272 lines
9.0 KiB
Markdown
272 lines
9.0 KiB
Markdown
# 🤖 Autonomous Trading Intelligence System
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## System Architecture Overview
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ AUTONOMOUS TRADING BRAIN │
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├─────────────────────────────────────────────────────────────────┤
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│ │
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│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
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│ │ IBKR Live │ │ Market Data │ │ Alternative │ │
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│ │ Integration │ │ Aggregator │ │ Data Sources │ │
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│ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │
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│ │ │ │ │
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│ └──────────────────┼──────────────────┘ │
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│ │ │
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│ ┌──────▼───────┐ │
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│ │ AI BRAIN │ │
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│ │ (TradingAgents│ │
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│ │ + Custom) │ │
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│ └──────┬───────┘ │
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│ │ │
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│ ┌─────────────────┼─────────────────┐ │
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│ │ │ │ │
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│ ┌────▼────┐ ┌─────▼─────┐ ┌─────▼─────┐ │
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│ │Position │ │ Risk Mgmt │ │ Alert │ │
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│ │Manager │ │ Engine │ │ System │ │
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│ └─────────┘ └───────────┘ └───────────┘ │
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│ │
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└─────────────────────────────────────────────────────────────────┘
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```
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## 1. Core Components
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### A. IBKR Live Integration Module
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```python
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# Key Features:
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- Real-time portfolio sync via IB Gateway/TWS API
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- Position tracking (shares, P&L, cost basis)
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- Order execution capability (with safety controls)
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- Account balance and margin monitoring
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- Historical trade analysis
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```
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### B. Data Aggregation Pipeline
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```python
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DATA_SOURCES = {
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"market_data": {
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"real_time": ["IEX Cloud", "Polygon.io", "AlphaVantage Premium"],
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"historical": ["yfinance", "IBKR API"]
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},
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"alternative_data": {
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"congressional_trades": ["CapitolTrades API", "QuiverQuant"],
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"insider_trading": ["SEC EDGAR", "OpenInsider API"],
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"social_sentiment": ["Reddit API", "Twitter/X API", "StockTwits"],
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"news": ["NewsAPI", "Benzinga", "Bloomberg Terminal"],
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"earnings": ["AlphaVantage", "Yahoo Finance", "Earnings Whispers"],
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"options_flow": ["FlowAlgo", "Unusual Whales API"],
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"institutional": ["13F filings", "WhaleWisdom API"]
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},
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"economic_data": {
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"fed": ["FRED API"],
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"macro": ["TradingEconomics", "World Bank API"]
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}
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}
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```
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### C. Autonomous Monitoring System
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```python
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MONITORING_INTERVALS = {
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"portfolio_health": "5 minutes",
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"market_movers": "15 minutes",
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"news_scan": "30 minutes",
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"congressional_trades": "1 hour",
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"earnings_calendar": "daily",
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"technical_analysis": "1 hour",
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"risk_assessment": "30 minutes"
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}
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```
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## 2. Implementation Plan
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### Phase 1: Foundation (Week 1-2)
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- [ ] Set up IBKR API connection using ib_insync
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- [ ] Create database (PostgreSQL/TimescaleDB) for historical data
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- [ ] Build basic portfolio monitoring dashboard
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- [ ] Implement core data fetching modules
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### Phase 2: Intelligence Layer (Week 3-4)
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- [ ] Integrate TradingAgents with continuous monitoring
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- [ ] Add custom AI agents for specific strategies
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- [ ] Implement pattern recognition system
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- [ ] Create backtesting framework
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### Phase 3: Alerting & Automation (Week 5-6)
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- [ ] Build multi-channel alert system (Discord/Telegram/Email)
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- [ ] Create trading signal generator
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- [ ] Implement paper trading mode
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- [ ] Add risk management rules
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### Phase 4: Advanced Features (Week 7-8)
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- [ ] Congressional trade mirroring alerts
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- [ ] Earnings play recommendations
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- [ ] Options strategy suggestions
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- [ ] Portfolio rebalancing recommendations
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## 3. Key Modules to Build
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### A. Portfolio Monitor (`portfolio_monitor.py`)
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```python
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class PortfolioMonitor:
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def __init__(self):
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self.ibkr_client = IBKRClient()
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self.positions = {}
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self.alerts = []
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async def sync_portfolio(self):
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"""Sync with IBKR every 5 minutes"""
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async def calculate_metrics(self):
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"""Calculate P&L, exposure, risk metrics"""
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async def generate_recommendations(self):
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"""AI-powered buy/sell recommendations"""
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```
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### B. Market Scanner (`market_scanner.py`)
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```python
