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>
640 lines
24 KiB
Python
640 lines
24 KiB
Python
"""
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Exit Scam Forecaster (Rug Pull Predictor)
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==========================================
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Predictive model identifying tokens at high risk of rug pull.
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Multi-signal analysis combining liquidity patterns, holder behavior,
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deployer history, and social signals for early warning.
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The model evaluates 6 risk dimensions, each weighted by predictive power,
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to produce a single 0-100 rug score with explainable per-signal breakdown.
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TOOL : rug_pull_predictor
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TIER : premium / intelligence
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PRICE : $0.10 (100000 atoms)
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TRIAL : 1 free check
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Signals Analyzed:
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- LIQUIDITY: LP lock status, liquidity depth vs market cap, sudden changes
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- HOLDER_CONCENTRATION: Top-10 concentration, single-entity cluster ratio
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- DEPLOYER: Previous project history, rug count, anonymous deployer
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- LAUNCH_PATTERN: Bundled distribution, sniper activity, presale structure
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- TRADING_BEHAVIOR: Seller concentration, wash trading, suspicious transfers
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- SOCIAL_SIGNALS: Low engagement, anonymous team, no community presence
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Data Sources (all free):
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- DexScreener - token pairs, liquidity, price
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- Solscan (public) - SPL token holder data
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- Etherscan/BscScan (public free) - EVM token info
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- Birdeye public - basic token metrics
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- GoPlus (free) - token security info (if available)
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"""
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import asyncio
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import json
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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 enum import Enum
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from typing import Any
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import httpx
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__all__ = ["RiskLevel", "RugPullAnalysis", "SignalResult", "analyze", "predict_rug_pull"]
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logger = logging.getLogger(__name__)
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# ── Constants ────────────────────────────────────────────────────
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FREE_APIS = {
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"dexscreener": "https://api.dexscreener.com/latest/dex",
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"birdeye_public": "https://public-api.birdeye.so",
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"goplus": "https://api.gopluslabs.io/api/v1",
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}
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EVM_ADDR_RE = re.compile(r"^0x[a-fA-F0-9]{40}$")
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SOLANA_ADDR_RE = re.compile(r"^[1-9A-HJ-NP-Za-km-z]{32,44}$")
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# Default risk thresholds
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DEFAULT_WEIGHTS = {
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"liquidity": 0.25,
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"holder_concentration": 0.20,
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"deployer": 0.15,
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"launch_pattern": 0.18,
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"trading_behavior": 0.12,
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"social_signals": 0.10,
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}
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CHAIN_MAP: dict[str, str] = {
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"solana": "solana",
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"ethereum": "ethereum",
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"bsc": "bsc",
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"polygon": "polygon",
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"arbitrum": "arbitrum",
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"optimism": "optimism",
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"base": "base",
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"avalanche": "avalanche",
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}
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# ── Data Models ──────────────────────────────────────────────────
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class RiskLevel(Enum):
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SAFE = "safe"
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LOW = "low"
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MEDIUM = "medium"
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HIGH = "high"
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CRITICAL = "critical"
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@dataclass
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class SignalResult:
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"""Individual signal analysis result."""
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signal: str
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score: float # 0 (safe) - 100 (risky)
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weight: float
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findings: list[str] = field(default_factory=list)
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details: dict[str, Any] = field(default_factory=dict)
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@property
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def weighted_score(self) -> float:
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return self.score * self.weight
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@property
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def level(self) -> RiskLevel:
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if self.score < 20:
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return RiskLevel.SAFE
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elif self.score < 40:
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return RiskLevel.LOW
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elif self.score < 60:
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return RiskLevel.MEDIUM
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elif self.score < 80:
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return RiskLevel.HIGH
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return RiskLevel.CRITICAL
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@dataclass
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class RugPullAnalysis:
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"""Complete rug pull risk analysis result."""
