polymarket-wallet-xrayX-ray any Polymarket wallet — skill level, entry quality, bot detection, and edge analysis. Queries Polymarket's public APIs, no authentication needed. Inspi...
Install via ClawdBot CLI:
clawdbot install adlai88/polymarket-wallet-xrayAnalyze 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
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.
Past performance does not guarantee future results. A wallet's historical metrics tell you about:
Why copying is risky:
Use this skill to:
DO NOT use this skill to:
Use this skill when you want to:
NOT for:
# Analyze a single wallet
python wallet_xray.py 0x1234...abcd
# Analyze wallet + only look at specific market
python wallet_xray.py 0x1234...abcd "Bitcoin"
# Compare two wallets head-to-head
python wallet_xray.py 0x1111... 0x2222... --compare
# Find wallets matching criteria (top Time Profitable in market)
python wallet_xray.py "Will BTC hit $100k?" --top-wallets 5 --dry-run
# Check your account status
python scripts/status.py
APIs Used (Public, No Auth Required):
https://gamma-api.polymarket.com — Market searchhttps://clob.polymarket.com — Trade history and orderbookThe 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."
}
Wallet was profitable (not underwater) for 75% of their trading period. This wallet endured only 25% painful drawdowns — that's discipline.
They buy near the best available price. 28 basis points is normal for active traders. No evidence of FOMO market orders.
Average 45 seconds between trades. This is human. A bot would be <1 second.
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.
import subprocess
import json
# Analyze a wallet known for skilled trading
result = subprocess.run(
["python", "wallet_xray.py", "0x123...abc", "--json"],
capture_output=True,
text=True
)
data = json.loads(result.stdout)
# LEARN from their profile, don't copy blindly
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)")
# THEN: Ask yourself
# - Why are they profitable? (skill or luck?)
# - Can I replicate their decision-making process?
# - Do I have their capital size, timing, or information?
# Analyze multiple wallets to understand patterns
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']}%")
# Use this data to understand what successful trading LOOKS LIKE
# Then build your own strategy based on these insights
# Analyze before you decide what to do
result = subprocess.run(
["python", "wallet_xray.py", "0x123...abc", "--json"],
capture_output=True,
text=True
)
data = json.loads(result.stdout)
# Make an INFORMED decision based on analysis + YOUR OWN JUDGMENT
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.")
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
"Wallet has no trades"
"Market not found"
"Analysis took too long"
--limit 100 to analyze only recent trades for faster results"API rate limited"
--limit to speed up individual analyses"Connection error"
curl https://clob.polymarket.com/trades--limit 50 to reduce loadThis skill is based on the forensic trading analysis framework from @thejayden's "Autopsy of a Polymarket Whale".
The original post shows how to:
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.
Generated Mar 1, 2026
Novice traders use the skill to analyze successful Polymarket wallets and learn key metrics like Time Profitable and entry quality, helping them understand disciplined trading patterns without risking capital. This supports self-education in prediction markets by identifying what separates skilled traders from risky ones.
Investment analysts employ the skill to study wallet behaviors and detect anomalies like bots or arbitrage activity in Polymarket, providing insights into market dynamics and trader psychology. This aids in generating reports on prediction market trends and identifying potential market inefficiencies.
Security teams at prediction market platforms use the skill to flag suspicious trading patterns, such as inhuman speed or hedge checks, to monitor for bot activity and ensure fair market conditions. This helps maintain platform integrity by identifying automated strategies that may disrupt user experience.
Quantitative traders analyze wallet metrics to inform their own algorithmic strategies, using data on slippage, win rates, and behavior to refine entry and exit models in prediction markets. This enables data-driven decision-making without directly copying trades, enhancing strategy robustness.
Financial influencers and educators use the skill to generate content, such as case studies or tutorials, by comparing wallet profiles and explaining trading concepts like FOMO vs. discipline to their audience. This drives engagement through practical examples from real Polymarket data.
Offer a subscription-based platform where users access enhanced wallet analysis features, tutorials, and community insights to learn trading skills, generating revenue through monthly fees. This model targets novice traders seeking structured learning resources in prediction markets.
Provide the skill's analysis capabilities as an API service, allowing data analytics firms to integrate wallet metrics into their own products for clients in finance or research, with revenue from API usage tiers or licensing fees. This leverages the skill's public data access to offer scalable insights.
Offer consulting services where experts use the skill to conduct in-depth wallet analyses for clients like hedge funds or academic researchers, providing customized reports on trader behavior and market trends, with revenue from project-based fees. This model capitalizes on deep analytical expertise in prediction markets.
💬 Integration Tip
Integrate this skill by calling its Python scripts with wallet addresses or market queries, ensuring your system handles JSON outputs for metrics like profitability and behavior to inform decision-making without authentication.
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