AI in Trading
What language models are and are not good for in a trading workflow, and where the failure modes are.
- AI and trading discipline
A model can make discipline easier or much harder, and which one depends on where you put it. The failure mode is subtle and worth naming.
- Multi-agent setups and why they compound risk
Several specialised models sounds like separation of concerns. It multiplies non-determinism, adds untrusted channels between components, and diffuses accountability.
- Context windows and stale market data
A model reasons over whatever is in its context, with no sense of how old any of it is. In a moving market that is a correctness problem, not a cost one.
- Local vs hosted models for trading
Your chart context and position data are sensitive. Where the model runs decides who else sees them, and the trade-off is capability against custody.
- Reviewing your trade journal with a model
The one AI trading use case with a clear payoff and almost no risk — provided you supply data rather than ask for impressions.
- How to evaluate an AI trading claim
Six questions that account for most of the gap between a published result and what you would experience. Use them on any tool, including ours.
- Non-determinism and what it does to testing
The same prompt, the same chart, a different trade. Why a single evaluation run is a sample rather than a result, and what to do about it.
- Why a trade plan should be structured output
Prose analysis cannot be validated, logged comparably, or checked against limits. A schema turns a model's opinion into something a program can refuse.
- When not to use AI for trading
The situations where adding a model makes things worse rather than slower — and the one where it is most tempting and most damaging.
- How AI hallucination shows up in market analysis
Not invented tickers — invented specificity. Numbers that sound like data, patterns named with confidence, and a tone that does not vary with certainty.
- A workflow for trading with an AI copilot
Where a model belongs in a trading process, in the order the steps actually happen — and which parts should never be handed over.
- Prompt injection when the model can place orders
Anything the model reads sits in the same context as your instructions. With order tools attached, that turns untrusted text into a path to your account.
- Why AI backtests do not survive live trading
A model evaluated on data it was trained on is grading its own homework. The specific leaks, and why the gap is wider for LLMs than for rule-based systems.
- Can AI predict the market?
Language models are not forecasting engines, and the honest answer is no. What they are genuinely useful for in a trading workflow, and where the claim goes wrong.