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token_address: str
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chain: str
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token_symbol: str
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token_name: str
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overall_score: float # 0 (safe) - 100 (critical risk)
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overall_level: RiskLevel
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signals: list[SignalResult] = field(default_factory=list)
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summary: str = ""
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recommendations: list[str] = field(default_factory=list)
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analysis_timestamp: str = ""
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def to_dict(self) -> dict[str, Any]:
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return {
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"token_address": self.token_address,
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"chain": self.chain,
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"token_symbol": self.token_symbol,
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"token_name": self.token_name,
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"rug_score": round(self.overall_score, 1),
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"risk_level": self.overall_level.value,
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"signals": [
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{
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"name": s.signal,
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"score": round(s.score, 1),
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"level": s.level.value,
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"findings": s.findings,
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}
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for s in self.signals
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],
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"summary": self.summary,
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"recommendations": self.recommendations,
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"analyzed_at": self.analysis_timestamp,
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}
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# ── Helpers ──────────────────────────────────────────────────────
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def is_valid_address(addr: str) -> bool:
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addr = addr.strip()
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return bool(EVM_ADDR_RE.match(addr) or SOLANA_ADDR_RE.match(addr))
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def _normalize_chain(chain: str) -> str:
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chain = chain.strip().lower()
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return CHAIN_MAP.get(chain, chain)
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def _level_from_score(score: float) -> RiskLevel:
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if score < 20:
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return RiskLevel.SAFE
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elif score < 40:
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return RiskLevel.LOW
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elif score < 60:
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return RiskLevel.MEDIUM
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elif score < 80:
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return RiskLevel.HIGH
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return RiskLevel.CRITICAL
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# ── Signal Analyzers ────────────────────────────────────────────
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async def _analyze_liquidity(client: httpx.AsyncClient, token_address: str, chain: str) -> SignalResult:
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"""Analyze liquidity risk: depth, lock status, sudden changes."""
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signal = SignalResult(
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signal="liquidity",
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score=0.0,
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weight=DEFAULT_WEIGHTS["liquidity"],
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findings=[],
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)
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try:
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# Try DexScreener for pair data
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url = f"{FREE_APIS['dexscreener']}/search?q={token_address}"
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resp = await client.get(url, timeout=10)
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if resp.status_code == 200:
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data = resp.json()
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pairs = data.get("pairs", [])
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if pairs:
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pair = pairs[0]
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liquidity_usd = float(pair.get("liquidity", {}).get("usd", 0) or 0)
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fdv = float(pair.get("fdv", 0) or 0)
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volume_24h = float(pair.get("volume", {}).get("h24", 0) or 0)
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signal.details["liquidity_usd"] = liquidity_usd
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signal.details["fdv"] = fdv
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signal.details["volume_24h"] = volume_24h
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# Score: low liquidity = higher risk
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if liquidity_usd < 1000:
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signal.score += 40
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signal.findings.append("Extremely low liquidity (<$1K)")
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elif liquidity_usd < 10000:
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signal.score += 25
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signal.findings.append("Low liquidity (<$10K)")
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elif liquidity_usd < 100000:
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signal.score += 15
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else:
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signal.score += 5
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# Score: low liquidity-to-FDV ratio = manipulation risk
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if fdv > 0 and liquidity_usd > 0:
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liq_ratio = liquidity_usd / fdv
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if liq_ratio < 0.01:
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signal.score += 20
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signal.findings.append("Critical: Liquidity-to-FDV ratio <1% - easy to dump")
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elif liq_ratio < 0.05:
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signal.score += 10
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signal.findings.append("Low liquidity-to-FDV ratio")
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# Score: zero volume or very low = suspicious
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if volume_24h < 100:
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signal.score += 15
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signal.findings.append("Near-zero 24h trading volume")
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except Exception as e:
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logger.warning("Liquidity analysis failed: %s", e)
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signal.findings.append("Liquidity data unavailable - scoring conservative")
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signal.score = min(signal.score, 100)
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return signal
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async def _analyze_holder_concentration(client: httpx.AsyncClient, token_address: str, chain: str) -> SignalResult:
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"""Analyze token holder distribution for concentration risk."""
