Wallet Intelligence Scout

Full wallet analysis: scoring, pattern detection, profiling, and bot scanning.

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Analyze any Polymarket wallet using all 4 wallet intelligence modules: scoring, behavioral analysis, deep profiling, and bot detection.

Full Code

"""Wallet intelligence: score, analyze, profile, and scan a wallet."""

import horizon as hz

WALLET = "0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045"

# ── Step 1: Score the wallet ──
score = hz.score_wallet(WALLET)
print(f"Wallet: {WALLET[:10]}...")
print(f"  Composite score: {score.composite:.2f}/100")
print(f"  Win rate:        {score.win_rate:.1%}")
print(f"  ROI:             {score.roi:.1%}")
print(f"  Sharpe:          {score.sharpe:.2f}")
print(f"  Trade count:     {score.trade_count}")
print(f"  Tier:            {score.tier}")

# ── Step 2: Behavioral analysis ──
analysis = hz.analyze_wallet(WALLET)
print(f"\nBehavioral Analysis:")
print(f"  Primary strategy:    {analysis.primary_strategy}")
print(f"  Exploitable:         {analysis.is_exploitable}")
for pattern in analysis.patterns:
    print(f"  Pattern: {pattern.name} (confidence={pattern.confidence:.2f})")
    print(f"    → {pattern.description}")

# ── Step 3: Deep profiling ──
profile = hz.profile_wallet(WALLET)
print(f"\nDeep Profile:")
print(f"  Profit strategy:     {profile.profit_strategy.name}")
print(f"  Avg hold time:       {profile.temporal.avg_hold_hours:.1f}h")
print(f"  Peak trading hour:   {profile.temporal.peak_hour}")
print(f"  Market preference:   {profile.market_selection.preferred_category}")
print(f"  Avg order size:      {profile.order_flow.avg_size:.1f} contracts")
print(f"  Edge source:         {profile.edge.source}")
print(f"  Edge decay (hours):  {profile.edge.half_life_hours:.1f}")

# ── Step 4: Bot detection ──
bots = hz.scan_bots(WALLET)
print(f"\nBot Scan:")
print(f"  Is bot:              {bots.is_bot}")
print(f"  Bot confidence:      {bots.confidence:.2f}")
print(f"  Bot type:            {bots.bot_type}")
print(f"  Indicators:          {', '.join(bots.indicators)}")

# ── Summary recommendation ──
if score.composite > 70 and not analysis.is_exploitable:
    print(f"\n✓ Strong wallet - consider copy-trading")
elif analysis.is_exploitable:
    print(f"\n⚡ Exploitable patterns - consider reverse-copy")
else:
    print(f"\n– Average wallet - monitor only")

How It Works

1. score_wallet() computes a 0–100 composite score from win rate, ROI, Sharpe, and consistency
2. analyze_wallet() identifies behavioral patterns (momentum chasing, mean reversion, news trading, etc.) and flags exploitable habits
3. profile_wallet() builds a deep profile: temporal patterns, market preferences, order flow characteristics, and edge source
4. scan_bots() detects if the wallet is likely automated (MEV bots, copy bots, arb bots)

Run It

python examples/wallet_scout.py

Pipeline Integration

Use wallet scoring inside a live strategy to dynamically weight copy signals:

"""Score wallets and auto-select the best for copy trading."""

import horizon as hz

wallets = [
    "0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045",
    "0x1234567890abcdef1234567890abcdef12345678",
    "0xabcdefabcdefabcdefabcdefabcdefabcdefabcd",
]

# Score all wallets
scores = [(w, hz.score_wallet(w)) for w in wallets]
scores.sort(key=lambda x: x[1].composite, reverse=True)

print("Wallet Rankings:")
for i, (addr, s) in enumerate(scores, 1):
    print(f"  {i}. {addr[:10]}... score={s.composite:.0f} win={s.win_rate:.0%} roi={s.roi:.0%}")

# Copy the best wallet
best_addr, best_score = scores[0]
print(f"\nCopying: {best_addr[:10]}... (score={best_score.composite:.0f})")

See Wallet Intelligence and Wallet Profiler for the full API reference.