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https://github.com/BigBodyCobain/Shadowbroker.git
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BigBodyCobain
af9b3d08cc
Add Telegram OSINT with hourly incremental t.me scraping, metro geocoding separate from news centroids, threat-intercept popup UI with inline media, and HTML markers above alert boxes so pins stay clickable. Expose GFW_API_TOKEN in onboarding and Settings Maritime; harden GFW/CCTV/geo fetchers. Port Osiris- derived recon, SCM, entity graph, malware/cyber feeds, sanctions, and submarine cable layers with tests and documentation. Co-authored-by: Cursor <cursoragent@cursor.com>
95 lines
4.4 KiB
Python
95 lines
4.4 KiB
Python
"""Country risk index (static scores + USGS quake enrichment)."""
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from __future__ import annotations
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from datetime import datetime, timezone
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from typing import Any
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from zoneinfo import ZoneInfo
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from services.network_utils import fetch_with_curl
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RISK_FACTORS: dict[str, dict[str, Any]] = {
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"UA": {"base": 85, "tags": ["active_conflict", "infrastructure_damage"]},
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"RU": {"base": 72, "tags": ["sanctions", "military_mobilization"]},
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"IL": {"base": 78, "tags": ["active_conflict", "regional_instability"]},
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"PS": {"base": 90, "tags": ["active_conflict", "humanitarian_crisis"]},
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"SY": {"base": 82, "tags": ["post_conflict", "infrastructure_damage"]},
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"YE": {"base": 88, "tags": ["active_conflict", "humanitarian_crisis"]},
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"MM": {"base": 76, "tags": ["civil_unrest", "military_junta"]},
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"SD": {"base": 84, "tags": ["active_conflict", "humanitarian_crisis"]},
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"AF": {"base": 80, "tags": ["post_conflict", "governance_collapse"]},
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"KP": {"base": 70, "tags": ["nuclear_risk", "isolation"]},
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"IR": {"base": 68, "tags": ["sanctions", "nuclear_program", "regional_proxy"]},
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"CN": {"base": 35, "tags": ["strategic_competition", "taiwan_tensions"]},
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"TW": {"base": 45, "tags": ["invasion_risk", "semiconductor_dependency"]},
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"VE": {"base": 60, "tags": ["economic_collapse", "political_instability"]},
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"HT": {"base": 85, "tags": ["gang_violence", "governance_collapse"]},
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"LB": {"base": 65, "tags": ["economic_crisis", "political_deadlock"]},
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"PK": {"base": 55, "tags": ["terrorism", "political_instability"]},
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"SO": {"base": 82, "tags": ["terrorism", "state_fragility"]},
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"LY": {"base": 72, "tags": ["divided_government", "militia_control"]},
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"ET": {"base": 62, "tags": ["ethnic_tensions", "regional_conflicts"]},
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}
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EXCHANGES = [
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{"name": "NYSE", "tz": "America/New_York", "open": 9.5, "close": 16, "country": "US"},
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{"name": "NASDAQ", "tz": "America/New_York", "open": 9.5, "close": 16, "country": "US"},
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{"name": "LSE", "tz": "Europe/London", "open": 8, "close": 16.5, "country": "GB"},
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{"name": "TSE", "tz": "Asia/Tokyo", "open": 9, "close": 15, "country": "JP"},
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{"name": "SSE", "tz": "Asia/Shanghai", "open": 9.5, "close": 15, "country": "CN"},
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{"name": "HKEX", "tz": "Asia/Hong_Kong", "open": 9.5, "close": 16, "country": "HK"},
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{"name": "FRA", "tz": "Europe/Berlin", "open": 8, "close": 20, "country": "DE"},
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{"name": "TSX", "tz": "America/Toronto", "open": 9.5, "close": 16, "country": "CA"},
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{"name": "MOEX", "tz": "Europe/Moscow", "open": 10, "close": 18.5, "country": "RU"},
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]
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def _exchange_open(ex: dict[str, Any]) -> bool:
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try:
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now = datetime.now(ZoneInfo(ex["tz"]))
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if now.weekday() >= 5:
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return False
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decimal = now.hour + now.minute / 60
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return ex["open"] <= decimal < ex["close"]
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except Exception:
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return False
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def build_country_risk_payload() -> dict[str, Any]:
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quake_risks: dict[str, float] = {}
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try:
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resp = fetch_with_curl(
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"https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/4.5_day.geojson",
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timeout=5,
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)
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if resp.status_code == 200:
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for f in resp.json().get("features") or []:
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place = (f.get("properties") or {}).get("place") or ""
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mag = (f.get("properties") or {}).get("mag") or 0
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for code in RISK_FACTORS:
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if code.lower() in place.lower():
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quake_risks[code] = quake_risks.get(code, 0) + mag
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except Exception:
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pass
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countries = []
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for code, data in RISK_FACTORS.items():
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base = data["base"]
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score = min(100, base + quake_risks.get(code, 0))
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countries.append(
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{
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"code": code,
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"risk_score": score,
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"risk_level": "CRITICAL" if base >= 80 else "HIGH" if base >= 60 else "ELEVATED" if base >= 40 else "LOW",
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"tags": data["tags"],
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}
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)
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countries.sort(key=lambda c: c["risk_score"], reverse=True)
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exchanges = [{"name": e["name"], "country": e["country"], "open": _exchange_open(e)} for e in EXCHANGES]
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return {
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"countries": countries,
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"exchanges": exchanges,
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"open_exchanges": sum(1 for e in exchanges if e["open"]),
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"total_exchanges": len(exchanges),
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"timestamp": datetime.now(timezone.utc).isoformat(),
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}
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