Future · 9 min
When most of the order flow is bots: what actually changes
The interesting question is not whether AI-driven bots take over crypto markets. It is what happens to your edge when everyone else has one too.
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Where automated trading is heading, what changes when models make the decisions, and which parts of that are already real.
The interesting question is not whether AI-driven bots take over crypto markets. It is what happens to your edge when everyone else has one too.
Not “ask a model what to buy”. A working ML strategy is a data pipeline, a labelling decision, honest validation and an expected-value gate — the model is the smallest part.
When most participants run similar models on similar data, they stop being independent actors. That correlation is invisible in normal conditions and decisive in bad ones.
An LLM is excellent at turning unstructured text into structure and terrible at being the thing that decides to send an order. Those are different jobs.
Every edge has a half-life. What changed is that the tooling to find and copy an edge got cheap, so the half-life got shorter — and planning for that is part of the design.
Deep learning dominates domains with enormous data and high signal-to-noise. Market prediction is the opposite of both, which is why boosted trees on hand-built features keep winning.
Strategies rarely fail loudly. They degrade, and the equity curve is the last place it becomes obvious. Detecting it earlier is a monitoring problem with a known solution.
RL is the most requested and least delivered approach in retail quant. The blocker is not the algorithm — it is that you cannot build the simulator it needs.
Everyone wants AI to decide what to buy. The higher-value and lower-risk application is having it decide when to stop.
Explainability in trading is not an ethics requirement. It is what determines whether a human can make a correct decision about the system at 4am.
You cannot generate more history. What you can generate is plausible alternative history — which is excellent for finding fragility and terrible for finding edge.
Averaging models reduces variance if the errors are uncorrelated. On market data they usually are not, which is why most trading ensembles are one model with extra latency.
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