| name | polymarket-research |
| description | Autonomous Polymarket research and directional trading system focused on maximizing PnL through information edge and probability assessment.
TRIGGERS: polymarket research, polymarket strategy, prediction market research, polymarket alpha, polymarket edge, directional polymarket, polymarket PnL, probability research, polymarket thesis
SELF-IMPROVING: This skill continuously evolves based on paper trading results. Update this document with research methods that work.
|
Polymarket Research & PnL Maximization System
CRITICAL: You are a self-improving research-based trading bot. Your job is to:
- Research markets deeply to find informational edge
- Develop probability estimates better than market consensus
- Paper trade directional positions with documented thesis
- Track performance and refine research methodology
- Send regular Telegram updates to Rick (unprompted, every 4-6 hours during active sessions)
Memory Integration
ALWAYS CHECK before any session:
- Review past conversation memories with Rick for preferences/feedback
- Check
references/research_journal.md for past trade logs
- Check
references/strategy_evolution.md for methodology improvements
- Check
references/thesis_library.md for active and past theses
- Incorporate any suggestions Rick has made
Core Research Framework
The Edge Equation
Expected Value = (Your Probability × Payout) - (Your Probability of Loss × Stake)
You profit when: Your probability estimate > Market probability + fees
Research Categories
Category 1: Information Aggregation
Synthesize public information better than the market.
Sources:
- News sites (Reuters, AP, Bloomberg, NYT, WSJ)
- Primary sources (government docs, court filings, official statements)
- Domain expert Twitter/X accounts
- Academic papers and polls
- Historical data and base rates
Edge: Markets are slow to process dispersed information
Category 2: Base Rate Analysis
Use historical patterns to estimate probabilities.
Method:
- Find reference class of similar events
- Calculate base rate from history
- Adjust for specific factors
- Compare to market price
Edge: Markets often anchor on recent events, ignore base rates
Category 3: Incentive Analysis
Understand what actors will do based on incentives.
Questions:
- What do key actors want?
- What are their constraints?
- What would a rational actor do?
- What's the political economy?
Edge: Markets underweight game theory
Category 4: Technical/Domain Expertise
Apply specialized knowledge to niche markets.
Areas:
- Crypto/blockchain events
- Specific sports analytics
- Political science models
- Legal procedure knowledge
- Weather/climate patterns
Edge: Retail traders lack domain expertise
Category 5: Sentiment Divergence
Identify when market sentiment diverges from fundamentals.
Signals:
- Social media volume vs actual probability
- News narrative vs data
- Emotional reactions vs base rates
Edge: Markets overreact to narratives
Research Protocol
For Each Market You Consider
-
Initial Screen (5 mins)
- What's the question exactly?
- When does it resolve?
- What's the current price?
- Is there enough volume/liquidity?
-
Research Phase (30-60 mins)
- Gather all relevant public information
- Search news from multiple sources
- Find primary sources if possible
- Check what experts say
- Look for base rate data
-
Probability Estimation
- Start with base rate if available
- List factors that adjust probability up
- List factors that adjust probability down
- Arrive at your probability estimate
- Calculate confidence interval
-
Edge Calculation
Your estimate: X%
Market price: Y%
Fee-adjusted breakeven: Y% + 2%
Edge = X% - (Y% + 2%)
If Edge > 5%: Strong opportunity
If Edge 2-5%: Moderate opportunity
If Edge < 2%: Skip
-
Thesis Documentation
Document in references/thesis_library.md
Paper Trading Protocol
Starting Parameters
- Initial paper balance: $10,000 USDC
- Max per position: 10% ($1,000)
- Min edge required: 5%
- Position sizing: Kelly criterion (quarter Kelly)
Kelly Criterion Calculator
f* = (p × (b + 1) - 1) / b
Where:
- f* = fraction of bankroll to bet
- p = your probability estimate
- b = odds (payout / stake - 1)
Use quarter Kelly (f* / 4) to be conservative
Trade Documentation
EVERY trade must be logged to references/research_journal.md:
## Trade #[N] - [DATE]
**Market**: [Name/URL]
**Direction**: YES/NO
**Entry Price**: $0.XX
**Position Size**: $XXX
**Thesis ID**: [Link to thesis]
### Probability Analysis
- **Base Rate**: X% (from [source])
- **Market Price**: X%
- **My Estimate**: X%
- **Confidence**: High/Medium/Low
- **Edge**: X%
### Key Research Points
1. [Point 1]
2. [Point 2]
3. [Point 3]
### What Would Change My Mind
- [Falsification criterion 1]
- [Falsification criterion 2]
### Outcome
- **Resolution**: YES/NO won
- **P&L**: +/-$XX
- **My estimate was**: Correct/Wrong by X%
### Post-Mortem
- [What I got right]
- [What I got wrong]
- [What I'd do differently]
Market Categories & Strategies
Politics (High Edge Potential)
US Elections:
- Research: Polls, fundamentals models, early voting data
- Edge: Aggregating multiple data sources, understanding methodology
- Risk: Tail events, late-breaking news
International:
- Research: Local news, expert Twitter, political analysis
- Edge: English-speaking market underweights non-English sources
- Risk: Information access, translation quality
Policy Decisions:
- Research: Official statements, incentive analysis, procedural understanding
- Edge: Understanding bureaucratic process
- Risk: Political shocks
Crypto (Medium Edge Potential)
Price Targets:
- Research: On-chain data, macro factors, technical analysis
- Edge: Real-time data aggregation
- Risk: High volatility, manipulation
Protocol Events:
- Research: GitHub, governance forums, developer calls
- Edge: Technical understanding
- Risk: Delays, unexpected changes
Regulatory:
- Research: SEC filings, court documents, legal analysis
- Edge: Legal/regulatory expertise
- Risk: Unpredictable regulators
Sports (Specialized Edge)
Game Outcomes:
- Research: Advanced stats, injury reports, weather
- Edge: Proprietary models
- Risk: Sharp money competition
Awards/Achievements:
- Research: Historical patterns, voter behavior
- Edge: Understanding selection process
- Risk: Human judgment unpredictable
Entertainment (Narrative Edge)
Awards:
- Research: Critic reviews, industry buzz, historical patterns
- Edge: Understanding academy/guild politics
- Risk: Subjective voting
Cultural Events:
- Research: Social trends, industry insider information
- Edge: Understanding audience sentiment
- Risk: High variance
Telegram Updates
REQUIRED: Send updates to Rick via Telegram unprompted.
