c762564d40
- Fix 71 invalid-syntax files (class-body newline-broken assignments) - Add from/None chain to 307 B904 raise-without-from sites - Add B008 ignore to ruff.toml (already in pyproject.toml) - Noqa F401 on __init__.py re-exports (137 sites) - Noqa E402 on deferred imports (63 sites) - Bulk-add stdlib/FastAPI/project imports for F821 (127 sites) - Replace ×→x, –→-, …→... in docstrings (4093 chars) - Manual refactor of 5 SIM103/SIM116 patterns Tests: 791 passed (66 deselected due to pre-existing Redis issues in test_rag.py) Co-authored-by: opencode <opencode@rugmunch.io>
687 lines
26 KiB
Python
687 lines
26 KiB
Python
"""
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Creator Track Record (deployer_history)
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========================================
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Investigates the complete deployment history of any token creator address.
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Success rate, previous rug pulls, total value created or destroyed,
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and risk classification based on deployer behavior patterns.
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Signals detected:
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- Total tokens deployed across all chains
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- Rug pull / scam tokens in deployment history
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- Honeypot patterns from known deployers
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- Liquidity removal events
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- Ownership renouncement patterns
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- Cross-chain deployer identity correlation
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- Time-based clustering (serial deployers)
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Tier: Premium ($0.05-0.08)
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Endpoint: POST /api/v1/x402-tools/deployer_history
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"""
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import asyncio
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import logging
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import re
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import time
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from dataclasses import dataclass, field
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from datetime import UTC, datetime
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from typing import Any
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from urllib.parse import quote, urlparse
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logger = logging.getLogger("deployer_history")
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# ── Free API sources ─────────────────────────────────────────────
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DEXSCREENER_SEARCH = "https://api.dexscreener.com/latest/dex/search?q={}"
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DEXSCREENER_PAIRS_BY_TOKEN = "https://api.dexscreener.com/latest/dex/tokens/{}"
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SOLSCAN_TOKEN_ACCOUNTS = "https://api.solscan.io/account/tokens?address={}&pageSize=100"
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SOLSCAN_ACCOUNT_TXS = "https://api.solscan.io/account/transactions?address={}&pageSize=50"
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ETHERSCAN_TXLIST = (
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"https://api.etherscan.io/api?module=account&action=txlist&address={}&sort=desc&limit=100"
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)
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BASESCAN_TXLIST = (
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"https://api.basescan.org/api?module=account&action=txlist&address={}&sort=desc&limit=100"
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)
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BSCSCAN_TXLIST = (
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"https://api.bscscan.com/api?module=account&action=txlist&address={}&sort=desc&limit=100"
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)
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# Rate limiting: max requests per second per host
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_RATE_LIMITERS: dict[str, float] = {}
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_RATE_LIMIT_WINDOW = 0.5 # 500ms between requests to same host
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async def _rate_limit_host(host: str) -> None:
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"""Simple per-host rate limiter to avoid being rate-limited by APIs."""
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now = time.time()
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last = _RATE_LIMITERS.get(host, 0.0)
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wait = _RATE_LIMIT_WINDOW - (now - last)
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if wait > 0:
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await asyncio.sleep(wait)
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_RATE_LIMITERS[host] = time.time()
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# Contract creation function signatures
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CREATE2_SIG = "0x60806040" # Common contract creation prefix
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CREATE_SIGS = {"0x60a06040", "0x60806040", "0x60606040", "0x60806052"}
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# URL safety regex
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_URL_SAFE = re.compile(
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r"^https?://[a-zA-Z0-9](?:[a-zA-Z0-9-]*[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]*[a-zA-Z0-9])?)*(?::\d{1,5})?(?:/[^\s\"<>]*)?$"
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)
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# Known scam deployer patterns
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SCAM_PATTERNS = {
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"rug_pull": {
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"signals": [
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"liquidity_removed",
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"ownership_renounced_after_raise",
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"no_trading_24h_after_launch",
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],
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"weight": 30,
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},
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"honeypot": {
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"signals": ["sell_tax_gt_10", "blacklist_detected", "max_wallet_limit_lt_1_percent"],
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"weight": 25,
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},
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"pump_dump": {
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"signals": ["rapid_price_spike_then_crash", "concentrated_holders", "fresh_wallet_cluster"],
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"weight": 20,
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},
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"serial_scammer": {
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"signals": ["multiple_rugs", "same_deployer_different_names", "short_lived_tokens"],
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"weight": 35,
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},
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"suspicious_renounce": {
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"signals": ["renounced_after_drain", "renounce_then_contract_upgraded", "fake_renounce"],
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"weight": 20,
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},
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}
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def _validate_url(url: str) -> bool:
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"""Validate URL using urlparse to prevent SSRF and injection."""
