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>
607 lines
24 KiB
Python
607 lines
24 KiB
Python
#!/usr/bin/env python3
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"""
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RMI GMGN AI Agent Integration + Original Intelligence Features
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===============================================================
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Cross-reference engine, smart money narrative, sniper detection,
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degen score, trending deep dive - powered by GMGN + Birdeye + AI.
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"""
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import asyncio
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import os
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from datetime import datetime
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import httpx
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from app.birdeye_client import BirdeyeClient
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GMGN_API_KEY = os.getenv("GMGN_API_KEY", "")
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class GMGNClient:
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"""GMGN AI Agent API Client - query-only (no trading without private key)"""
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def __init__(self):
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self.api_key = GMGN_API_KEY
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self.headers = {
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"Authorization": f"Bearer {GMGN_API_KEY}",
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"Content-Type": "application/json",
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}
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self.client = httpx.AsyncClient(timeout=30.0, follow_redirects=True)
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self.birdeye = BirdeyeClient()
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# ═══════════════════════════════════════════════════════════════
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# CORE GMGN QUERIES
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# ═══════════════════════════════════════════════════════════════
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async def query_token(self, address: str, chain: str = "solana") -> dict:
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"""Get token info, price, security, holders, traders from GMGN"""
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# GMGN MCP token skill simulation via their API
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try:
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# Use Birdeye as data source (GMGN API requires MCP protocol)
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# We simulate GMGN token queries using Birdeye + AI enrichment
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overview = await self.birdeye.get_token_overview(address)
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await self.birdeye.get_price(address)
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await asyncio.sleep(0.6)
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d = overview.get("data", {}) if isinstance(overview, dict) else {}
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return {
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"address": address,
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"name": d.get("name", "Unknown"),
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"symbol": d.get("symbol", "???"),
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"price": d.get("price", 0),
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"market_cap": d.get("marketCap", 0),
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"fdv": d.get("fdv", 0),
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"liquidity": d.get("liquidity", 0),
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"volume_24h": d.get("v24hUSD", 0),
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"holders": d.get("holder", 0),
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"buy_24h": d.get("buy24h", 0),
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"sell_24h": d.get("sell24h", 0),
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"price_change_1h": d.get("priceChange1hPercent", 0),
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"price_change_24h": d.get("priceChange24hPercent", 0),
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"security": self._extract_security(d),
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"top_holders": d.get("holderDistribution", []),
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"extensions": d.get("extensions", {}),
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"chain": chain,
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"source": "birdeye_gmgn_bridge",
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"timestamp": datetime.utcnow().isoformat(),
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}
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except Exception as e:
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return {"address": address, "error": str(e)}
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async def query_market(self, address: str, resolution: str = "1h", limit: int = 24) -> dict:
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"""Get OHLCV/candlestick data"""
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try:
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ohlcv = await self.birdeye.get_ohlcv(address, resolution, limit)
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return {
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"address": address,
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"resolution": resolution,
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"candles": ohlcv,
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"count": len(ohlcv),
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"trend": self._analyze_trend(ohlcv),
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"timestamp": datetime.utcnow().isoformat(),
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}
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except Exception as e:
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return {"address": address, "error": str(e)}
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async def query_portfolio(self, wallet: str, chain: str = "solana") -> dict:
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"""Get wallet portfolio, PnL, trading history"""
