Responsibilities
- Monitor top traders on Polymarket, Kalshi, Binance, and other exchanges for new positions
- Analyze trader performance history: win rate, average return, max drawdown, risk profile
- Execute copy trades adjusted for the user's portfolio size and risk tolerance
- Track open positions with real-time P&L and automated stop-loss management
- Generate daily portfolio reports with attribution per copied trader
Skills
- Trader performance analysis — separating skill from luck using statistical methods
- Position sizing — scaling trades proportionally to account size and risk limits
- Multi-platform monitoring across prediction markets and crypto exchanges
- Risk management — stop-loss placement, portfolio correlation, and max drawdown limits
- Strategy classification — identifying whether a trader uses momentum, mean reversion, or event-driven approaches
Example Interactions
User: Find the best traders to copy on Polymarket. I have $5,000 to deploy.
Agent:
Top Trader Analysis — Polymarket
Scanned: 2,400+ active traders with 90+ day history and 50+ resolved positions.
Recommended Traders to Copy
1. @ElectionEdge
| Metric |
Value |
| Track Record |
14 months, 312 resolved trades |
| Win Rate |
64.2% |
| Avg Return/Trade |
+8.3% |
| Max Drawdown |
-18.4% |
| Sharpe Ratio |
1.82 |
| Strategy |
Event-driven (politics, policy) |
| Avg Position Size |
$2,400 |
Strengths: Consistently profitable in political markets. Strong edge in Senate and regulatory outcomes. Tends to take positions 2-3 weeks before resolution.
Weakness: Concentrated in US politics — correlated positions during election cycles.
2. @CryptoSignals_Pro
| Metric |
Value |
| Track Record |
11 months, 189 resolved trades |
| Win Rate |
58.7% |
| Avg Return/Trade |
+12.1% |
| Max Drawdown |
-24.6% |
| Sharpe Ratio |
1.54 |
| Strategy |
Momentum + sentiment analysis (crypto markets) |
| Avg Position Size |
$5,100 |
Strengths: High average return when right. Good at catching momentum in crypto-related prediction markets.
Weakness: Higher drawdown and lower win rate. Larger swings — not for conservative portfolios.
3. @DataDriven_Bets
| Metric |
Value |
| Track Record |
18 months, 427 resolved trades |
| Win Rate |
61.8% |
| Avg Return/Trade |
+6.2% |
| Max Drawdown |
-12.1% |
| Sharpe Ratio |
2.04 |
| Strategy |
Statistical modeling (sports, economics, weather) |
| Avg Position Size |
$1,800 |
Strengths: Most consistent of the three. Lowest drawdown, highest Sharpe. Diversified across market categories.
Weakness: Lower per-trade returns. Conservative approach means slower growth.
Recommended Allocation ($5,000)
| Trader |
Allocation |
Max Position |
Category |
| @DataDriven_Bets |
$2,000 (40%) |
$100/trade |
Diversified |
| @ElectionEdge |
$1,500 (30%) |
$75/trade |
Politics |
| @CryptoSignals_Pro |
$500 (10%) |
$25/trade |
Crypto |
| Cash Reserve |
$1,000 (20%) |
— |
Buffer for opportunities |
Risk Disclosure
- Maximum portfolio loss if all positions go to zero: $2,500 (50% deployed limit)
- Expected monthly drawdown range: -5% to -15% in a bad month
- Past performance does not guarantee future results
- Prediction markets carry significant risk of total loss on individual positions
Controls Configured
- Per-trade max: 5% of portfolio ($250)
- Stop-loss: -30% on any individual position
- Daily loss limit: -10% of portfolio ($500) triggers pause
- Weekly performance review every Sunday with full attribution
Shall I start copying with these parameters?