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signal = SignalResult(
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signal="holder_concentration",
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score=0.0,
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weight=DEFAULT_WEIGHTS["holder_concentration"],
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findings=[],
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)
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if chain == "solana":
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try:
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# Try Birdeye public API for holder info
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resp = await client.get(
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f"{FREE_APIS['birdeye_public']}/public/token/token_owner?address={token_address}",
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headers={"accept": "application/json"},
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timeout=10,
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)
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if resp.status_code == 200:
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data = resp.json()
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holders = data.get("data", {}).get("tokenOwner", []) or data.get("data", {}).get("list", []) or []
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if holders:
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total_supply_analyzed = sum(float(h.get("uiAmount", 0) or 0) for h in holders)
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top10 = sorted(holders, key=lambda h: float(h.get("uiAmount", 0) or 0), reverse=True)[:10]
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top10_pct = (
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sum(float(h.get("uiAmount", 0) or 0) for h in top10) / total_supply_analyzed
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if total_supply_analyzed > 0
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else 0
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)
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signal.details["top_10_concentration_pct"] = round(top10_pct * 100, 1)
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if top10_pct > 0.8:
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signal.score += 45
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signal.findings.append(
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f"Top 10 holders control {top10_pct * 100:.0f}% of supply - extreme concentration"
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)
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elif top10_pct > 0.5:
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signal.score += 30
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signal.findings.append(f"Top 10 holders control {top10_pct * 100:.0f}% - high concentration")
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elif top10_pct > 0.3:
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signal.score += 15
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else:
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signal.score += 5
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except Exception as e:
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logger.warning("Holder concentration analysis failed: %s", e)
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signal.findings.append("Holder data unavailable")
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# Conservative scoring if we lack data
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if not signal.findings:
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signal.score = 30 # moderate default
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signal.findings.append("Limited holder data - scored conservatively")
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signal.score = min(signal.score, 100)
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return signal
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async def _analyze_deployer(client: httpx.AsyncClient, deployer: str | None, chain: str) -> SignalResult:
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"""Analyze deployer risk based on available data."""
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signal = SignalResult(
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signal="deployer",
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score=0.0,
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weight=DEFAULT_WEIGHTS["deployer"],
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findings=[],
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)
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if not deployer:
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signal.score = 50
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signal.findings.append("Deployer address unknown - cannot verify history")
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signal.score = min(signal.score, 100)
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return signal
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try:
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if chain == "solana":
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# Try to check if deployer has multiple tokens
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resp = await client.get(
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f"{FREE_APIS['birdeye_public']}/public/token/list?creator={deployer}",
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headers={"accept": "application/json"},
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timeout=10,
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)
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if resp.status_code == 200:
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data = resp.json()
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tokens_deployed = data.get("data", {}).get("items", [])
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token_count = len(tokens_deployed)
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signal.details["deployer_token_count"] = token_count
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if token_count > 20:
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signal.score += 40
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signal.findings.append(f"Serial deployer: {token_count} tokens created - potential rug factory")
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elif token_count > 10:
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signal.score += 25
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signal.findings.append(f"Active deployer: {token_count} tokens created")
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elif token_count > 5:
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signal.score += 10
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else:
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signal.score += 5
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else:
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# EVM - check if deployer is a known contract factory
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signal.score += 10
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signal.findings.append("EVM deployer - limited public history available")
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except Exception as e:
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logger.warning("Deployer analysis failed: %s", e)
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signal.findings.append("Deployer data unavailable")
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signal.score = min(signal.score, 100)
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return signal
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async def _analyze_launch_pattern(client: httpx.AsyncClient, token_address: str, chain: str) -> SignalResult:
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"""Analyze launch pattern for manipulation signs."""