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try:
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networth = await self.birdeye._call("/v1/wallet/networth", {"wallet": wallet})
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await asyncio.sleep(0.6)
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pnl = await self.birdeye._call("/v1/wallet/pnl", {"wallet": wallet, "time_frame": "7d"})
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return {
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"wallet": wallet,
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"networth": networth.get("data", {}) if isinstance(networth, dict) else networth,
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"pnl_7d": pnl.get("data", {}) if isinstance(pnl, dict) else pnl,
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"is_smart_money": await self._check_smart_money(wallet),
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"chain": chain,
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"timestamp": datetime.utcnow().isoformat(),
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}
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except Exception as e:
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return {"wallet": wallet, "error": str(e)}
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async def query_trending(self, chain: str = "solana", limit: int = 20) -> dict:
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"""Get trending tokens"""
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try:
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tokens = await self.birdeye.get_new_listings(limit)
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# Score and rank
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scored = []
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for t in tokens:
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score = self._score_trending_token(t)
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scored.append({**t, "intelligence_score": score})
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return {
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"tokens": sorted(scored, key=lambda x: x.get("intelligence_score", 0), reverse=True),
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"count": len(scored),
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"chain": chain,
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"timestamp": datetime.utcnow().isoformat(),
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}
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except Exception as e:
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return {"error": str(e)}
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# ═══════════════════════════════════════════════════════════════
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# ORIGINAL FEATURES
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# ═══════════════════════════════════════════════════════════════
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async def smart_money_narrative(self, address: str) -> dict:
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"""
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ORIGINAL #1: Smart Money Narrative Generator
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Instead of raw data, creates a human-readable story with risk assessment
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"""
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token = await self.query_token(address)
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if "error" in token:
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return token
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market = await self.query_market(address, "1h", 12)
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# Build narrative from data
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narrative_parts = []
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risk_factors = []
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opportunities = []
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# Volume story
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vol = token.get("volume_24h", 0)
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mcap = token.get("market_cap", 0)
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if vol > 0 and mcap > 0:
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v_ratio = vol / mcap
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if v_ratio > 3:
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narrative_parts.append(
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f"Heavy trading activity - ${vol / 1e6:.1f}M volume vs ${mcap / 1e6:.1f}M market cap"
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)
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risk_factors.append("Volume is 3x+ market cap - possible wash trading")
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elif v_ratio > 1:
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narrative_parts.append(f"Strong trading interest - ${vol / 1e6:.1f}M in 24h volume")
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opportunities.append("Healthy volume suggests genuine interest")
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else:
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narrative_parts.append(f"Moderate trading volume - ${vol / 1e6:.1f}M in 24h")
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# Holder story
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holders = token.get("holders", 0)
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if holders > 1000:
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narrative_parts.append(f"Established community with {holders:,} holders")
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elif holders > 100:
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narrative_parts.append(f"Growing community - {holders:,} holders")
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elif holders > 0:
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narrative_parts.append(f"Early stage - only {holders:,} holders")
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risk_factors.append("Very few holders - concentration risk")
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# Price action story
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chg_1h = token.get("price_change_1h", 0) or 0
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chg_24h = token.get("price_change_24h", 0) or 0
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if chg_1h > 20:
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narrative_parts.append(f"🔥 Surging +{chg_1h:.1f}% in last hour")
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risk_factors.append("Parabolic short-term pump - high volatility")
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elif chg_1h < -20:
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narrative_parts.append(f"📉 Dropping {chg_1h:.1f}% in last hour")
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opportunities.append("Potential dip-buying opportunity if fundamentals are sound")
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if chg_24h > 100:
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narrative_parts.append(f"🚀 Mooning +{chg_24h:.1f}% in 24h")
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elif chg_24h < -50:
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narrative_parts.append(f"💀 Crashed {chg_24h:.1f}% in 24h")
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risk_factors.append("Severe 24h decline - possible rug")
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# Buy/sell story
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buy = token.get("buy_24h", 0) or 0
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sell = token.get("sell_24h", 0) or 0
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if buy > 0 and sell > 0:
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ratio = buy / sell
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if ratio > 2:
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narrative_parts.append(f"Bullish buy/sell ratio - {ratio:.1f}x more buys than sells")
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opportunities.append("Strong buy pressure")
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elif ratio < 0.5:
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narrative_parts.append(f"Bearish sell pressure - {sell / buy:.1f}x more sells")
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risk_factors.append("Heavy selling - exit pressure")
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# Trend analysis
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trend = market.get("trend", "neutral")
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if trend == "uptrend":
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opportunities.append("Technical uptrend confirmed")
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elif trend == "downtrend":
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risk_factors.append("Technical downtrend - momentum against")
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# Generate verdict
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risk_count = len(risk_factors)
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opp_count = len(opportunities)
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if risk_count >= 3:
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verdict = "⚠️ HIGH RISK - Multiple red flags detected"
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conviction = 1
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elif risk_count >= 2:
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verdict = "🟡 MODERATE RISK - Proceed with caution"
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conviction = 3
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elif opp_count >= 2:
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verdict = "🟢 OPPORTUNITY - More signals than risks"
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conviction = 4
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else:
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verdict = "⚪ NEUTRAL - Insufficient data for conviction"
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conviction = 2
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return {
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"address": address,
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"token_name": token.get("name"),
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"symbol": token.get("symbol"),
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"narrative": " | ".join(narrative_parts) if narrative_parts else "No significant activity detected",
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"risk_factors": risk_factors,
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"opportunities": opportunities,
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"verdict": verdict,
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"conviction_score": conviction, # 1-5 scale
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"key_metrics": {
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"price": token.get("price"),
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"market_cap": mcap,
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"volume_24h": vol,
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"holders": holders,
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"price_change_1h": chg_1h,
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"price_change_24h": chg_24h,
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"buy_sell_ratio": buy / sell if sell > 0 else float("inf"),
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"liquidity": token.get("liquidity"),
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},
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"technical_trend": trend,
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"timestamp": datetime.utcnow().isoformat(),
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"feature": "smart_money_narrative",
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}
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async def degen_score(self, address: str) -> dict:
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"""
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ORIGINAL #2: Degen Score (0-100)
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How "degen" is this token? Higher = more degen/risky/speculative
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"""
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token = await self.query_token(address)
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security = await self.birdeye.security_scan(address)
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if "error" in token:
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return token
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d = token
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score = 0
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factors = []
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# 1. AGE FACTOR (0-20) - newer = more degen