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class MarketScanner:
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def __init__(self):
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self.scanners = {
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"momentum": MomentumScanner(),
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"value": ValueScanner(),
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"breakout": BreakoutScanner(),
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"insider": InsiderScanner(),
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"congressional": CongressionalScanner()
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}
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async def scan_opportunities(self):
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"""Continuous market scanning"""
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async def rank_opportunities(self):
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"""AI-powered opportunity ranking"""
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```
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### C. Alert Engine (`alert_engine.py`)
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```python
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class AlertEngine:
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def __init__(self):
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self.channels = {
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"discord": DiscordBot(),
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"telegram": TelegramBot(),
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"email": EmailNotifier(),
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"sms": TwilioSMS()
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}
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async def send_alert(self, alert_type, message, priority):
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"""Multi-channel alert distribution"""
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```
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## 4. Alert Types & Actions
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### 🚨 CRITICAL ALERTS (Immediate Action)
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- Stop loss triggers
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- Margin calls
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- Extreme volatility in holdings
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- Major news affecting positions
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### 📊 TRADING SIGNALS
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```
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FORMAT:
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━━━━━━━━━━━━━━━━━━━━━━━
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🎯 ACTION: BUY/SELL
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📈 TICKER: NVDA
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💰 PRICE: $450.25
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🎯 TARGET: $465.00
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🛑 STOP: $445.00
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📊 CONFIDENCE: 85%
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📝 REASON: Congressional buying + Earnings beat
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━━━━━━━━━━━━━━━━━━━━━━━
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```
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### 🔍 OPPORTUNITY ALERTS
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- Congressional trades matching your watchlist
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- Unusual options activity
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- Insider buying in your sectors
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- Earnings surprises
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- Technical breakouts
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## 5. Database Schema
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```sql
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-- Portfolio tracking
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CREATE TABLE positions (
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id SERIAL PRIMARY KEY,
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ticker VARCHAR(10),
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shares INTEGER,
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avg_cost DECIMAL,
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current_price DECIMAL,
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last_updated TIMESTAMP
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);
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-- Trade recommendations
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CREATE TABLE recommendations (
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id SERIAL PRIMARY KEY,
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ticker VARCHAR(10),
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action VARCHAR(10),
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price_target DECIMAL,
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stop_loss DECIMAL,
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confidence DECIMAL,
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reasoning TEXT,
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created_at TIMESTAMP
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);
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-- Congressional trades
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CREATE TABLE congressional_trades (
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id SERIAL PRIMARY KEY,
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politician VARCHAR(100),
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ticker VARCHAR(10),
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action VARCHAR(10),
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amount_range VARCHAR(50),
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filed_date DATE
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);
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```
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## 6. Deployment Strategy
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### Local Server Setup
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```bash
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# Docker Compose for all services
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docker-compose up -d postgres redis rabbitmq
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# Main application
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python autonomous_trader.py --mode=production
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# Background workers
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celery -A tasks worker --loglevel=info
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celery -A tasks beat --loglevel=info
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```
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### Cloud Deployment (AWS/GCP)
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```yaml
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services:
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- trading_brain: EC2/Compute Engine
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- database: RDS/Cloud SQL
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- message_queue: SQS/Pub-Sub
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- monitoring: CloudWatch/Stackdriver
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- alerts: Lambda/Cloud Functions
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```
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## 7. Safety Features
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### Risk Controls
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```python
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RISK_LIMITS = {
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"max_position_size": 0.20, # 20% of portfolio
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"max_daily_loss": 0.05, # 5% daily loss limit
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"max_trades_per_day": 10,
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"require_confirmation": True, # For trades > $10k
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"paper_trade_first": True # Test mode
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}
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```
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### Fail-Safes
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- Circuit breakers for extreme market conditions
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- Automatic position hedging
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- Emergency liquidation protocols
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- Manual override capabilities
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## 8. Quick Start Implementation
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Let me create the initial autonomous monitoring script: |