OpenClaw Polymarket Autopilot: Paper Trading and Strategy Review Workflow
Automate paper-trading simulations on prediction markets with backtesting and daily performance logs.
0) TL;DR (3-minute launch)
- Prediction-market testing is noisy when research, entry logic, and outcome tracking are manual.
- Workflow in short: Scheduled market scan → collect candidate markets and constraints → apply strategy filters and confidence thresholds → log paper entries with rationale → track resolution outcomes → publish performance and strategy diagnostics
- Start fast: Run paper-trading only for initial validation period.
- Guardrail: No autonomous real-money execution.
1) What problem this solves
Prediction-market testing is noisy when research, entry logic, and outcome tracking are manual. This workflow runs repeatable paper-trading loops with explicit risk controls.
2) Who this is for
- Operators responsible for trading research decisions
- Builders who need repeatable simulation workflows
- Teams that want automation with explicit human checkpoints
3) Workflow map
Scheduled market scan
-> collect candidate markets and constraints
-> apply strategy filters and confidence thresholds
-> log paper entries with rationale
-> track resolution outcomes
-> publish performance and strategy diagnostics4) MVP setup
- Run paper-trading only for initial validation period
- Define max concurrent positions and confidence floor
- Log every simulated entry with thesis and invalidation rule
- Review hit-rate and calibration weekly
- Pause strategy when drawdown or drift thresholds trigger
5) Prompt template
You are my prediction-market research operator. For each scan: 1) shortlist markets that fit strategy rules 2) explain thesis and counter-thesis 3) log paper-trade recommendation with confidence 4) update prior positions with outcome status Do not execute real-money actions.
6) Cost and payoff
Cost
Primary costs are model calls, integration maintenance, and periodic prompt tuning.
Payoff
Faster execution cycles, fewer context switches, and clearer decision quality over time.
Scale
Add role-specific subagents, stronger evaluation metrics, and staged automation permissions.
7) Risk boundaries
- No autonomous real-money execution
- Treat model confidence as advisory, not guarantee
- Stop strategy automatically when risk thresholds are breached
9) FAQ
How quickly can this workflow deliver value?
Most teams see meaningful results within 1-2 weeks when they keep the initial scope narrow and measurable.
What should stay manual at the beginning?
Keep ambiguous, high-risk, or customer-impacting actions behind explicit human approval until quality is proven.
How do we prevent automation drift over time?
Review logs weekly, sample outputs, and tune prompts/rules as data patterns and business goals change.
What KPI should we track first?
Track one leading metric (speed or coverage) plus one quality metric (accuracy, escalation rate, or user satisfaction).