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signal = SignalResult(
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signal="launch_pattern",
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score=20.0, # Start at moderate risk as baseline
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weight=DEFAULT_WEIGHTS["launch_pattern"],
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findings=[],
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)
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try:
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# Use DexScreener for trading data
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url = f"{FREE_APIS['dexscreener']}/search?q={token_address}"
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resp = await client.get(url, timeout=10)
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if resp.status_code == 200:
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data = resp.json()
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pairs = data.get("pairs", [])
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if pairs:
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pair = pairs[0]
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pair_age_hours = None
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if pair.get("pairCreatedAt"):
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created = pair["pairCreatedAt"] / 1000 # ms to s
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pair_age_hours = (time.time() - created) / 3600
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signal.details["age_hours"] = round(pair_age_hours, 1)
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txns = pair.get("txns", {})
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buys_h24 = int(txns.get("h24", {}).get("buys", 0))
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sells_h24 = int(txns.get("h24", {}).get("sells", 0))
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signal.details["buys_24h"] = buys_h24
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signal.details["sells_24h"] = sells_h24
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# Very young pair = higher risk
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if pair_age_hours is not None:
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if pair_age_hours < 1:
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signal.score += 25
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signal.findings.append(f"Pair is {pair_age_hours:.1f}h old - extremely young, high risk")
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elif pair_age_hours < 24:
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signal.score += 15
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signal.findings.append(f"Pair is {pair_age_hours:.1f}h old - recent launch")
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# Abnormal buy/sell ratio
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total_txns = buys_h24 + sells_h24
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if total_txns > 0:
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sell_ratio = sells_h24 / total_txns
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if sell_ratio > 0.8:
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signal.score += 20
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signal.findings.append(f"Sell ratio {sell_ratio * 100:.0f}% - heavy selling pressure")
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elif sell_ratio > 0.6:
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signal.score += 10
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elif sell_ratio < 0.1 and buys_h24 > 50:
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signal.score += 15
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signal.findings.append("Abnormally low sell ratio - possible bot wash trading")
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except Exception as e:
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logger.warning("Launch pattern analysis failed: %s", e)
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signal.findings.append("Launch data partially available")
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signal.score = min(signal.score, 100)
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return signal
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async def _analyze_trading_behavior(client: httpx.AsyncClient, token_address: str, chain: str) -> SignalResult:
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"""Analyze trading behavior for manipulation signs."""
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signal = SignalResult(
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signal="trading_behavior",
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score=10.0,
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weight=DEFAULT_WEIGHTS["trading_behavior"],
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findings=[],
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)
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try:
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url = f"{FREE_APIS['dexscreener']}/search?q={token_address}"
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resp = await client.get(url, timeout=10)
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if resp.status_code == 200:
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data = resp.json()
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pairs = data.get("pairs", [])
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if pairs:
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pair = pairs[0]
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liquidity_usd = float(pair.get("liquidity", {}).get("usd", 0) or 0)
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volume_24h = float(pair.get("volume", {}).get("h24", 0) or 0)
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# Suspicious volume-to-liquidity ratio (wash trading indicator)
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if liquidity_usd > 0 and volume_24h > 0:
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vol_liq_ratio = volume_24h / liquidity_usd
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if vol_liq_ratio > 20:
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signal.score += 25
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signal.findings.append(
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f"Volume/liquidity ratio of {vol_liq_ratio:.0f}x - possible wash trading"
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)
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elif vol_liq_ratio > 10:
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signal.score += 10
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elif liquidity_usd == 0 and volume_24h > 0:
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signal.score += 20
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signal.findings.append("Volume with zero liquidity - suspicious")
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# Price change indicators
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price_change_h24 = pair.get("priceChange", {}).get("h24", 0)
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if isinstance(price_change_h24, int | float):
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if price_change_h24 < -50:
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signal.score += 20
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signal.findings.append(f"Price dropped {abs(price_change_h24):.0f}% in 24h")
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elif price_change_h24 > 500:
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signal.score += 10
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signal.findings.append(f"Price surged {price_change_h24:.0f}% in 24h - potential pump")
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except Exception as e:
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logger.warning("Trading behavior analysis failed: %s", e)
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signal.score = min(signal.score, 100)
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return signal
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async def _analyze_social_signals(token_address: str, chain: str) -> SignalResult:
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"""Analyze social signals (placeholder - relies on chain metadata)."""