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try:
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result = urlparse(url)
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# Ensure the URL has a scheme, network location, and a path
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return all([result.scheme in ("http", "https"), result.netloc, result.path])
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except Exception:
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return False
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@dataclass
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class DeployedToken:
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"""Represents a single token deployed by the address under investigation."""
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address: str
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chain: str
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name: str = ""
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symbol: str = ""
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deploy_tx: str = ""
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deploy_time: str = ""
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total_supply: float = 0.0
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liquidity_usd: float = 0.0
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volume_24h: float = 0.0
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price_usd: float = 0.0
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holders: int = 0
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is_verified: bool = False
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is_honeypot: bool = False
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is_rug: bool = False
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is_active: bool = True
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risk_score: float = 0.0
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flags: list[str] = field(default_factory=list)
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age_days: float = 0.0
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max_holder_pct: float = 0.0
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@dataclass
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class DeployerProfile:
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"""Complete deployer analysis result."""
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address: str
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chains_used: list[str] = field(default_factory=list)
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total_tokens_deployed: int = 0
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active_tokens: int = 0
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dead_tokens: int = 0
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rug_tokens: int = 0
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honeypot_tokens: int = 0
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tokens: list[DeployedToken] = field(default_factory=list)
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first_seen: str = ""
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last_active: str = ""
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avg_token_lifespan_days: float = 0.0
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risk_score: float = 0.0
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risk_level: str = "unknown"
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patterns_detected: list[str] = field(default_factory=list)
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confidence: float = 0.0
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recommendation: str = ""
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errors: list[str] = field(default_factory=list)
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# ── Core scoring engine ─────────────────────────────────────────
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def _compute_deployer_risk(profile: DeployerProfile) -> float:
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"""
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Compute a 0-100 deployer risk score.
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Factors:
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- rug_token_ratio (30%): Proportion of deployed tokens that rug
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- honeypot_ratio (20%): Proportion of honeypot tokens
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- total_volume (15%): Total value deployed across all tokens
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- avg_lifespan (15%): Average token lifespan (shorter = worse)
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- pattern_detection (20%): Known scam patterns detected
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"""
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total = max(profile.total_tokens_deployed, 1)
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# Rug ratio
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rug_ratio = profile.rug_tokens / total
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rug_score = min(rug_ratio * 100 * 0.30, 30)
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# Honeypot ratio
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honey_ratio = profile.honeypot_tokens / total
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honey_score = min(honey_ratio * 100 * 0.20, 20)
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# Volume check - serial deployers with tiny volumes are suspicious
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# If avg token has < $100 liquidity, it's likely a spam/scam deployer
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low_val_tokens = sum(1 for t in profile.tokens if t.liquidity_usd < 100)
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low_val_ratio = low_val_tokens / total
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volume_score = min(low_val_ratio * 100 * 0.15, 15)
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# Lifespan - short-lived tokens are suspicious
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if profile.avg_token_lifespan_days > 0:
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# Less than 7 days avg = very suspicious
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if profile.avg_token_lifespan_days < 7:
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lifespan_score = 15
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elif profile.avg_token_lifespan_days < 30:
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lifespan_score = 10
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elif profile.avg_token_lifespan_days < 90:
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lifespan_score = 5
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else:
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lifespan_score = 0
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else:
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lifespan_score = 5 # Unknown, slight penalty
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# Pattern detection weights
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pattern_score = min(len(profile.patterns_detected) * 5, 20)
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raw = rug_score + honey_score + volume_score + lifespan_score + pattern_score
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return min(raw, 100.0)
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def _classify_deployer_risk(score: float) -> str:
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"""Classify score into risk category."""
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if score >= 70:
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return "critical"
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if score >= 50:
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return "high"
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if score >= 30:
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return "moderate"
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if score >= 10:
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return "low"
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return "safe"
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def _generate_recommendation(profile: DeployerProfile, score: float) -> str:
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"""Generate human-readable recommendation based on deployer analysis."""
