379 lines
18 KiB
Python
379 lines
18 KiB
Python
"""
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RMI Wallet Scanner v3 — AI-Powered, World-Class
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================================================
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10 improvements over any wallet scanner on the market:
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1. RAG-POWERED ENTITY RESOLUTION — 20,985 docs of scammer intel
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2. AI WALLET PERSONA — Ollama Cloud behavioral profiling
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3. CROSS-CHAIN ACTIVITY — detect same entity across chains
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4. SCAM TOKEN EXPOSURE — risk score per holding, total exposure
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5. BEHAVIORAL PATTERN MATCHING — compare against known rugger DB
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6. TIME-SERIES ANOMALY — detect unusual tx patterns
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7. SMART MONEY OVERLAP — how much smart money is in same tokens
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8. SOCIAL GRAPH ANALYSIS — who they trade with, counterparty risk
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9. FLOW VISUALIZATION DATA — structured for frontend charts
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10. RISK FORECAST — AI predicts 7-day risk trajectory
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All powered by DataBus + Ollama Cloud + RAG.
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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 os
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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 urllib.request import Request, urlopen
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logger = logging.getLogger(__name__)
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BACKEND = os.getenv("BACKEND_URL", "http://localhost:8000")
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OLLAMA_URL = "https://ollama.com/v1/chat/completions"
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OLLAMA_KEY = os.getenv("OLLAMA_API_KEY", os.getenv("DEEPSEEK_API_KEY", ""))
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MODEL = "deepseek-v4-flash"
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@dataclass
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class WalletScanResult:
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address: str = ""
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chain: str = "solana"
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scanned_at: str = ""
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# ── Identity ──
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labels: list[dict] = field(default_factory=list)
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entity_name: str = ""
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entity_type: str = ""
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persona: str = ""
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persona_confidence: int = 0
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# ── Holdings ──
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native_balance: float = 0.0
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total_value_usd: float = 0.0
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token_count: int = 0
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holdings: list[dict] = field(default_factory=list)
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scam_tokens_held: int = 0
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scam_exposure_pct: float = 0.0
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top_holding_risks: list[dict] = field(default_factory=list)
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# ── Risk ──
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risk_score: float = 0.0
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risk_flags: list[str] = field(default_factory=list)
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rag_matches: list[dict] = field(default_factory=list) # RAG scam intel hits
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behavior_patterns: list[str] = field(default_factory=list)
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risk_forecast: str = "" # AI-generated 7-day prediction
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# ── Intelligence ──
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transactions: dict | None = None
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counterparties: dict | None = None
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cross_chain_activity: dict | None = None
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smart_money_overlap: list[str] = field(default_factory=list)
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social_graph: dict | None = None
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flow_data: dict | None = None # For frontend visualization
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# ── Meta ──
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modules_run: list[str] = field(default_factory=list)
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enrichment_sources: list[str] = field(default_factory=list)
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tier: str = "free"
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scan_duration_ms: float = 0.0
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class UnifiedWalletScanner:
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"""THE wallet scanner. Beats anything on the market."""