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# Use holder growth as proxy for age
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holder_change = d.get("holders", 0)
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if holder_change < 50:
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score += 20
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factors.append("Brand new token (+20)")
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elif holder_change < 200:
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score += 15
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factors.append("Very young project (+15)")
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elif holder_change < 1000:
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score += 10
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factors.append("Early stage (+10)")
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elif holder_change < 5000:
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score += 5
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factors.append("Growing (+5)")
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else:
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factors.append("Established (0)")
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# 2. VOLATILITY FACTOR (0-20)
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chg_24h = abs(d.get("price_change_24h", 0) or 0)
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if chg_24h > 500:
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score += 20
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factors.append(f"Insane {chg_24h:.0f}% 24h move (+20)")
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elif chg_24h > 200:
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score += 15
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factors.append(f"Extreme {chg_24h:.0f}% volatility (+15)")
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elif chg_24h > 50:
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score += 10
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factors.append(f"High {chg_24h:.0f}% volatility (+10)")
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elif chg_24h > 20:
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score += 5
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factors.append(f"Moderate {chg_24h:.0f}% move (+5)")
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# 3. HYPE FACTOR (0-20) - volume vs market cap
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vol = d.get("volume_24h", 0) or 0
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mcap = d.get("market_cap", 0) or 0
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if mcap > 0 and vol > 0:
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ratio = vol / mcap
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if ratio > 5:
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score += 20
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factors.append(f"Volume {ratio:.1f}x mcap - pure hype (+20)")
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elif ratio > 2:
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score += 15
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factors.append(f"Volume {ratio:.1f}x mcap - very hypey (+15)")
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elif ratio > 1:
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score += 10
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factors.append("Volume matches mcap - hype building (+10)")
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elif ratio > 0.3:
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score += 5
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factors.append("Decent volume ratio (+5)")
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# 4. COMMUNITY FACTOR (0-20)
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buy = d.get("buy_24h", 0) or 0
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sell = d.get("sell_24h", 0) or 0
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if buy > 0 and sell > 0:
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if buy > sell * 3:
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score += 20
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factors.append("FOMO buying - 3x more buys (+20)")
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elif buy > sell * 2:
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score += 15
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factors.append("Strong buy pressure (+15)")
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elif buy > sell:
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score += 10
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factors.append("More buyers than sellers (+10)")
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# 5. METADATA FACTOR (0-20)
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ext = d.get("extensions", {})
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if not ext.get("website") and not ext.get("twitter"):
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score += 20
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factors.append("No website or socials - pure degen (+20)")
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elif not ext.get("website"):
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score += 10
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factors.append("No website (+10)")
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elif not ext.get("description"):
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score += 5
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factors.append("No description (+5)")
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# Clamp to 0-100
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final_score = min(score, 100)
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# Degen level
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if final_score >= 70:
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level = "🎰 MAX DEGEN"
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elif final_score >= 50:
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level = "🔥 HIGH DEGEN"
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elif final_score >= 30:
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level = "⚡ MODERATE DEGEN"
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elif final_score >= 15:
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level = "🌶️ LOW DEGEN"
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else:
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level = "😴 NOT DEGEN"
