Prediction Market Strategy: A Framework For Repeatable Bets
A prediction market strategy is a written set of rules for when to enter, exit, and size. Build process, measure expectancy, then scale carefully.
A prediction market strategy is a repeatable way to trade outcome contracts. Prices look like probabilities. Your job is not to "feel" the news. Your job is to define when the price is wrong enough, liquid enough, and sized safely enough to act.
Run the system, not the dopamine. Invent and prove process before you scale live size. Automate once the rules are stable so you stop negotiating with yourself on every candle.
Without a written strategy, prediction markets become a casino with charts.
The Core Objects Of A Strategy
Every serious prediction market strategy answers four questions:
- Where do I look (category, window, liquidity floor)?
- When do I enter (signal, fair value, band)?
- When do I exit (target, stop, time, resolution)?
- How much do I risk (size, max books, daily loss)?
If you cannot answer all four in writing, you do not have a strategy yet.
Edge Hypotheses That Can Be Tested
| Hypothesis type | Example idea | Failure mode |
|---|---|---|
| Model vs market | Fair 62%, market 50% | Model bias, stale inputs |
| Timing | Late window mean reversion | Regime flip, thin book |
| Structure | Both sides inventory with edge | Inventory blowup |
| Information lag | Slow crowd on known data | Already priced in |
Pick one primary hypothesis per book. Strategies that try to be everything usually measure nothing.
Building Blocks
1. Market filter
Liquidity, spread, time to resolution, category allowlist. Skip junk.
2. Signal
A number or rule you can recompute. Not "vibes after reading Twitter."
3. Execution policy
Clip size, max slippage, cancel rules. Measure fills against these assumptions.
4. Risk policy
Hard caps. No silent overrides after a loss.
5. Review policy
Settled only metrics for win rate. Volume from buys if you track fills. Be consistent.
Expectancy Beats Win Rate
A strategy can win 70% of the time and still lose money if losses are large. Track:
Expectancy ≈ (win% × avg win) − (loss% × avg loss) after fees.
Honest trading logs exist so you see this number before full-size capital teaches you the hard way.
Trading The Strategy With Discipline
- Log every fill with time and price.
- Subtract fees.
- Estimate slippage on thin books.
- Review weekly: keep, change one variable, or kill.
Do not change five knobs at once. That is not science; that is coping.
From Strategy Document To Automation
Once rules are stable:
- Encode them so they cannot "forget" at night.
- Keep experimental size and full-size risk as separate decisions.
- Require dual confirmation or explicit arming for larger capital.
- Log every decision for autopsy.
This is the Chatito philosophy: strategies over emotions, process first, scale when ready.
Strategy Anti-Patterns
- Betting narratives without a size rule
- Averaging down with no inventory plan
- Copying wallets with no lag model
- Calling a streak "skill" before sample gates
A One-Page Template
Copy this and fill it:
- Name:
- Category:
- Edge hypothesis:
- Entry:
- Exit:
- Size:
- Max open:
- Daily loss kill:
- Sample target:
- Kill criteria:
Pin it. Trade only what is on the page.
Closing
A prediction market strategy is a contract with yourself. Write it, trade it, measure expectancy, then automate only what survives. That is how prediction markets become a system instead of a mood.
Not financial advice. Trading involves risk of loss. Past results do not guarantee future performance.
FAQ
- What is a prediction market strategy?
- A written, testable set of rules for trading outcome contracts: when to enter, when to exit, how much to size, and what to skip. It is not a gut call on a single event.
- How is prediction market strategy different from stock trading?
- Prices are probability-like. Edge often comes from information, model disagreement, liquidity, or timing near resolution, not classic discounted cash flows.
- Do I need a model to have a strategy?
- You need a defined edge hypothesis. It can be statistical, structural, or information-based, but it must be testable and size-limited.
- How long should I run a strategy before scaling?
- Until you have a meaningful settled sample and positive expectancy after fees. There is no universal day count; sample quality matters more than calendar time.
- Can AI invent a prediction market strategy for me?
- AI can propose rules and analyze history, but you must still sample-test, risk-limit, and approve live. Never auto-arm large capital without human control.
Not financial advice. Trading involves risk of loss. Paper ≠ live.