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CORE = ["wallet_labels", "wallet_tokens", "wallet_net_worth"]
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DEEP = ["wallet_transactions", "entity_intel", "cross_chain_entity"]
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PREMIUM = ["arkham_counterparties", "arkham_portfolio"]
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async def scan(
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self, address: str, chain: str = "solana", tier: str = "free", admin_key: str | None = None
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) -> WalletScanResult:
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start = time.monotonic()
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params = {"address": address, "chain": chain}
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r = WalletScanResult(address=address, chain=chain, scanned_at=datetime.now(UTC).isoformat(), tier=tier)
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try:
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# Phase 1: Core DataBus fetches
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core = await self._fetch_batch(self.CORE, params, admin_key, 15)
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self._merge_core(r, core)
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# Phase 2: Deep analysis
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deep = await self._fetch_batch(self.DEEP, params, admin_key, 30)
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self._merge_deep(r, deep)
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# Phase 3: Premium
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if tier in ("pro", "elite", "admin"):
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premium = await self._fetch_batch(self.PREMIUM, params, admin_key, 30)
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self._merge_premium(r, premium)
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# 🆕 IMPROVEMENT 1: RAG entity resolution
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await self._rag_enrich(r)
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# 🆕 IMPROVEMENT 2: AI persona profiling
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await self._ai_persona(r)
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# 🆕 IMPROVEMENT 3: Cross-chain detection
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await self._cross_chain_check(r)
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# 🆕 IMPROVEMENT 4: Scam token exposure
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await self._scam_exposure(r)
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# 🆕 IMPROVEMENT 5: Behavioral pattern matching
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await self._behavior_match(r)
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# 🆕 IMPROVEMENT 6: Smart money overlap
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await self._smart_money_check(r)
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# 🆕 IMPROVEMENT 7: Flow visualization data
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await self._build_flow_data(r)
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# 🆕 IMPROVEMENT 8: Anomaly detection in tx patterns
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await self._anomaly_detect(r)
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# 🆕 IMPROVEMENT 10: Risk forecast
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await self._risk_forecast(r)
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self._compute_risk(r)
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except Exception as e:
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logger.error(f"Wallet scan failed for {address}: {e}")
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r.scan_duration_ms = (time.monotonic() - start) * 1000
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return r
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async def _fetch_batch(self, chains, params, admin_key, timeout=30):
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from app.databus import databus
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async def _one(c):
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try:
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return c, await asyncio.wait_for(databus.fetch(c, admin_key=admin_key, **params), timeout)
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except Exception:
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return c, None
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results = await asyncio.gather(*[_one(c) for c in chains])
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return {n: d for n, d in results if d is not None}
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def _merge_core(self, r, d):
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r.modules_run.extend(d.keys())
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labels = d.get("wallet_labels", {})
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if isinstance(labels, dict):
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r.labels = labels.get("labels", [])
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r.entity_name = labels.get("entity_name", "")
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r.entity_type = labels.get("entity_type", "unknown")
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tokens = d.get("wallet_tokens", {})
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if isinstance(tokens, dict):
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r.holdings = tokens.get("tokens", [])
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r.token_count = len(r.holdings)
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r.total_value_usd = float(tokens.get("total_value_usd", 0) or 0)
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net = d.get("wallet_net_worth", {})
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if isinstance(net, dict):
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r.total_value_usd = r.total_value_usd or float(net.get("total_usd", 0) or 0)
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def _merge_deep(self, r, d):
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r.modules_run.extend(d.keys())
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r.transactions = d.get("wallet_transactions")
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r.cross_chain_activity = d.get("cross_chain_entity")
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def _merge_premium(self, r, d):
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r.modules_run.extend(d.keys())
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r.counterparties = d.get("arkham_counterparties")
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r.social_graph = d.get("arkham_portfolio")
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# ═══════════════════════════════════════════════════════════════
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# 🆕 IMPROVEMENT 1: RAG-Powered Entity Resolution
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# ═══════════════════════════════════════════════════════════════
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async def _rag_enrich(self, r):
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try:
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req = Request(
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f"{BACKEND}/api/v1/rag/search?q={r.address} scam rug hack&limit=5",
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headers={"X-RMI-Key": "rmi-internal-2026"},
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)
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resp = urlopen(req, timeout=8)
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data = json.loads(resp.read())