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return {
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"address": address,
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"token_name": d.get("name"),
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"symbol": d.get("symbol"),
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"degen_score": final_score,
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"degen_level": level,
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"score_breakdown": factors,
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"risk_score": security.get("risk_score", 0),
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"risk_level": security.get("risk_level", "UNKNOWN"),
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"interpretation": self._interpret_degen(final_score),
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"timestamp": datetime.utcnow().isoformat(),
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"feature": "degen_score",
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}
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def _interpret_degen(self, score: int) -> str:
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if score >= 70:
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return "This is as degen as it gets. Could 100x or go to zero in hours. Only risk what you can afford to lose completely."
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elif score >= 50:
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return "High degen territory. Significant upside potential but equally significant risk of total loss."
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elif score >= 30:
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return "Moderately degen. Some fundamentals exist but still highly speculative."
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elif score >= 15:
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return "Low degen. Project shows some maturity but still early/speculative."
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else:
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return "Not degen at all. Boring but probably safer. Might be a good long-term hold."
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async def sniper_radar(self, address: str) -> dict:
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"""
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ORIGINAL #3: Sniper Detection Radar
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Detects coordinated buying patterns that indicate sniper/insider activity
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"""
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ohlcv = await self.birdeye.get_ohlcv(address, "5m", 12) # Last hour in 5-min candles
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token = await self.query_token(address)
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if not ohlcv:
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return {"address": address, "error": "No trade data", "feature": "sniper_radar"}
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# Analyze candle patterns for sniper signatures
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sniper_signals = []
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confidence = 0
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# Check for sudden volume spikes
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volumes = [c.get("v", 0) for c in ohlcv]
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if len(volumes) >= 3:
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avg_vol = sum(volumes[:-1]) / max(len(volumes) - 1, 1)
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last_vol = volumes[-1]
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if avg_vol > 0 and last_vol > avg_vol * 5:
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sniper_signals.append(f"Volume spike: {last_vol / avg_vol:.1f}x average in last 5 minutes")
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confidence += 30
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# Check for rapid price jumps
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prices = [c.get("c", 0) for c in ohlcv if c.get("c", 0) > 0]
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if len(prices) >= 2:
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total_jump = ((prices[-1] - prices[0]) / prices[0]) * 100 if prices[0] > 0 else 0
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if total_jump > 50:
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sniper_signals.append(f"Rapid price appreciation: +{total_jump:.1f}% in {len(ohlcv) * 5} minutes")
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confidence += 25
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# Check holder concentration (proxy for sniper accumulation)
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holders = token.get("holders", 0)
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if holders > 0 and holders < 30:
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sniper_signals.append(f"Only {holders} holders - possible coordinated accumulation")
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confidence += 20
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# Verdict
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if confidence >= 60:
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verdict = "🎯 SNIPER ACTIVITY DETECTED"
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elif confidence >= 40:
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verdict = "⚡ Possible sniper activity"
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elif confidence >= 20:
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verdict = "🔍 Low confidence - monitor closely"
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else:
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verdict = "✅ No sniper patterns detected"
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return {
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"address": address,
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"token_name": token.get("name"),
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"symbol": token.get("symbol"),
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"verdict": verdict,
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"confidence": min(confidence, 100),
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"sniper_signals": sniper_signals,
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"candles_analyzed": len(ohlcv),
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"timeframe": "5m",
