Paper trading is the highest-signal beginner exercise in markets — whether you are testing a strategy on stocks, crypto, futures, or prediction markets — and an AI agent doing the execution makes paper sessions cleaner than any human can. But it only works if you measure the right things. This is a guide to what paper sessions actually prove, what they hide, and the rules for graduating to live without lying to yourself.
1. Whether the agent reads your rules the way you meant them. The most common source of "this AI is broken" complaints is not a broken model — it is a rule the user thought meant one thing and the agent interpreted as another. Paper sessions surface this divergence cheaply. The first week of any new rule is a translation test, not a profit test.
2. Whether the rule has any edge against real prices and real fills. Even on paper, your fills come from real market data: real bids, real asks, real spreads. If a rule cannot turn a profit against the actual market — under realistic-slippage paper assumptions — it definitely cannot turn one live, where slippage is worse.
3. Whether you can tolerate the variance. Most strategies that work over a year have weeks where they lose 5–15% of the test bankroll. Watching that happen on paper is the only way to learn whether you will pull the plug at the bottom of a drawdown live. If you would have shut the agent off in week 3 of a drawdown that recovered by week 6, you don't yet have the temperament for that strategy.
Fill quality. Most paper systems assume you got the price you wanted. Reality: by the time your order reached the venue, the book had moved. Your $0.42 fill might really be $0.45. On thin event-market contracts, the gap between paper-fill and real-fill can eat the entire theoretical edge of a strategy. Insist on paper that simulates realistic slippage and partial fills, and discount any paper P&L number that doesn't.
Real-money emotion. A 30% drawdown on paper feels uncomfortable. A 30% drawdown on $5,000 of your actual money feels different. The agent's behavior is the same — yours is not. The only honest test is a live deployment with a tiny real-money position, after the paper bar is cleared.
P&L over any window short of a year is dominated by variance. Decision quality is the signal you should be tracking.
For every trade the agent took, ask: did the entry condition actually fire? was the position sized within the rule? did the agent exit when the rule said to? For every trade it skipped, ask: did the conditions truly not hold, or did the agent get conservative for the wrong reasons?
A losing month with clean decision quality — agent fired the rule correctly every time, sized cleanly, took every exit signal — is a green light to keep running. A winning month with sloppy execution is a red flag, because the wins are luck and the next month will probably reveal it.
A practical, conservative rule for moving from paper to live:
A human paper trader cheats. Not deliberately — but they remember the trade they "would have skipped if I'd been live." They retroactively forgive the trade that lost as "not a real signal." Their paper journal is fiction by month two.
An AI agent's paper sessions don't lie. Every trade is logged with the rule that fired, the conditions observed, the size computed. There is no "I would have skipped that one." Either the rule fired or it didn't. This is why paper trading with an agent is, paradoxically, more honest than paper trading by hand — there is no human in the loop to soften the record.
The corollary: if you find yourself wanting to override the agent's paper decisions, you don't yet trust the rule. That's useful information. Either revise the rule or revise your trust in your own judgment — but don't pretend the override was the rule.
Whether the agent reads your rules correctly, whether the rule has edge against real prices, and whether you can tolerate the strategy's variance. It does not prove you will be profitable live.
Long enough to cover varied conditions — quiet, volatile, scheduled events. For most prediction-market strategies, 4–8 weeks minimum. Measure regimes covered, not calendar days.
Fill quality. Many paper systems assume you got the displayed price. Reality: the book moves while your order is in flight. Discount any paper P&L from a system that doesn't simulate slippage.
P&L over short windows is variance. Decision quality is signal. A losing month with clean execution is a green light; a winning month with sloppy execution is a red flag.
A little, but not as much as feels natural. Profitable paper clears a necessary bar, not a sufficient one. Audit the wins — were they the rule firing correctly, or luck?
Use what you would actually risk live. A $10M paper account teaches you nothing; a $5k paper account behaves like the live $5k account you'll eventually deploy.
TraderBear runs on paper money out of the box. Every decision is logged with the rule that fired and the inputs that were observed. Going live is an explicit, multi-step opt-in — not a checkbox you can flip by accident.
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