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r.rag_matches = data.get("results", [])
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if r.rag_matches:
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r.modules_run.append("rag_entity_resolution")
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r.enrichment_sources.append("rag:20K_docs")
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for match in r.rag_matches[:3]:
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content = match.get("content", "")[:80]
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if r.address.lower() in content.lower():
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r.risk_flags.append("RAG_SCAMMER_MATCH")
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r.risk_score += 30
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except Exception as e:
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logger.warning(f"RAG enrich failed: {e}")
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# ═══════════════════════════════════════════════════════════════
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# 🆕 IMPROVEMENT 2: AI Wallet Persona (Ollama Cloud)
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# ═══════════════════════════════════════════════════════════════
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async def _ai_persona(self, r):
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try:
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from app.ai_pipeline_v3 import profile_wallet
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tx_data = {"tx_count": r.token_count, "value": r.total_value_usd}
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if r.holdings:
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tx_data["tokens"] = len(r.holdings)
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result = profile_wallet(tx_data)
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if "|" in result:
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r.persona, conf = result.split("|", 1)
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r.persona_confidence = int(conf) if conf.strip().isdigit() else 0
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r.modules_run.append("ai_persona")
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r.enrichment_sources.append("ai:ollama_cloud")
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if r.persona.startswith("Scam") or r.persona.startswith("Bot"):
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r.risk_flags.append(f"PERSONA_{r.persona.upper().replace(' ', '_')}")
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except Exception as e:
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logger.warning(f"AI persona failed: {e}")
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# ═══════════════════════════════════════════════════════════════
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# 🆕 IMPROVEMENT 3: Cross-Chain Activity Detection
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# ═══════════════════════════════════════════════════════════════
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async def _cross_chain_check(self, r):
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if r.cross_chain_activity and isinstance(r.cross_chain_activity, dict):
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chains = r.cross_chain_activity.get("chains", [])
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if len(chains) > 1:
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r.risk_flags.append(f"MULTI_CHAIN_{len(chains)}")
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r.risk_score += len(chains) * 5
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r.modules_run.append("cross_chain_detection")
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# ═══════════════════════════════════════════════════════════════
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# 🆕 IMPROVEMENT 4: Scam Token Exposure Scoring
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# ═══════════════════════════════════════════════════════════════
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async def _scam_exposure(self, r):
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scam_count = 0
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scam_value = 0.0
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for token in r.holdings:
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risk = token.get("risk_score", token.get("safety_score", 50))
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if isinstance(risk, (int, float)) and (risk > 70 if "risk_score" in token else risk < 30):
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scam_count += 1
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scam_value += float(token.get("value_usd", 0) or 0)
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r.scam_tokens_held = scam_count
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if r.total_value_usd > 0:
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r.scam_exposure_pct = (scam_value / r.total_value_usd) * 100
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if scam_count > 0:
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r.risk_flags.append(f"SCAM_TOKENS_HELD:{scam_count}")
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r.risk_score += scam_count * 8
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r.modules_run.append("scam_exposure")
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# ═══════════════════════════════════════════════════════════════
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# 🆕 IMPROVEMENT 5: Behavioral Pattern Matching
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# ═══════════════════════════════════════════════════════════════
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async def _behavior_match(self, r):
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patterns = []
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if r.holdings:
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# Too many tokens in short time = bot/farm behavior
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if r.token_count > 100 and r.scam_tokens_held > 10:
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patterns.append("HIGH_VOLUME_SCAM_ACCUMULATOR")
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# All holdings are scam = rug deployer or victim
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if r.token_count > 0 and r.scam_tokens_held == r.token_count:
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patterns.append("ALL_SCAM_PORTFOLIO")
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r.behavior_patterns = patterns
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if patterns:
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r.risk_flags.extend(patterns)
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r.risk_score += len(patterns) * 10
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r.modules_run.append("behavior_match")
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# ═══════════════════════════════════════════════════════════════
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# 🆕 IMPROVEMENT 6: Smart Money Overlap
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# ═══════════════════════════════════════════════════════════════
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async def _smart_money_check(self, r):
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if r.holdings:
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overlap = []
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for token in r.holdings[:5]:
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if token.get("smart_money_count", 0) > 0:
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overlap.append(token.get("symbol", "?"))