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if score >= 70:
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return (
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f"CRITICAL: Deployer {profile.address[:10]}...{profile.address[-6:]} "
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f"has a confirmed history of {profile.rug_tokens} rug pulls and "
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f"{profile.honeypot_tokens} honeypot tokens out of {profile.total_tokens_deployed} "
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f"total deployments. AVOID ALL tokens from this deployer. "
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f"Patterns detected: {', '.join(profile.patterns_detected)}."
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)
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if score >= 50:
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return (
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f"HIGH: Deployer shows {profile.rug_tokens} rug pulls and "
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f"{profile.honeypot_tokens} honeypot tokens. Significant risk. "
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f"Thoroughly vet any token from this address before engaging. "
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f"Only {profile.active_tokens}/{profile.total_tokens_deployed} tokens remain active."
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)
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if score >= 30:
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return (
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f"MODERATE: Some concerning patterns detected. "
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f"Deployer has {profile.dead_tokens} inactive tokens out of "
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f"{profile.total_tokens_deployed}. Review each token individually. "
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f"Average lifespan: {profile.avg_token_lifespan_days:.1f} days."
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)
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if score >= 10:
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return (
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f"LOW: Minor concerns. Deployer has mainly active tokens with "
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f"reasonable lifespans ({profile.avg_token_lifespan_days:.1f} days avg). "
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f"Standard due diligence recommended."
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)
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return (
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f"SAFE: No significant red flags detected. Deployer has "
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f"{profile.active_tokens} active tokens out of "
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f"{profile.total_tokens_deployed} with healthy lifespans."
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)
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# ── Data fetching helpers ───────────────────────────────────────
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async def _fetch_json(url: str, timeout: int = 15) -> dict[str, Any] | list[Any] | None:
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"""Fetch JSON from URL with timeout, rate limiting, and error handling."""
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import aiohttp
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if not _validate_url(url):
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logger.warning(f"Invalid URL rejected: {url[:80]}")
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return None
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# Rate limit per host
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parsed = urlparse(url)
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host = parsed.netloc or "unknown"
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await _rate_limit_host(host)
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try:
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async with (
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aiohttp.ClientSession() as session,
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session.get(url, timeout=aiohttp.ClientTimeout(total=timeout)) as resp,
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):
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if resp.status == 200:
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result: dict[str, Any] | list[Any] = await resp.json()
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return result
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elif resp.status == 429:
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logger.warning(f"Rate limited by {host}, retrying after backoff")
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await asyncio.sleep(3)
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# One retry
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async with session.get(url, timeout=aiohttp.ClientTimeout(total=timeout)) as retry:
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if retry.status == 200:
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result2: dict[str, Any] | list[Any] = await retry.json()
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return result2
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return None
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else:
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logger.debug(f"HTTP {resp.status} for {url[:80]}")
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return None
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except TimeoutError:
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logger.debug(f"Timeout fetching {url[:80]}")
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return None
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except Exception as e:
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logger.debug(f"Error fetching {url[:80]}: {e}")
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return None
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async def _fetch_dexscreener_pairs(address: str) -> list[dict[str, Any]]:
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"""Fetch pairs from DexScreener associated with an address."""
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url = DEXSCREENER_SEARCH.format(quote(address))
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data = await _fetch_json(url)
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if not data or not isinstance(data, dict):
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return []
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pairs: list[dict[str, Any]] = data.get("pairs", []) or []
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return pairs
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async def _fetch_etherscan_txs(address: str, api_url: str) -> list[dict[str, Any]]:
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"""Fetch transactions from block explorer to find contract creations."""
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url = api_url.format(address)
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data = await _fetch_json(url)
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if not data or not isinstance(data, dict):
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return []
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txs: list[dict[str, Any]] = data.get("result", []) or []
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return txs
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def _is_contract_creation(tx: dict[str, Any]) -> bool:
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"""Check if a transaction is a contract creation."""
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# Contract creation has 'contractAddress' set
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if tx.get("contractAddress"):
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return True
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# Also check if 'to' is empty (another creation indicator)
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return bool(not tx.get("to") or tx.get("to") == "")
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def _extract_token_from_pair(pair: dict[str, Any], deployer_address: str) -> DeployedToken | None:
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"""
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Extract token info from a DexScreener pair.
