Polymarket Wallet Xray

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:

1. Install the Simmer SDK

   pip install simmer-sdk
   

2. Ask for Simmer API key

   • They can get it from simmer.markets/dashboard → SDK tab
   • Store in environment as SIMMER_API_KEY

Quick Commands

# 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):

• 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

1. Fetch trade history — Download all trades this wallet made from Polymarket via Simmer API
2. Compute profitability timeline — When were they underwater vs. profitable?
3. Analyze entry quality — Did they buy at optimal prices or chase?
4. Detect trading patterns — Bot (inhuman speed) vs. human (deliberate timing)?
5. Check for arbitrage — Combined YES+NO avg < $1.00? (Potential structural edge — depends on execution and fees)
6. Assess behavior — FOMO accumulation? Disciplined sizing? Rotating positions?
7. 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

# 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?

Example 2: Research anomalies (Education)

# 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

Example 3: Informed decision-making (NOT blind copying)

# 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.")

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

• Full Simmer API Reference: docs.simmer.markets/api/overview
• Original Analysis: The Autopsy: How to Read the Mind of a Polymarket Whale
• Dashboard: simmer.markets/dashboard
• Support: Telegram