| name | polymarket-wallet-xray |
| description | X-ray any Polymarket wallet — skill level, entry quality, bot detection, and edge analysis. Queries Polymarket's public APIs, no authentication needed. Inspired by @thejayden's "Autopsy of a Polymarket Whale" analysis. |
| metadata | {"author":"Simmer (@simmer_markets)","version":"1.1.4","displayName":"Polymarket Wallet X-Ray","difficulty":"beginner"} |
Polymarket Wallet X-Ray
Analyze any Polymarket wallet's trading patterns, skill level, and edge detection.
No authentication needed. Queries Polymarket's public CLOB API directly.
Inspired by: The Autopsy: How to Read the Mind of a Polymarket Whale by @thejayden
🚨 Framework, not a production trading system. Read DISCLAIMER.md before connecting to a wallet with real funds.
This skill implements the forensic trading analysis framework developed by @thejayden. Read the original post to understand the philosophy behind Time Profitable, hedge checks, bot detection, and accumulation signals.
This is an analysis tool, not a trading signal. The skill returns forensic metrics for ANY Polymarket wallet — your agent uses them to UNDERSTAND traders, learn patterns, and make informed decisions. This is for education and research, not for blindly copying positions.
⚠️ Important Disclaimer
Past performance does not guarantee future results. A wallet's historical metrics tell you about:
- ✅ How they traded in the past
- ✅ Their historical win rate and entry quality
- ❌ NOT whether their strategy will work going forward
Why copying is risky:
- Market conditions change constantly
- A trader's edge might have been luck, timing, or specific to historical events
- Slippage and fees erode thin edges to zero
- Other traders copying the same strategy destroy the edge
Use this skill to:
- ✅ Learn what skilled traders look like (metrics, behavior)
- ✅ Identify potential anomalies (bots, arbitrageurs)
- ✅ Understand trader psychology (FOMO vs. discipline)
- ✅ Inform your own strategy decisions
DO NOT use this skill to:
- ❌ Automatically copytrade wallets
- ❌ Expect to replicate their returns
- ❌ Trade on these metrics without understanding why
- ❌ Risk significant capital on patterns you don't understand
When to Use This Skill
Use this skill when you want to:
- Learn how skilled traders operate — What metrics separate winners from losers?
- Understand trading psychology — Who chases prices? Who has discipline?
- Detect bots and anomalies — Identify suspicious patterns for research
- Research arbitrage activity — Find wallets with hedged positions (educational)
- Compare trader profiles — What does a consistent trader look like vs. a lucky one?
- Inform your own strategy — Use patterns as input to YOUR decision-making, not as direct signals
NOT for:
- Copying trades blindly or automatically
- Assuming past returns = future returns
- Making large bets on these metrics alone
Setup Flow
When user asks to install or configure this skill:
-
Install the Simmer SDK
pip install simmer-sdk
-
Ask for Simmer API key
- They can get it from simmer.markets/dashboard → SDK tab
- Store in environment as
SIMMER_API_KEY
Quick Commands
python wallet_xray.py 0x1234...abcd
python wallet_xray.py 0x1234...abcd "Bitcoin"
python wallet_xray.py 0x1111... 0x2222... --compare
python wallet_xray.py "Will BTC hit $100k?" --top-wallets 5 --dry-run
python scripts/status.py
APIs Used (Public, No Auth Required):
- Gamma API:
https://gamma-api.polymarket.com/markets/keyset — Market search (cursor-paginated)
- CLOB API:
https://clob.polymarket.com — Trade history and orderbook
What You Get Back
The skill returns comprehensive forensic metrics:
{
"wallet": "0x1234...abcd",
"total_trades": 156,
"total_period_hours": 42.5,
"profitability": {
"time_profitable_pct": 75.3,
"win_rate_pct": 68.2,
"avg_profit_per_win": 0.035,
"avg_loss_per_loss": -0.018,
"realized_pnl_usd": 2450.00
},
"entry_quality": {
"avg_slippage_bps": 28,
"quality_rating": "B+",
"assessment": "Good entries, occasional FOMO"
},
"behavior": {
"is_bot_detected": false,
"trading_intensity": "high",
"avg_seconds_between_trades": 45,
"price_chasing": "moderate",
"accumulation_signal": "growing"
},
"edge_detection": {
"hedge_check_combined_avg": 0.98,
"has_arbitrage_edge": false,
"assessment": "No locked-in edge; relies on direction"
},
"risk_profile": {
"max_drawdown_pct": 12.5,
"volatility": "medium",
"max_position_concentration": 0.22
},
"recommendation": "Good trader. Skilled entries, disciplined sizing. Good metrics for learning from. Not advice to copytrade."
}
How It Works
- Fetch trade history — Download all trades this wallet made from Polymarket via Simmer API
- Compute profitability timeline — When were they underwater vs. profitable?
- Analyze entry quality — Did they buy at optimal prices or chase?
- Detect trading patterns — Bot (inhuman speed) vs. human (deliberate timing)?
- Check for arbitrage — Combined YES+NO avg < $1.00? (Potential structural edge — depends on execution and fees)
- Assess behavior — FOMO accumulation? Disciplined sizing? Rotating positions?
- Generate recommendation — Is this wallet worth following? What's the risk?