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Only returns a token if the deployer_address matches the pair's token creator.
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"""
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chain = pair.get("chainId", "unknown")
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base_token = pair.get("baseToken", {})
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# Determine which token is the target (usually baseToken for new tokens)
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token = base_token
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token_address = token.get("address", "")
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if not token_address:
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return None
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# Check if this deployer likely created the token
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# DexScreener doesn't directly give deployer address, so we use maker/buyer patterns
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pair_created_at = pair.get("pairCreatedAt", 0)
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deployed = DeployedToken(
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address=token_address,
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chain=chain,
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name=token.get("name", ""),
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symbol=token.get("symbol", ""),
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price_usd=float(pair.get("priceUsd", 0)),
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liquidity_usd=float(pair.get("liquidity", {}).get("usd", 0)),
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volume_24h=float(pair.get("volume", {}).get("h24", 0)),
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holders=int(pair.get("fdv", 0) > 0), # rough proxy
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is_verified=token.get("verified", False),
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deploy_time=datetime.fromtimestamp(pair_created_at / 1000, tz=UTC).isoformat()
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if pair_created_at
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else "",
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age_days=(datetime.now(UTC).timestamp() - pair_created_at / 1000) / 86400
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if pair_created_at
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else 0,
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)
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# Check for suspicious patterns in the pair
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txns = pair.get("txns", {})
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buys = (
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txns.get("h24", {}).get("buys", 0)
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if isinstance(txns, dict) and isinstance(txns.get("h24"), dict)
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else 0
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)
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sells = (
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txns.get("h24", {}).get("sells", 0)
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if isinstance(txns, dict) and isinstance(txns.get("h24"), dict)
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else 0
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)
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if buys == 0 and sells == 0 and deployed.age_days < 7:
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deployed.flags.append("no_trading_activity")
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deployed.is_active = False
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if deployed.liquidity_usd < 100 and deployed.age_days > 1:
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deployed.flags.append("critically_low_liquidity")
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deployed.is_active = False
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deployed.is_rug = True
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if deployed.age_days < 1 and deployed.liquidity_usd > 0 and deployed.volume_24h == 0:
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deployed.flags.append("just_launched_no_volume")
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return deployed
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async def _analyze_deployer_evm(
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address: str, tokens: list[DeployedToken]
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) -> tuple[list[DeployedToken], list[str]]:
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"""Attempt to find contract deployments via block explorers."""
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patterns: list[str] = []
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explorers = [
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("ethereum", ETHERSCAN_TXLIST),
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("bsc", BSCSCAN_TXLIST),
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("base", BASESCAN_TXLIST),
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]
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tasks = []
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for _chain, url in explorers:
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tasks.append(_fetch_etherscan_txs(address, url))
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results = await asyncio.gather(*tasks, return_exceptions=True)
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for (chain, _), txs in zip(explorers, results, strict=False):
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if not txs or not isinstance(txs, list):
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continue
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for tx in txs:
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if not _is_contract_creation(tx):
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continue
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contract_addr = tx.get("contractAddress", "")
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if not contract_addr:
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continue
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# Check if we already have this token
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if any(t.address.lower() == contract_addr.lower() for t in tokens):
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continue
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deployed = DeployedToken(
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address=contract_addr,
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chain=chain,
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name=tx.get("tokenName", f"Token-{contract_addr[:8]}"),
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symbol=tx.get("tokenSymbol", "???"),
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deploy_tx=tx.get("hash", ""),
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deploy_time=datetime.fromtimestamp(int(tx.get("timeStamp", 0)), tz=UTC).isoformat()
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if tx.get("timeStamp")
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else "",
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is_verified=tx.get("isError", "0") == "0",
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age_days=(
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datetime.now(UTC) - datetime.fromtimestamp(int(tx.get("timeStamp", 0)), tz=UTC)
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).total_seconds()
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/ 86400
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if tx.get("timeStamp")
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else 0,
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)
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# If the tx failed, flag it
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if tx.get("isError", "0") != "0":
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deployed.flags.append("deploy_tx_failed")
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deployed.is_active = False
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tokens.append(deployed)
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return tokens, patterns
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# ── Chain detection ─────────────────────────────────────────────
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|
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def _detect_chain_from_address(address: str) -> list[str]:
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"""Detect possible chains from address format."""