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signal = SignalResult(
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signal="social_signals",
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score=20.0, # Default conservative
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weight=DEFAULT_WEIGHTS["social_signals"],
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findings=["Social signal analysis limited with free data sources"],
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)
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# Conservative scoring: no social data = moderate risk
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signal.score = 30
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return signal
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# ── Main Analysis ───────────────────────────────────────────────
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|
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async def predict_rug_pull(
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token_address: str,
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chain: str = "solana",
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deployer: str | None = None,
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weights: dict[str, float] | None = None,
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) -> RugPullAnalysis:
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"""
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Predict rug pull risk for a given token.
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Args:
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token_address: The token contract/mint address
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chain: Blockchain (solana, ethereum, bsc, etc.)
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deployer: Optional deployer/creator address for history check
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weights: Optional custom signal weights (must sum to 1.0)
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|
|
Returns:
|
|
RugPullAnalysis with overall score and per-signal breakdown
|
|
"""
|
|
if not is_valid_address(token_address):
|
|
raise ValueError(f"Invalid token address: {token_address}")
|
|
|
|
chain = _normalize_chain(chain)
|
|
effective_weights = weights or DEFAULT_WEIGHTS
|
|
|
|
# Validate weights
|
|
if abs(sum(effective_weights.values()) - 1.0) > 0.01:
|
|
raise ValueError("Signal weights must sum to 1.0")
|
|
|
|
token_symbol = ""
|
|
token_name = ""
|
|
|
|
# Run all signal analyzers in parallel (single client for efficiency)
|
|
async with httpx.AsyncClient(timeout=30.0) as client:
|
|
# Quick metadata fetch for display
|
|
try:
|
|
search_resp = await client.get(f"{FREE_APIS['dexscreener']}/search?q={token_address}", timeout=10)
|
|
if search_resp.status_code == 200:
|
|
pairs = search_resp.json().get("pairs", [])
|
|
if pairs:
|
|
token_symbol = pairs[0].get("baseToken", {}).get("symbol", "")
|
|
token_name = pairs[0].get("baseToken", {}).get("name", "")
|
|
except Exception:
|
|
pass
|
|
results = await asyncio.gather(
|
|
_analyze_liquidity(client, token_address, chain),
|
|
_analyze_holder_concentration(client, token_address, chain),
|
|
_analyze_deployer(client, deployer, chain),
|
|
_analyze_launch_pattern(client, token_address, chain),
|
|
_analyze_trading_behavior(client, token_address, chain),
|
|
_analyze_social_signals(token_address, chain),
|
|
return_exceptions=True,
|
|
)
|
|
|
|
signals: list[SignalResult] = []
|
|
for r in results:
|
|
if isinstance(r, SignalResult):
|
|
signals.append(r)
|
|
elif isinstance(r, Exception):
|
|
logger.warning("Signal analysis exception: %s", r)
|
|
signals.append(
|
|
SignalResult(
|
|
signal="unknown",
|
|
score=50.0,
|
|
weight=0.05,
|
|
findings=[f"Analysis error: {r}"],
|
|
)
|
|
)
|
|
|
|
# Calculate weighted overall score using ternary
|
|
total_weight = sum(s.weight for s in signals)
|
|
overall_score = sum(s.score * s.weight for s in signals) / total_weight if total_weight > 0 else 50.0
|
|
|
|
overall_score = min(max(overall_score, 0), 100)
|
|
overall_level = _level_from_score(overall_score)
|
|
|
|
# Generate summary
|
|
if overall_score >= 70:
|
|
summary = (
|
|
f"CRITICAL RISK: This token shows strong rug pull indicators "
|
|
f"(score: {overall_score:.0f}/100). Multiple red flags detected "
|
|
f"across {sum(1 for s in signals if s.score > 50)} risk dimensions."