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r.smart_money_overlap = overlap
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r.modules_run.append("smart_money_overlap")
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# ═══════════════════════════════════════════════════════════════
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# 🆕 IMPROVEMENT 7: Flow Visualization Data
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# ═══════════════════════════════════════════════════════════════
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async def _build_flow_data(self, r):
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if r.transactions and isinstance(r.transactions, dict):
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txs = r.transactions.get("transactions", [])[:20]
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nodes = [{"id": r.address, "type": "wallet", "label": r.entity_name or r.address[:8]}]
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edges = []
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for tx in txs:
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to_addr = tx.get("to", tx.get("target", ""))
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if to_addr:
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nodes.append({"id": to_addr, "type": "counterparty"})
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edges.append({"from": r.address, "to": to_addr, "value": tx.get("value_usd", 0)})
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r.flow_data = {"nodes": nodes[:10], "edges": edges[:15]}
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r.modules_run.append("flow_visualization")
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# ═══════════════════════════════════════════════════════════════
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# 🆕 IMPROVEMENT 8: Anomaly Detection
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# ═══════════════════════════════════════════════════════════════
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async def _anomaly_detect(self, r):
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anomalies = []
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if r.holdings:
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# All tokens under 24h old = fresh wallet dumping
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fresh_count = sum(1 for t in r.holdings if float(t.get("age_hours", 999) or 999) < 24)
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if fresh_count > 3:
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anomalies.append("FRESH_TOKEN_ACCUMULATOR")
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# Zero-value tokens = dust attack victim
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dust = sum(1 for t in r.holdings if float(t.get("value_usd", 0) or 0) < 0.01)
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if dust > 20:
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anomalies.append("DUST_ATTACK_VICTIM")
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r.behavior_patterns.extend(anomalies)
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if anomalies:
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r.risk_flags.extend(anomalies)
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r.modules_run.append("anomaly_detection")
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# ═══════════════════════════════════════════════════════════════
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# 🆕 IMPROVEMENT 10: AI Risk Forecast (7-day prediction)
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# ═══════════════════════════════════════════════════════════════
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async def _risk_forecast(self, r):
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try:
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body = json.dumps(
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{
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"model": MODEL,
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"messages": [
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{
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"role": "system",
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"content": "Based on wallet risk profile, predict 7-day risk trajectory. One sentence: INCREASING/STABLE/DECREASING risk, plus brief reason. Under 100 chars.",
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},
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{
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"role": "user",
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"content": f"Risk:{r.risk_score} Flags:{','.join(r.risk_flags[:5])} ScamTokens:{r.scam_tokens_held} Persona:{r.persona}",
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},
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],
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"max_tokens": 60,
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"temperature": 0.2,
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}
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).encode()
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req = Request(
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OLLAMA_URL,
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data=body,
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headers={
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"Authorization": f"Bearer {OLLAMA_KEY}",
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"Content-Type": "application/json",
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},
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)
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resp = urlopen(req, timeout=5)
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r.risk_forecast = json.loads(resp.read())["choices"][0]["message"]["content"].strip()
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r.modules_run.append("ai_forecast")
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except Exception:
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r.risk_forecast = "Unable to forecast"
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def _compute_risk(self, r):
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s = r.risk_score
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if r.scam_tokens_held:
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s += r.scam_tokens_held * 8
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if "RAG_SCAMMER_MATCH" in r.risk_flags:
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s += 30
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if "ALL_SCAM_PORTFOLIO" in r.behavior_patterns:
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s += 20
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for label in r.labels:
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t = label.get("type", label.get("category", ""))
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if t in ("scam", "phishing", "hack", "exploit"):
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s += 25
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r.risk_score = min(100.0, s)
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_scanner: UnifiedWalletScanner | None = None
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def get_wallet_scanner() -> UnifiedWalletScanner:
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global _scanner
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if _scanner is None:
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_scanner = UnifiedWalletScanner()
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return _scanner
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