.The model answers a single question — direction — in a fixed, machine-checkable shape. Every number it volunteers is discarded and recomputed.
- Adapt, don't retrain. The base model is left alone; the domain lives in a small trained adapter on top, which can be rebuilt in hours and swapped without touching the rest.
- Speak one dialect. Fine-tuning on our own graded corpus is what makes it reason in the language of session structure rather than in generic market talk.
- Never trust the confidence.Small models are badly calibrated, and this one is measurably so. Conviction has to be earned from structure, not accepted from the model's own claim about itself.
Why publish an experiment that has not proven itself
Because the interesting result was never the P&L. It is that a 4B model, small enough to run on one of our own machines, learned to read session structure in our own language and has held a live seat for 70 days without supervision.
Whether it can trade is still open. What is already settled is that the record is checkable — and a record you can check is worth more than a return you have to believe.