Agentictrading.
When the intelligence in a trading system moves from the human who wrote the rules into the machine that makes the call.
Agentic trading is autonomous AI agents that reason about markets, make the call, and execute it end to end: not signals for a human to trade, but the whole decision, made by the machine.
What makes trading agentic.
For decades, market software did what it was told: a rule fired, an order went out. The intelligence lived in the human who wrote the rule. Agentic trading moves it into the system, into an agent that reads the market, reasons through the situation, and acts with no answer specified in advance.
Four properties separate an agent from the software that came before it:
It reasons, it doesn't just predict
A quant model outputs a number: a probability, a signal. An agent works the whole situation: what's happening, why, the options, the risks, the plan.
It decides under open-endedness
Rules only cover what their author foresaw. An agent has to act when the situation is novel or contradictory, which in live markets is most of the time.
It executes end to end
No recommendation handed to a human. The agent sizes the position, sets risk, times the entry, manages the trade, and exits: decision and action in one loop.
It adapts
Edges decay the moment they're found. An agent shifts with the market instead of running a fixed strategy until it breaks.
Algorithmic and quant systems run logic a human authored in advance. Agentic trading systems generate the decision themselves, at runtime, in situations no one scripted. That shift, from executing rules to making judgments, is what "agentic" means.
Agentic vs. algorithmic vs. quant.
Not competitors, but layers, each adding autonomy on top of the last.
| Dimension | Algorithmic | Quantitative | Agentic |
|---|---|---|---|
| Core engine | Pre-programmed rules | Statistical / ML models | Reasoning AI agents |
| Output | Order execution | Predictions and signals | End-to-end decisions and actions |
| Handles novelty | No, only scripted cases | Within the model's distribution | Yes, reasons about new situations |
| Who makes the call | The human who wrote the rule | The model, then often a human | The agent itself |
| Adapts at runtime | No | Retrained periodically | Yes, adjusts as conditions change |
A useful way to hold it: algorithmic trading automates execution, quantitative trading automates prediction, and agentic trading automates judgment. Each is harder than the last, and judgment is the one frontier models have not yet been taught to do safely with money.
Why it's hard for today's models.
Frontier large language models can talk about finance fluently. Ask one to explain a basis trade or critique a portfolio and it will perform well. But talking about markets and operating in them are different skills, and models are trained for the first, not the second.
The gap is data. Models learn finance from text: filings, news, textbooks, transcripts. None of that captures how a real decision is actually made: the live reasoning, the alternatives weighed and rejected, the sizing logic, the exit plan, and how it all turned out. That record, the decision episode, is exactly what is missing from the public internet, and exactly what agentic trading requires.
Finance also breaks the standard training playbook in ways chatbots do not. Rewards are delayed: a trade may not resolve for weeks. Good decisions lose money to variance and bad ones profit from luck, so outcomes alone are a noisy teacher. The environment is adversarial. And expert feedback is scarce and expensive. These are not reasons agentic trading is impossible; they are the reasons it needs purpose-built training data rather than more scraped text.
What an agentic trader has to learn.
Building a reliable agentic trader is a post-training problem. The model has the language and world knowledge; what it lacks is decision-making competence in markets. Teaching that requires a specific kind of data:
Complete decision episodes. Not "bought, then sold, made 4%," but the full context, reasoning, action, and outcome of each decision, captured from a live trading environment.
Reasoning traces. The step-by-step analysis behind a decision, so the model learns sound process, not just lucky results. Process supervision rewards each correct step, which separates skill from variance better than judging the final P&L.
Counterfactuals. What would have happened with different sizing, timing, or stops. Replaying each decision point under alternatives turns one trade into many training examples.
Human feedback from experts. Evaluations of whether each step of the reasoning was sound, from people who can actually judge a trading decision, not generalist labelers.
UV Labs generates this data as a byproduct of running real agents: reinforcement-learning trajectories from live traders operating real capital, captured as complete decision episodes. It is the post-training data that teaches a model to act in markets, not just describe them.
Common questions.
What is agentic trading?
Agentic trading is the use of autonomous AI agents that reason about markets, make decisions, and execute trades end to end, rather than producing signals for a human to act on. The system perceives market state, forms a thesis, sizes and times positions, manages risk, and adapts as conditions change.
How is agentic trading different from algorithmic trading?
Algorithmic trading executes fixed, pre-programmed rules. Agentic trading uses AI agents that reason about open-ended situations and decide what to do when no rule applies. Algorithmic systems follow logic a human wrote in advance; agentic systems generate the decision themselves at runtime.
How is agentic trading different from quantitative trading?
Quantitative trading uses statistical and machine-learning models to predict prices or generate signals. Agentic trading goes further: the agent reasons through the full decision, including thesis, sizing, timing, risk, and adaptation, and carries it out, rather than just predicting.
Can large language models do agentic trading?
Frontier LLMs are trained on text about markets, not on records of decisions made in them, so they describe strategy well but have no training signal for operating capital. They lack the post-training data that captures how real trading decisions are made and how they turn out. Reliable agentic trading requires teaching models on complete decision episodes, with reasoning traces, tool calls, outcomes, and counterfactuals from live markets.
What data does an agentic trading system need to be trained?
Price, news, and market data are not enough. Agentic trading models need decision data: complete episodes that capture market context, the agent's reasoning, the action taken, the outcome, and what would have happened under different choices, paired with human feedback.
Keep reading.
Building agentic trading systems?
UV Labs provides the decision data that teaches models to act in markets. Let's talk about what your models need.