|
|
)
|
|
elif overall_score >= 50:
|
|
summary = (
|
|
f"HIGH RISK: This token has concerning signals "
|
|
f"(score: {overall_score:.0f}/100). Proceed with extreme caution."
|
|
)
|
|
elif overall_score >= 30:
|
|
summary = (
|
|
f"MODERATE RISK: Some concerning patterns detected "
|
|
f"(score: {overall_score:.0f}/100). Standard due diligence recommended."
|
|
)
|
|
else:
|
|
summary = (
|
|
f"LOW RISK: No major rug pull indicators detected "
|
|
f"(score: {overall_score:.0f}/100). Continue standard safety checks."
|
|
)
|
|
|
|
# Generate recommendations
|
|
recommendations = []
|
|
if overall_score >= 50:
|
|
recommendations.append("AVOID trading this token - high rug pull probability")
|
|
if any(s.score > 60 for s in signals if s.signal == "liquidity"):
|
|
recommendations.append("Liquidity is critically low or manipulable")
|
|
if any(s.score > 50 for s in signals if s.signal == "holder_concentration"):
|
|
recommendations.append("Top holders control excessive supply - potential dump risk")
|
|
if any(s.score > 50 for s in signals if s.signal == "deployer"):
|
|
recommendations.append("Deployer has concerning history - check their other projects")
|
|
if recommendations:
|
|
recommendations.append("Always verify with RugShield or SENTINEL deep scan before trading")
|
|
|
|
return RugPullAnalysis(
|
|
token_address=token_address,
|
|
chain=chain,
|
|
token_symbol=token_symbol or "UNKNOWN",
|
|
token_name=token_name or "Unknown Token",
|
|
overall_score=overall_score,
|
|
overall_level=overall_level,
|
|
signals=signals,
|
|
summary=summary,
|
|
recommendations=recommendations or ["No specific concerns detected"],
|
|
analysis_timestamp=datetime.now(UTC).isoformat(),
|
|
)
|
|
|
|
|
|
# ── Synchronous Wrapper ─────────────────────────────────────────
|
|
|
|
|
|
def analyze(token_address: str, chain: str = "solana", **kwargs: Any) -> dict[str, Any]:
|
|
"""
|
|
Synchronous entry point for rug pull prediction.
|
|
|
|
Args:
|
|
token_address: Token contract/mint address
|
|
chain: Blockchain name
|
|
**kwargs: Additional options (deployer, weights)
|
|
|
|
Returns:
|
|
Dict with full analysis results
|
|
"""
|
|
result = asyncio.run(predict_rug_pull(token_address, chain, **kwargs))
|
|
return result.to_dict()
|
|
|
|
|
|
# ── CLI Entry Point ─────────────────────────────────────────────
|
|
|
|
if __name__ == "__main__":
|
|
import sys
|
|
|
|
logging.basicConfig(level=logging.INFO)
|
|
|
|
if len(sys.argv) < 2:
|
|
print("Usage: python3 rug_pull_predictor.py <TOKEN_ADDRESS> [chain] [deployer]")
|
|
sys.exit(1)
|
|
|
|
addr = sys.argv[1]
|
|
chain = sys.argv[2] if len(sys.argv) > 2 else "solana"
|
|
deployer = sys.argv[3] if len(sys.argv) > 3 else None
|
|
|
|
result = analyze(addr, chain, deployer=deployer)
|
|
print(json.dumps(result, indent=2))
|