Understanding the Metrics
⏱️ Time Profitable (e.g., 75.3%)
Wallet was profitable (not underwater) for 75% of their trading period. This wallet endured only 25% painful drawdowns — that's discipline.
- >80% = Sniper-like (skilled entries, holds through drawdowns)
- 50-80% = Solid (good discipline)
- <50% = Risky (likely panic-held losses)
🎯 Entry Quality (e.g., 28 bps average slippage)
They buy near the best available price. 28 basis points is normal for active traders. No evidence of FOMO market orders.
- <20 bps = Expert. Limit orders, patience.
- 20-40 bps = Good. Balanced speed/price.
- >50 bps = Weak. Chasing prices.
🤖 Bot Detection (e.g., false)
Average 45 seconds between trades. This is human. A bot would be <1 second.
- <5 sec = Likely bot. Avoid unless you know it's a legitimate market maker.
- 5-30 sec = Possible bot.
- >30 sec = Human.
💰 Hedge Check (e.g., combined avg 0.98)
If they bought YES at $0.70 and NO at $0.30, combined = $1.00. This wallet spent exactly what they should to be neutral.
If combined < $1.00, they may have entered with a structural edge (lower combined cost than $1 payout). Actual profit depends on execution, fees, and spread.
- < $0.95 = Strong potential edge. Likely institutional/pro.
- $0.95-1.00 = Slight edge detected.
- > $1.00 = No edge; betting on direction.
Usage Examples
Example 1: Learning from a skilled trader (Analysis)
import subprocess
import json
result = subprocess.run(
["python", "wallet_xray.py", "0x123...abc", "--json"],
capture_output=True,
text=True
)
data = json.loads(result.stdout)
time_prof = data["profitability"]["time_profitable_pct"]
entry_qual = data["entry_quality"]["quality_rating"]
print(f"📊 What this trader does well:")
print(f" • Time Profitable: {time_prof}% (disciplined)")
print(f" • Entry Quality: {entry_qual} (patient buyer)")
print(f" • Behavior: {data['behavior']['accumulation_signal']} (not FOMO)")
Example 2: Research anomalies (Education)
wallets = ["0x111...", "0x222...", "0x333..."]
print("Comparing trader profiles:")
for wallet in wallets:
result = subprocess.run(
["python", "wallet_xray.py", wallet, "--json"],
capture_output=True,
text=True
)
data = json.loads(result.stdout)
is_bot = "🤖 BOT" if data["behavior"]["is_bot_detected"] else "👤 HUMAN"
print(f"\n{wallet}: {is_bot}")
print(f" Win Rate: {data['profitability']['win_rate_pct']}%")
print(f" Time Profitable: {data['profitability']['time_profitable_pct']}%")
Example 3: Informed decision-making (NOT blind copying)
result = subprocess.run(
["python", "wallet_xray.py", "0x123...abc", "--json"],
capture_output=True,
text=True
)
data = json.loads(result.stdout)
if data["profitability"]["time_profitable_pct"] > 75 and \
data["entry_quality"]["quality_rating"] in ["A", "A+"]:
print(f"✅ This wallet shows skill (high Time Profitable, good entries)")
print(f"⚠️ But I will NOT copytrade blindly.")
print(f"📋 Instead, I'll:")
print(f" 1. Backtest their patterns on fresh data")
print(f" 2. Add my own market signals")
print(f" 3. Start with small position (1-2% of capital)")
print(f" 4. Monitor for next 30 days")
print(f" 5. Adjust if it stops working")
else:
print(f"❌ This wallet doesn't show strong enough metrics.")
print(f" Safer to avoid or research further before deciding.")
Running the Skill
Analyze a single wallet (default):
python wallet_xray.py 0x1234...abcd
Analyze wallet for a specific market:
python wallet_xray.py 0x1234...abcd "Bitcoin"
Output as JSON (for scripts):
python wallet_xray.py 0x1234...abcd --json
Compare two wallets:
python wallet_xray.py 0x1111... 0x2222... --compare
Limit analysis to recent trades (faster):
python wallet_xray.py 0x1234...abcd --limit 100
Troubleshooting
"Wallet has no trades"
- This wallet hasn't traded yet, or all trades are too old
- Try a wallet you know is active
"Market not found"
- The market query didn't match anything on Polymarket
- Try a more specific market name or leave it blank to analyze all markets
"Analysis took too long"
- For wallets with >500 trades, analysis can take 30+ seconds
- Use
--limit 100 to analyze only recent trades for faster results
"API rate limited"
- You're analyzing many wallets in quick succession
- Wait a minute before trying again, or use
--limit to speed up individual analyses
"Connection error"
- Check that Polymarket's CLOB API is reachable:
curl https://clob.polymarket.com/trades
- If down, try again later or use
--limit 50 to reduce load
Credits
This skill is based on the forensic trading analysis framework from @thejayden's "Autopsy of a Polymarket Whale".
The original post shows how to:
- Spot fake gurus (high PnL, terrible entries)
- Detect bots (inhuman trading speed)
- Find arbitrage opportunities (hedged positions)
- Understand trader psychology (FOMO vs. discipline)
All metrics and analysis patterns used here are derived from that work. If you find this useful, give the original post a read and follow @thejayden.
Links
