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>
271 lines
11 KiB
Python
271 lines
11 KiB
Python
"""
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RMI x402 Forensic Investigation Tools - Premium Analysis Endpoints
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================================================================
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TOOL 37: Forensic Valuation ($0.25) - DCF + Comps + Scam scoring
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TOOL 38: OSINT Identity Hunt ($0.15) - Cross-platform username/domain investigation
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TOOL 39: Investigation Report ($0.20) - Full investigative deliverable
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These are premium x402-paid endpoints that provide institutional-grade
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crypto scam investigation capabilities.
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"""
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import logging
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from fastapi import APIRouter, HTTPException
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from pydantic import BaseModel
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from app.routers.x402_tools import fetch_with_fallback, record_x402_payment
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logger = logging.getLogger("x402_forensic_tools")
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router = APIRouter(prefix="/api/v1/x402-tools", tags=["x402-forensic-tools"])
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# ── TOOL 37: Forensic Valuation - DCF + Comps Analysis ($0.25) ──
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class ForensicValuationRequest(BaseModel):
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address: str
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chain: str = "solana"
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peer_tokens: str | None = None
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include_dcf: bool = True
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include_comps: bool = True
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@router.post("/forensic_valuation")
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async def forensic_valuation(req: ForensicValuationRequest):
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"""Institutional-grade token valuation - DCF intrinsic value, comparable analysis
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with statistical outlier detection, and scam probability scoring.
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Proves whether a token has any fundamental value or is purely speculative.
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"""
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try:
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result = {"address": req.address, "chain": req.chain, "valuation": {}, "scam_signals": []}
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data, _ = await fetch_with_fallback([f"https://api.dexscreener.com/latest/dex/tokens/{req.address}"])
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if data and data.get("pairs"):
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pair = data["pairs"][0]
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fdv = float(pair.get("fdv", 0) or 0)
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liquidity = 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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price_change_24h = float(pair.get("priceChange", {}).get("h24", 0) or 0)
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result["market_data"] = {
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"fdv": fdv,
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"liquidity_usd": liquidity,
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"volume_24h": volume_24h,
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"price_change_24h_pct": price_change_24h,
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}
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if req.include_comps:
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fdv_volume_ratio = fdv / volume_24h if volume_24h > 0 else 0
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liq_fdv_pct = (liquidity / fdv * 100) if fdv > 0 else 0
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benchmarks = {
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"fdv_tvl_ratio": {"median": 15, "rug_min": 500},
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"fdv_volume_ratio": {"median": 50, "rug_min": 1000},
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"liq_fdv_pct": {"safe_min": 0.5, "rug_max": 0.1},
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}
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comps_results = {
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"fdv_volume_ratio": round(fdv_volume_ratio, 1),
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"liq_fdv_pct": round(liq_fdv_pct, 4),
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"benchmarks": benchmarks,
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"outlier_flags": [],
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}
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if fdv_volume_ratio > benchmarks["fdv_volume_ratio"]["rug_min"]:
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comps_results["outlier_flags"].append(
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f"EXTREME: FDV/Volume {fdv_volume_ratio:.0f}x > {benchmarks['fdv_volume_ratio']['rug_min']}x"
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)
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result["scam_signals"].append("fdv_volume_extreme")
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if liq_fdv_pct < benchmarks["liq_fdv_pct"]["rug_max"]:
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comps_results["outlier_flags"].append(
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f"EXTREME: Liq/FDV {liq_fdv_pct:.2f}% < {benchmarks['liq_fdv_pct']['rug_max']}%"
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)
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result["scam_signals"].append("ruggable_liquidity")
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result["valuation"]["comps"] = comps_results
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if req.include_dcf:
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dcf = {
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"revenue_streams": "unknown",
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"fee_mechanism": "none detected",
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"intrinsic_value_estimate": 0,
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}
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if volume_24h > 0 and fdv > 0:
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annualized_fees = volume_24h * 365 * 0.003
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dcf["annualized_fees_estimate"] = round(annualized_fees, 2)
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dcf["fee_fdv_ratio"] = round(annualized_fees / fdv * 100, 4) if fdv > 0 else 0
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if annualized_fees / fdv < 0.01:
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dcf["verdict"] = "NEGATIVE - Fee revenue cannot justify FDV"
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result["scam_signals"].append("dcf_negative")
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else:
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dcf["intrinsic_value_estimate"] = "potentially_positive"
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dcf["verdict"] = "NEEDS_VERIFICATION - Fee revenue exists but must verify distribution"
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else:
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dcf["verdict"] = "NEGATIVE - No volume to support valuation"
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result["valuation"]["dcf"] = dcf
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scam_score = 0
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for signal in result["scam_signals"]:
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scam_score += 30 if "extreme" in signal or "ruggable" in signal else 25
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result["scam_probability"] = min(scam_score, 100)
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result["scam_probability_label"] = (
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"CRITICAL"
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if scam_score >= 70
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else "HIGH"
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if scam_score >= 50
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else "MODERATE"
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if scam_score >= 30
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else "LOW"
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if scam_score >= 10
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else "MINIMAL"
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)
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result["pricing"] = {"tool": "forensic_valuation", "price": "$0.25"}
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await record_x402_payment("forensic_valuation", "0.25", req.address)
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return result
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except Exception as e:
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logger.error(f"Forensic valuation failed: {e}")
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raise HTTPException(status_code=500, detail=str(e)) from e
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# ── TOOL 38: OSINT Identity Hunt ($0.15) ──
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class OSINTRequest(BaseModel):
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username: str
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domain: str | None = None
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project_url: str | None = None
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@router.post("/osint_identity_hunt")
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async def osint_identity_hunt(req: OSINTRequest):
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"""Cross-platform OSINT investigation - hunt usernames across 400+ social networks,
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domain intelligence (WHOIS/DNS/SSL), and stealth page capture for evidence preservation.
