A common misconception: an AI trading agent that handles stocks should work for everything else. In practice the structural differences between asset classes are large enough that the agent's strategy logic, risk math, and execution layer all have to adapt. This page walks through what changes, what stays the same, and what to look for in a multi-asset agent.
| Dimension | Stocks / ETFs | Crypto | Futures | Event markets |
|---|---|---|---|---|
| Max upside per unit | Unbounded | Unbounded | Unbounded (× leverage) | $1.00 minus entry |
| Max downside per unit | Entry price (no leverage) | Entry price | Can exceed margin | Entry price |
| Hours | Regular sessions + premarket | 24/7 | Near-24h with sessions | 24/7 (until contract settles) |
| Time horizon | Open-ended | Open-ended | Expiry-bound | Fixed — each contract settles |
| Source of edge | Forecasting, momentum, fundamentals | Microstructure, sentiment, on-chain signals | Curve trades, term structure | Mispriced probability |
| Position sizing risk | Tail loss possible on shocks | Tail loss + venue risk | Tail loss + margin call | Clean — loss bounded |
| Liquidity | Very deep in major names | Deep in majors, thin in alts | Deep in front-month majors | Variable — many thin contracts |
| Typical AI use | Forecasting, signals, execution | Sentiment, signals, execution | Spread / curve detection | Mispricing detection |
The architecture that keeps an AI trading tool safe is identical across asset classes:
Long history of data, regulated venues, deep liquidity in major names. The agent's main work is signal generation and execution discipline. The structural risks are: overnight gaps in single names, earnings volatility, regulatory halts. A good agent surfaces these as explicit warnings — "Earnings in 3 days, recommend halving size or skipping entry" — rather than treating every stock the same.
24/7 trading means no "market closed" reset. The agent's pacing has to respect human sleep schedules — running indefinitely without breaks burns out the user faster than the trading does. Other structural differences: venue risk is higher (exchanges fail), on-chain signals matter, and the same ticker symbol can mean different things on different chains. A good agent surfaces these without requiring the user to be a crypto expert.
Leverage is the dominant variable. A position sizing rule that looks conservative in stocks (1% of bankroll per trade) is aggressive in futures, because the contract notional may be 50x the margin. Multi-asset agents need to translate "1% risk" into the actual contract counts that match — not just blindly pass through the same dollar amount. Contract specs (point value, tick size, margin requirement) have to be wired correctly per contract or the user gets surprised.
The cleanest risk math of the four — loss is always bounded at entry × size. But the work the agent does is fundamentally different: instead of forecasting outcomes, it's identifying when the implied probability disagrees with publicly available evidence. There is no traditional stop-loss; once you're in, you're often in until settlement. A separate page goes deep on event-market agent design.
One bear, multiple categories. The same plain-English rule input, the same TypeScript risk-cap engine, the same audit log format. The bear's stage and learning generalize across whichever categories you've taught it. Paper money is the default in every category; going live requires per-category opt-in.
Yes, if it was designed with asset-class abstraction from the start. The user-facing rule input stays the same; the platform handles each venue's microstructure under the hood. The risk-cap layer is what most matters.
Payoff shape and exit flexibility. Stocks have unbounded upside and easy exits. Event markets have binary capped payoffs and limited exit. Futures add leverage. Strategy logic has to adapt to each.
No single answer. Stocks have the most education. Event markets have the cleanest risk math. Crypto is approachable but stressful. Futures punish beginners. Start with what you can afford to lose, audit each trade weekly.
Through abstraction layers. The market layer handles venue-specific work; the user-facing rule format stays uniform. A well-designed multi-asset agent surfaces category-specific microstructure without requiring the user to learn each venue.
It can if the platform was built for one category and bolted on others. Doesn't have to. Risk caps and audit logs reuse; category-specific work varies by platform investment.
Yes — a SPY breakout rule and a Kalshi CPI rule can coexist in the same agent with shared risk-cap accounting.
TraderBear's engine works across stocks, crypto, futures, and prediction markets — with the same risk caps, the same audit log, the same paper-first default. Adopt a bear and pick the category you want to start with.
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