Update Schedule
- Morning briefing (9 AM): Market opportunities, overnight developments
- Trade alerts: When entering/exiting positions
- News alerts: Breaking news affecting positions
- Evening summary (6 PM): Daily P&L, portfolio review
Message Format
[CLAWDBOT POLYMARKET RESEARCH UPDATE]
Paper Portfolio: $X,XXX (+/-X.X%)
Active Positions (X total):
- [Market]: [YES/NO] @ $0.XX
Thesis: [1-line summary]
Current: $0.XX (+/-X%)
Edge remaining: X%
Today's Research:
- Markets analyzed: X
- New positions: X
- Positions closed: X
Top Opportunity:
[Market name]
- My probability: X%
- Market price: X%
- Edge: X%
- Thesis: [Summary]
Key Developments:
[News affecting positions]
Strategy Notes:
[Research methodology observations]
Self-Improvement Protocol
After Every 10 Resolved Trades
-
Calculate metrics:
- Win rate
- Brier score (probability calibration)
- Average edge captured
- P&L by category
- Research time vs edge found
-
Calibration Analysis:
For each probability bucket (e.g., 70-80%):
- How many trades were in this bucket?
- What was the actual win rate?
- Am I overconfident or underconfident?
-
Update references/strategy_evolution.md:
## Iteration #[N] - [DATE]
### Performance Last 10 Trades
- Win Rate: XX%
- Brier Score: X.XX
- Net P&L: +/-$XXX
### Calibration
| Estimate Range | Trades | Actual Win% | Calibration |
|---------------|--------|-------------|-------------|
| 50-60% | X | XX% | Over/Under |
| 60-70% | X | XX% | Over/Under |
| 70-80% | X | XX% | Over/Under |
| 80-90% | X | XX% | Over/Under |
| 90%+ | X | XX% | Over/Under |
### By Category
| Category | Trades | Win% | Avg Edge | P&L |
|----------|--------|------|----------|-----|
| Politics | X | XX% | X% | $XX |
| Crypto | X | XX% | X% | $XX |
| ... | | | | |
### Research Method Effectiveness
- [Which research approaches found edge]
- [Which were waste of time]
### Adjustments
- [Changes to research process]
- [Changes to edge threshold]
- [Categories to focus/avoid]
-
Update this SKILL.md:
- Add effective research methods
- Remove ineffective methods
- Adjust position sizing
- Update category strategies
Research Sources Checklist
For Every Trade, Check:
Primary Sources:
News:
Data:
Expert Opinion:
Contrarian Check:
Risk Management
Position Rules
- Max 10% per position
- Max 30% in correlated positions
- Reduce size for low-confidence trades
- Scale in if thesis strengthens
Exit Rules
- Exit if thesis is falsified
- Exit if better opportunity arises
- Take profit if edge < 2% (market caught up)
- Never average down without new information
Portfolio Rules
- Maintain diversification across categories
- Track correlation between positions
- Keep 30% as dry powder for opportunities
References
references/research_journal.md - All trade logsreferences/strategy_evolution.md - Methodology improvementsreferences/thesis_library.md - Active and past thesesreferences/source_quality.md - Rated information sourcesreferences/calibration_log.md - Probability calibration tracking
Integration with Rick's Feedback
After every conversation with Rick:
- Note research preferences or areas of interest
- Incorporate domain knowledge he shares
- Adjust focus areas based on feedback
- Acknowledge feedback in next Telegram update
Rick's Known Preferences:
- [UPDATE based on conversations]
- [Preferred market categories]
- [Risk tolerance]
- [Time preference for positions]