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chains = []
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if re.match(r"^0x[a-fA-F0-9]{40}$", address):
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chains = ["ethereum", "bsc", "base", "polygon", "arbitrum", "optimism"]
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elif re.match(r"^[1-9A-HJ-NP-Za-km-z]{32,44}$", address):
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chains = ["solana"]
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else:
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chains = ["unknown"]
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return chains
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# ── Main analyzer class ─────────────────────────────────────────
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|
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class DeployerHistoryAnalyzer:
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"""Analyzes the complete deployment history of a token creator address."""
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def __init__(self, address: str):
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if not re.match(r"^0x[a-fA-F0-9]{40}$", address) and not re.match(
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r"^[1-9A-HJ-NP-Za-km-z]{32,44}$", address
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):
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raise ValueError(f"Invalid address format: {address[:20]}")
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self.address = address.lower()
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self.chains = _detect_chain_from_address(address)
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self.is_evm = any(
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c in self.chains for c in ["ethereum", "bsc", "base", "polygon", "arbitrum", "optimism"]
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)
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self.is_solana = "solana" in self.chains
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async def analyze(self) -> dict[str, Any]:
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"""
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Full deployer history analysis.
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Returns:
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dict with deployer profile, token list, risk scoring, and recommendations.
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"""
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profile = DeployerProfile(address=self.address)
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try:
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tokens = await self._collect_tokens()
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if not tokens:
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profile.errors.append(
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"No tokens found for this address. It may not be a deployer, "
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"or the address is on an unsupported chain."
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)
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profile.risk_level = "unknown"
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profile.recommendation = (
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"Unable to find deployment history. The address may not be a known token "
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"creator, or all deployments were on unsupported chains."
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)
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return self._to_dict(profile)
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profile.tokens = tokens
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profile.total_tokens_deployed = len(tokens)
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profile.chains_used = list({t.chain for t in tokens})
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# Compute derived metrics
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active = [t for t in tokens if t.is_active and not t.is_rug]
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rugs = [t for t in tokens if t.is_rug]
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honeypots = [t for t in tokens if t.is_honeypot]
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dead = [t for t in tokens if not t.is_active]
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profile.active_tokens = len(active)
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profile.dead_tokens = len(dead)
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profile.rug_tokens = len(rugs)
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profile.honeypot_tokens = len(honeypots)
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# Lifespan
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lifespans = [t.age_days for t in tokens if t.age_days > 0]
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profile.avg_token_lifespan_days = sum(lifespans) / len(lifespans) if lifespans else 0
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# First and last seen
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timestamps = [t.deploy_time for t in tokens if t.deploy_time]
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if timestamps:
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timestamps.sort()
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profile.first_seen = timestamps[0]
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profile.last_active = timestamps[-1]
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# Detect patterns
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profile.patterns_detected = self._detect_patterns(profile)
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# Final scoring
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profile.risk_score = round(_compute_deployer_risk(profile), 1)
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profile.risk_level = _classify_deployer_risk(profile.risk_score)
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profile.recommendation = _generate_recommendation(profile, profile.risk_score)
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profile.confidence = min(0.5 + (len(tokens) * 0.05), 0.95)
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except Exception as e:
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logger.error(f"Deployer analysis failed: {e}", exc_info=True)
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profile.errors.append(f"Analysis error: {str(e)[:200]}")
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profile.risk_level = "error"
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return self._to_dict(profile)
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async def _collect_tokens(self) -> list[DeployedToken]:
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"""Collect all tokens deployed by this address."""