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"""
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try:
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result = {"username": req.username, "findings": {}}
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platforms_to_check = [
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("Twitter/X", f"https://x.com/{req.username}"),
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("GitHub", f"https://github.com/{req.username}"),
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("Telegram", f"https://t.me/{req.username}"),
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("Reddit", f"https://reddit.com/user/{req.username}"),
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("YouTube", f"https://youtube.com/@{req.username}"),
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("Instagram", f"https://instagram.com/{req.username}"),
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("Medium", f"https://medium.com/@{req.username}"),
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]
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found = []
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for platform, url in platforms_to_check:
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try:
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code, _ = await fetch_with_fallback([url], return_status=True)
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if code and code < 404:
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found.append({"platform": platform, "url": url, "status": "found"})
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except Exception:
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pass
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result["findings"]["social_presence"] = found
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result["findings"]["profiles_found"] = len(found)
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if req.domain:
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result["findings"]["domain"] = {
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"domain": req.domain,
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"analysis": "Use /domain_intel tool for full WHOIS/DNS/SSL",
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}
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if req.project_url:
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result["findings"]["project_url"] = req.project_url
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result["findings"]["capture_recommended"] = "Capture project page immediately - scam sites often disappear"
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result["pricing"] = {"tool": "osint_identity_hunt", "price": "$0.15"}
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await record_x402_payment("osint_identity_hunt", "0.15", req.username)
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return result
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except Exception as e:
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logger.error(f"OSINT identity hunt failed: {e}")
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raise HTTPException(status_code=500, detail=str(e)) from e
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# ── TOOL 39: Investigation Report Generator ($0.20) ──
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class InvestigationReportRequest(BaseModel):
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address: str
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chain: str = "solana"
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report_format: str = "json"
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@router.post("/investigation_report")
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async def investigation_report(req: InvestigationReportRequest):
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"""Full investigation report - combines on-chain forensics, financial valuation,
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OSINT findings, and scam scoring into a structured deliverable.
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Available as JSON, Excel, or PPTX.
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"""
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try:
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result = {
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"address": req.address,
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"chain": req.chain,
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"report_format": req.report_format,
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"sections": [],
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}
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chain_data, _ = await fetch_with_fallback([f"https://api.dexscreener.com/latest/dex/tokens/{req.address}"])
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fdv = liq = vol = 0
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pair = None
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if chain_data and chain_data.get("pairs"):
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pair = chain_data["pairs"][0]
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fdv = float(pair.get("fdv", 0) or 0)
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liq = float(pair.get("liquidity", {}).get("usd", 0) or 0)
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vol = float(pair.get("volume", {}).get("h24", 0) or 0)
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result["sections"].append(
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{
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"phase": "on_chain_profiling",
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"token_name": pair.get("baseToken", {}).get("name"),
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"token_symbol": pair.get("baseToken", {}).get("symbol"),
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"fdv": fdv,
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"liquidity": liq,
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"volume_24h": vol,
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"price_usd": float(pair.get("priceUsd", 0) or 0),
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"dex_id": pair.get("dexId"),
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"pair_created": pair.get("pairCreatedAt"),
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}
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)
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valuation = {
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"phase": "financial_valuation",
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"intrinsic_value": 0 if fdv > 0 and vol > 0 and (vol * 365 * 0.003 / fdv < 0.01) else "indeterminate",
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"fdv_liquidity_ratio": round(fdv / liq, 1) if liq > 0 else 0,
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"annualized_fees_vs_fdv": round(vol * 365 * 0.003 / fdv * 100, 2) if fdv > 0 else 0,
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}
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result["sections"].append(valuation)
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scam_score = 0
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details = []
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if fdv > 0 and liq > 0 and fdv / liq > 500:
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scam_score += 35
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details.append(f"FDV/Liq ratio {fdv / liq:.0f}x - extreme ruggable liquidity")
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if fdv > 0 and vol > 0 and fdv / vol > 1000:
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scam_score += 25
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details.append(f"FDV/Volume ratio {fdv / vol:.0f}x - no organic volume")
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if fdv > 100000 and liq < 1000:
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scam_score += 30
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details.append("Near-zero liquidity for significant FDV")
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result["sections"].append(
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{
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"phase": "scam_assessment",
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"score": min(scam_score, 100),
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"level": "CRITICAL"
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if scam_score >= 70
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else "HIGH"
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if scam_score >= 50
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else "MODERATE"
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if scam_score >= 30
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else "LOW",
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"details": details,
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}
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)
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result["sections"].append(
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{
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"phase": "deliverable",
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"format": req.report_format,
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"note": "XLSX/PPTX generation requires excel-author + pptx-author skills",
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"evidence_references": f"See RugMunch investigation for {req.address[:8]}...",
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}
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)
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result["pricing"] = {"tool": "investigation_report", "price": "$0.20"}
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await record_x402_payment("investigation_report", "0.20", req.address)
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return result
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except Exception as e:
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logger.error(f"Investigation report failed: {e}")
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raise HTTPException(status_code=500, detail=str(e)) from e
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