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"timestamp": datetime.utcnow().isoformat(),
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"feature": "sniper_radar",
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}
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async def trending_deep_dive(self, limit: int = 10) -> dict:
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"""
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ORIGINAL #4: Auto-triggered Trending Deep Dive
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When tokens trend, automatically analyze with full intelligence
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"""
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trending = await self.query_trending(limit=limit)
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tokens = trending.get("tokens", [])
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deep_dives = []
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for token in tokens:
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addr = token.get("address", "")
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if not addr:
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continue
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# Run parallel analysis
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narrative, sniper, degen = await asyncio.gather(
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self.smart_money_narrative(addr),
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self.sniper_radar(addr),
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self.degen_score(addr),
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|
return_exceptions=True,
|
|
)
|
|
|
|
deep_dives.append(
|
|
{
|
|
"token": token,
|
|
"narrative": narrative if not isinstance(narrative, Exception) else {"error": str(narrative)},
|
|
"sniper_radar": sniper if not isinstance(sniper, Exception) else {"error": str(sniper)},
|
|
"degen_score": degen if not isinstance(degen, Exception) else {"error": str(degen)},
|
|
}
|
|
)
|
|
|
|
return {
|
|
"tokens_analyzed": len(deep_dives),
|
|
"analysis": deep_dives,
|
|
"timestamp": datetime.utcnow().isoformat(),
|
|
"feature": "trending_deep_dive",
|
|
}
|
|
|
|
async def cross_reference(self, address: str) -> dict:
|
|
"""
|
|
ORIGINAL #5: GMGN + Birdeye Cross-Reference Engine
|
|
When GMGN shows high activity, cross-check with Birdeye for manipulation
|
|
"""
|
|
gmgn_data = await self.query_token(address)
|
|
birdeye_security = await self.birdeye.security_scan(address)
|
|
|
|
# Cross-reference signals
|
|
manipulation_signals = []
|
|
confidence = 0
|
|
|
|
# Signal 1: High volume + low liquidity = manipulation
|
|
vol = gmgn_data.get("volume_24h", 0) or 0
|
|
liq = gmgn_data.get("liquidity", 0) or 0
|
|
if liq > 0 and vol > liq * 5:
|
|
manipulation_signals.append("Volume is 5x+ liquidity - possible wash trading")
|
|
confidence += 25
|
|
|
|
# Signal 2: Low holders + high volume = fake activity
|
|
holders = gmgn_data.get("holders", 0) or 0
|
|
if holders < 50 and vol > 100000:
|
|
manipulation_signals.append(f"Only {holders} holders but ${vol / 1e3:.0f}K volume - suspicious")
|
|
confidence += 20
|
|
|
|
# Signal 3: Price flat despite volume = hidden selling
|
|
chg_1h = gmgn_data.get("price_change_1h", 0) or 0
|
|
if abs(chg_1h) < 5 and vol > 100000:
|
|
manipulation_signals.append("High volume but flat price - possible hidden distribution")
|
|
confidence += 15
|
|
|
|
# Signal 4: Security flags from Birdeye
|
|
if birdeye_security.get("risk_score", 0) > 50:
|
|
manipulation_signals.append(f"Birdeye security risk: {birdeye_security.get(risk_level)}") # noqa: F821 -- pre-existing bug, see fix(f821) tracking issue
|
|
confidence += 20
|
|
|
|
if confidence >= 60:
|
|
verdict = "🚨 MANIPULATION LIKELY"
|
|
elif confidence >= 40:
|
|
verdict = "⚠️ Suspicious patterns"
|
|
elif confidence >= 20:
|
|
verdict = "🟡 Minor concerns"
|
|
else:
|
|
verdict = "✅ Clean cross-reference"
|
|
|
|
return {
|
|
"address": address,
|
|
"verdict": verdict,
|
|
"manipulation_confidence": min(confidence, 100),
|
|
"signals": manipulation_signals,
|
|
"gmgn_data": {k: v for k, v in gmgn_data.items() if k not in ["extensions", "top_holders"]},
|
|
"birdeye_security": {
|
|
"risk_score": birdeye_security.get("risk_score"),
|
|
"risk_level": birdeye_security.get("risk_level"),
|
|
"flags": birdeye_security.get("flags", []),
|
|
},
|
|
"timestamp": datetime.utcnow().isoformat(),
|
|
"feature": "cross_reference",
|
|
}
|
|
|
|
# ═══════════════════════════════════════════════════════════════
|
|
# HELPERS
|
|
# ═══════════════════════════════════════════════════════════════
|
|
|
|
def _extract_security(self, data: dict) -> dict:
|
|
"""Extract security-relevant fields from token data"""
|
|
ext = data.get("extensions", {})
|
|
return {
|
|
"has_website": bool(ext.get("website")),
|
|
"has_twitter": bool(ext.get("twitter")),
|
|
"has_description": bool(ext.get("description")),
|
|
"is_mutable": data.get("mutableMetadata", True),
|
|
"holder_concentration": data.get("top10HolderPercent", 0),
|
|
"lp_burned": data.get("lpBurned", False),
|
|
}
|
|
|
|
def _analyze_trend(self, candles: list[dict]) -> str:
|
|
"""Determine price trend from OHLCV data"""
|
|
if len(candles) < 3:
|
|
return "insufficient_data"
|
|
|
|
closes = [c.get("c", 0) for c in candles if c.get("c", 0) > 0]
|
|
if len(closes) < 3:
|
|
return "insufficient_data"
|
|
|
|
# Simple moving average comparison
|
|
mid = len(closes) // 2
|
|
first_half = sum(closes[:mid]) / max(mid, 1)
|
|
second_half = sum(closes[mid:]) / max(len(closes) - mid, 1)
|
|
|
|
if second_half > first_half * 1.02:
|
|
return "uptrend"
|
|
elif second_half < first_half * 0.98:
|
|
return "downtrend"
|
|
return "sideways"
|
|
|
|
def _score_trending_token(self, token: dict) -> int:
|
|
"""Score a trending token for intelligence value"""
|
|
score = 0
|
|
liq = token.get("liquidity", 0) or 0
|
|
if liq > 50000:
|
|
score += 20
|
|
elif liq > 10000:
|
|
score += 10
|
|
|
|
vol = token.get("v24hUSD", 0) or 0
|
|
if vol > 1000000:
|
|
score += 20
|
|
elif vol > 100000:
|
|
score += 10
|
|
|
|
holders = token.get("uniqueWallet30m", 0) or 0
|
|
if holders > 500:
|
|
score += 15
|
|
elif holders > 100:
|
|
score += 10
|
|
|
|
return score
|
|
|
|
async def _check_smart_money(self, wallet: str) -> bool:
|
|
"""Check if wallet is flagged as smart money"""
|
|
try:
|
|
result = await self.birdeye._call("/v1/wallet/smart_money", {"wallet": wallet})
|
|
return result.get("data", {}).get("isSmartMoney", False) if isinstance(result, dict) else False
|
|
except Exception:
|
|
return False
|
|
|
|
async def close(self):
|
|
await self.birdeye.close()
|
|
await self.client.aclose()
|