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Deploy Aureon OS (deploy/selfhost stack: nginx edge + fusion API x2 + console + HNC organ + redis + postgres; paper mode) (part 19)
da0ce7b verified | #!/usr/bin/env python3 | |
| """Quick display of firm intelligence database""" | |
| from aureon_baton_link import link_system as _baton_link; _baton_link(__name__) | |
| import sys | |
| sys.path.insert(0, '/workspaces/aureon-trading') | |
| from aureon_bot_intelligence_profiler import TRADING_FIRM_SIGNATURES | |
| print("=" * 90) | |
| print("🧠 BOT INTELLIGENCE PROFILER - FIRM & GEO DECODER DATABASE") | |
| print("=" * 90) | |
| print() | |
| # Count by region | |
| regions = {} | |
| for firm_id, firm_data in TRADING_FIRM_SIGNATURES.items(): | |
| country = firm_data['country'] | |
| if country not in regions: | |
| regions[country] = [] | |
| regions[country].append(firm_data) | |
| # Print summary | |
| print(f"📊 TOTAL FIRMS TRACKED: {len(TRADING_FIRM_SIGNATURES)}") | |
| print(f"🌍 COUNTRIES COVERED: {len(regions)}") | |
| print() | |
| # Show by region | |
| region_groups = { | |
| "🇺🇸 USA - Wall Street Giants": ["USA"], | |
| "🇬🇧 Europe - London & Amsterdam": ["UK", "Netherlands"], | |
| "🇯🇵 Asia-Pacific - Tokyo, Singapore, Hong Kong": ["Japan", "Singapore", "China", "China/Singapore"], | |
| "💀 Ghost Firms (Defunct)": ["Bahamas", "Singapore/BVI"] | |
| } | |
| for region_name, countries in region_groups.items(): | |
| firms_in_region = [] | |
| for country in countries: | |
| if country in regions: | |
| firms_in_region.extend(regions[country]) | |
| if firms_in_region: | |
| print(f"\n{region_name} ({len(firms_in_region)} firms)") | |
| print("-" * 85) | |
| for firm in firms_in_region: | |
| animal = firm.get('animal', '❓') | |
| capital = firm.get('estimated_capital', 0) | |
| if capital >= 1_000_000_000_000: # Trillion | |
| capital_str = f"${capital/1_000_000_000_000:.1f}T" | |
| elif capital >= 1_000_000_000: # Billion | |
| capital_str = f"${capital/1_000_000_000:.0f}B" | |
| elif capital > 0: | |
| capital_str = f"${capital/1_000_000:.0f}M" | |
| else: | |
| capital_str = "DEFUNCT" | |
| name = firm['name'] | |
| location = firm['hq_location'] | |
| strategies = ", ".join(firm.get('known_strategies', [])[:2]) | |
| print(f" {animal} {name:<35} | {location:<18} | {capital_str:>8} | {strategies}") | |
| # Show capabilities | |
| print("\n" * 2) | |
| print("🔍 GEOGRAPHIC DECODER CAPABILITIES") | |
| print("=" * 90) | |
| print() | |
| print("✅ LOCATION INTELLIGENCE:") | |
| print(" • HQ Tracking: City-level precision for 30+ global trading firms") | |
| print(" • Time Zone Analysis: Multi-timezone operation patterns (US/EU/Asia)") | |
| print(" • Region Classification: Americas / Europe / Asia-Pacific / 24/7 Global") | |
| print() | |
| print("✅ BOT ATTRIBUTION ENGINE:") | |
| print(" • HFT Fingerprinting: 1-600Hz frequency pattern matching") | |
| print(" • Latency Profiling: Ultra-low (<2ms) / Low (<10ms) / Medium / High") | |
| print(" • Order Consistency: 0.60-0.97 consistency scoring") | |
| print(" • Strategy Detection: Market making, arbitrage, HFT, momentum, etc.") | |
| print() | |
| print("✅ HIERARCHY MAPPING:") | |
| print(" • Megalodon: $50M+ per trade (Firm Leaders)") | |
| print(" • Whale: $1M-50M per trade (Coordinators)") | |
| print(" • Shark: $100K-1M per trade (Squad Leaders)") | |
| print(" • Minnow: <$100K per trade (Worker Bots)") | |
| print() | |
| print("✅ REAL-TIME INTELLIGENCE:") | |
| print(" • Live Ownership Attribution: Which firm owns which bot") | |
| print(" • Symbol Activity Tracking: What each firm is trading") | |
| print(" • Volume Monitoring: Estimates market impact by firm") | |
| print(" • Strategy Classification: 12+ strategy types detected") | |
| print() | |
| print("🌍 GEOGRAPHIC DECODE EXAMPLES:") | |
| print("-" * 85) | |
| print() | |
| print("📍 Bot detected at 9:30 AM US/Eastern, 150Hz frequency, ultra-low latency:") | |
| print(" → Region: Americas") | |
| print(" → Country: USA") | |
| print(" → Timezone: US/Eastern") | |
| print(" → Likely Firms: 🦈 Jane Street, 🦁 Citadel, 🐆 Jump Trading") | |
| print(" → Strategy: HFT Market Making") | |
| print() | |
| print("📍 Bot detected at 9:00 AM Europe/London, 80-300Hz, crypto focus:") | |
| print(" → Region: Europe") | |
| print(" → Country: UK") | |
| print(" → Timezone: Europe/London") | |
| print(" → Likely Firms: ❄️ Wintermute, 🤖 B2C2, 🐙 Optiver") | |
| print(" → Strategy: Crypto Market Making") | |
| print() | |
| print("📍 Bot detected at 9:00 AM Asia/Singapore, 30-150Hz, altcoin focus:") | |
| print(" → Region: Asia-Pacific") | |
| print(" → Country: Singapore") | |
| print(" → Timezone: Asia/Singapore") | |
| print(" → Likely Firms: 🐯 Amber Group, 🦂 QCP Capital, 🦁 Temasek") | |
| print(" → Strategy: Crypto Trading / Structured Products") | |
| print() | |
| print("=" * 90) | |
| print("🎯 CURRENT DETECTIONS FROM BOT SHAPE SCANNER:") | |
| print("=" * 90) | |
| print() | |
| print("BTCUSDT: 4,244 activities → ACCUMULATION_BOT 🤖") | |
| print("ETHUSDT: 3,415 activities → ACCUMULATION_BOT 🤖") | |
| print("SOLUSDT: 686 activities → ACCUMULATION_BOT 🤖") | |
| print("ADAUSDT: 194 activities → ACCUMULATION_BOT 🤖") | |
| print() | |
| print("🔍 Analysis: Detecting coordinated accumulation patterns across major crypto pairs") | |
| print("🏢 Likely Firms: Wintermute, Cumberland, B2C2 (crypto market makers)") | |
| print("=" * 90) | |