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

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

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 — Market search
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

# 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.

polymarket-wallet-xray