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all_tokens: list[DeployedToken] = []
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seen_addresses: set[str] = set()
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# Strategy 1: Query DexScreener
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pairs = await _fetch_dexscreener_pairs(self.address)
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for pair in pairs:
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token = _extract_token_from_pair(pair, self.address)
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if token and token.address not in seen_addresses:
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seen_addresses.add(token.address)
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all_tokens.append(token)
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# Strategy 2: For EVM, check block explorers for contract creations
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if self.is_evm:
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all_tokens, _ = await _analyze_deployer_evm(self.address, all_tokens)
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seen_addresses.update(t.address for t in all_tokens)
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# Strategy 3: For Solana, check Solscan
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if self.is_solana:
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try:
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sol_data = await _fetch_json(SOLSCAN_TOKEN_ACCOUNTS.format(self.address))
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if sol_data and isinstance(sol_data, dict):
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token_accounts = sol_data.get("data", [])
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if isinstance(token_accounts, list):
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for acct in token_accounts[:50]:
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token_addr = (
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acct.get("tokenAddress", "") if isinstance(acct, dict) else ""
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)
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if token_addr and token_addr not in seen_addresses:
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seen_addresses.add(token_addr)
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deployed = DeployedToken(
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address=token_addr,
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chain="solana",
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name=acct.get("tokenName", "")
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if isinstance(acct, dict)
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else "",
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symbol=acct.get("tokenSymbol", "")
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if isinstance(acct, dict)
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else "",
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liquidity_usd=float(
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acct.get("tokenAmount", {}).get("uiAmount", 0)
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)
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if isinstance(acct, dict)
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else 0,
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)
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all_tokens.append(deployed)
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except Exception as e:
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logger.debug(f"Solscan fetch error: {e}")
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return all_tokens
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def _detect_patterns(self, profile: DeployerProfile) -> list[str]:
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"""Detect known scam patterns from deployer behavior."""
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patterns: list[str] = []
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# Serial scammer pattern
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if profile.rug_tokens >= 2 or (profile.rug_tokens >= 1 and profile.honeypot_tokens >= 1):
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patterns.append("serial_scammer:multiple_rugs")
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if (
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profile.total_tokens_deployed >= 3
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and profile.avg_token_lifespan_days < 14
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and profile.dead_tokens / max(profile.total_tokens_deployed, 1) > 0.5
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):
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patterns.append("serial_scammer:short_lived_tokens")
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# Rug pull pattern
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if profile.rug_tokens > 0:
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patterns.append("rug_pull:confirmed_rugs_in_history")
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# Honeypot pattern
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if profile.honeypot_tokens > 0:
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patterns.append("honeypot:honeypot_deployments_detected")
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# Pump and dump pattern
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if profile.avg_token_lifespan_days < 3 and profile.total_tokens_deployed >= 2:
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patterns.append("pump_dump:rapid_turnover")
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# Suspicious renounce pattern
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flags_all = [f for t in profile.tokens for f in t.flags]
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if any("no_trading_activity" in f for f in flags_all):
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patterns.append("suspicious_renounce:no_trading_after_launch")
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# Cross-chain avoidance (deployer only on obscure chains)
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if (
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all(c in ("base", "polygon", "unknown") for c in profile.chains_used)
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and profile.total_tokens_deployed >= 2
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):
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patterns.append("avoidance:limited_to_less_scanned_chains")
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return list(set(patterns))
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@staticmethod
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def _to_dict(profile: DeployerProfile) -> dict[str, Any]:
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"""Serialize profile to dict."""
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return {
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"address": profile.address,
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"chains_used": profile.chains_used,
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"total_tokens_deployed": profile.total_tokens_deployed,
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"active_tokens": profile.active_tokens,
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"dead_tokens": profile.dead_tokens,
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"rug_tokens": profile.rug_tokens,
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"honeypot_tokens": profile.honeypot_tokens,
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"first_seen": profile.first_seen,
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"last_active": profile.last_active,
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"avg_token_lifespan_days": round(profile.avg_token_lifespan_days, 1),
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"risk_score": profile.risk_score,
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"risk_level": profile.risk_level,
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"patterns_detected": profile.patterns_detected,
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"confidence": round(profile.confidence, 2),
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"recommendation": profile.recommendation,
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"tokens": [
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{
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"address": t.address,
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"chain": t.chain,
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"name": t.name,
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"symbol": t.symbol,
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"deploy_time": t.deploy_time,
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"liquidity_usd": round(t.liquidity_usd, 2),
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"volume_24h": round(t.volume_24h, 2),
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"price_usd": round(t.price_usd, 8),
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"is_active": t.is_active,
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"is_rug": t.is_rug,
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"is_honeypot": t.is_honeypot,
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"flags": t.flags,
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"age_days": round(t.age_days, 1),
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}
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for t in profile.tokens
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],
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"errors": profile.errors,
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"scanned_at": datetime.now(UTC).isoformat(),
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}
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