Investment Intelligence Platform

Leveraging Data Engineering + Finance + Biotech expertise for systematic alpha generation

A complete, production-ready platform that detects investment opportunities through automated signal detection across clinical trials, patent expirations, and insider activity.

🎯 Quick Start

# 1. Clone or navigate to the project
cd /Users/waiyang/Desktop/repo/dreamers-v2

# 2. Set up databases
createdb clinical_trials patent_intelligence insider_signals

# 3. Load schemas
psql clinical_trials < clinical_trial_signals/database/schema.sql
psql patent_intelligence < patent_intelligence/sql/schema.sql
psql insider_signals < insider_hiring_signals/schema.sql

# 4. Run the dashboard (demo mode works without databases)
cd dashboard
pip install -r requirements.txt
streamlit run app.py

Access dashboard at: http://localhost:8501

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📊 Three Core Systems

1. Clinical Trial Signal Detection

Goal: Detect trial outcomes 2-4 weeks before announcements

Edge: Protocol amendments, enrollment patterns, patent filings signal trial success/failure

ROI Target: 5-10x on options plays

📖 Full Specs | 📁 Code

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2. Patent/IP Intelligence

Goal: Identify patent cliff events 6-18 months in advance

Edge: Predict generic competition timing with 90%+ accuracy

ROI Target: 20-40% on pair trades (long generic, short branded)

📖 Full Specs | 📁 Code

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3. Insider Activity + Hiring Signals

Goal: Detect company trajectory through behavioral signals

Edge: Combine Form 4, 13F, and hiring data for directional conviction

ROI Target: 15-25% on 3-6 month positions

📖 Full Specs | 📁 Code

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🎛️ Integrated Dashboard

Streamlit-based dashboard combining all three systems:

• Combined scoring (weighted across all signals)
• Real-time data from all pipelines
• Interactive charts and filterable tables
• Watchlist management
• Configurable alerts

📁 Code | [🚀 Demo Mode Available]

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⚙️ Orchestration & Automation

Automated scheduling using Apache Airflow (or Prefect):

Pipeline	Frequency	Time (ET)
Clinical Trial Signals	Daily	6:00 PM
Patent/IP Intelligence	Weekly	Monday 8:00 AM
Form 4 Insider Trades	Every 30 min	Market hours
13F Holdings	Quarterly	45 days post-quarter

Multi-channel alerts: Email, Slack, SMS

📁 Code | [🐳 Docker Deployment Available]

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💰 Budget & Costs

Initial Investment

• Total Budget: $5,000
• Infrastructure Setup: ~$1,000
• Remaining for Trading: ~$4,000

Monthly Operating Costs

• Local Deployment: $670/month
• Cloud Deployment (AWS): $600-800/month

Includes: Cloud infrastructure, AI APIs, LinkedIn access, PACER account, email/SMS

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📈 Expected Returns

Based on signal accuracy and market volatility:

Strategy	Allocation	Expected Return	Risk
Clinical trial options	30%	50-100%	High
Patent cliff pairs	40%	20-40%	Medium
Insider-driven longs	30%	15-25%	Low-Med

Target Blended Return: 30-50% annually

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🏗️ Project Structure

dreamers-v2/
├── README.md                              # You are here
├── PROJECT_SUMMARY.md                     # Complete documentation
│
├── clinical_trial_signals/                # System 1: Trial signals
│   ├── main.py
│   ├── demo.py
│   └── database/, scrapers/, scoring/, alerts/
│
├── patent_intelligence/                   # System 2: Patent cliffs
│   ├── src/pipeline.py
│   └── extractors/, transformers/, loaders/
│
├── insider_hiring_signals/                # System 3: Insider/hiring
│   ├── main.py
│   └── scrapers/, models/, reports/
│
├── dashboard/                             # Integrated UI
│   ├── app.py
│   └── components/, pages/, utils/
│
└── orchestration/                         # Automation
    ├── dags/                              # Airflow DAGs
    ├── docker/                            # Local deployment
    └── cloud/                             # AWS/GCP deployment

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🚀 Usage Examples

Run Individual Systems

# Clinical Trial Signals
cd clinical_trial_signals
python main.py --full

# Patent Intelligence
cd patent_intelligence
python -m src.pipeline --top-drugs 50

# Insider/Hiring Signals
cd insider_hiring_signals
python main.py scrape --all
python main.py score --ticker MRNA

Deploy with Docker

cd orchestration/docker
docker-compose up -d

# Access services
# Airflow: http://localhost:8080 (admin/admin)
# Grafana: http://localhost:3000 (admin/admin)
# Health: http://localhost:9091/health

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📚 Documentation

• PROJECT_SUMMARY.md - Complete guide with architecture, strategy, and monetization
• SPECS_1_Clinical_Trial_Signals.md - Clinical trial system specs
• SPECS_2_Patent_IP_Intelligence.md - Patent/IP system specs
• SPECS_3_Insider_Hiring_Signals.md - Insider/hiring system specs
• orchestration/COST_ESTIMATES.md - Detailed cost breakdown

Each system folder also has its own README with specific setup instructions.

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✅ Next Steps

Week 1: Validation

• Run all scrapers and verify data quality
• Review signal reports for accuracy
• Backtest scoring models on historical data

Week 2: Paper Trading

• Track 5-10 hypothetical trades
• Monitor signal accuracy
• Refine weights and thresholds

Week 3: Go Live

• Start with small positions ($500 each)
• Focus on highest-confidence signals (score >= 7)
• Implement strict stop losses

Month 2-3: Scale

• Increase position sizes gradually
• Expand watchlist
• Consider automated execution

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🎓 Why This Works

This platform provides an unfair advantage because:

1. Rare Skill Combination: Data engineering + finance + biotech expertise is extremely uncommon
2. Information Asymmetry: Automated detection of public signals that others miss
3. Systematic Edge: Removes emotion from investment decisions
4. Scalable: Can manage larger capital with same infrastructure
5. Defensible: Can't be easily replicated by general AI consultants

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💡 Future Enhancements

Additional Systems (when budget allows):

• HEOR model validation
• Clinical trial cost benchmarking
• FDA submission QC service
• Biotech M&A due diligence

Advanced Features:

• Automated trade execution (via Interactive Brokers API)
• Machine learning models for signal weighting
• Real-time news sentiment analysis
• Options strategy optimizer

Monetization (after proven track record):

• Raise a biotech-focused fund
• Sell research reports/newsletter
• License platform to hedge funds
• Offer consulting services

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🔒 Risk Management

Position Sizing

• Max 10% per trade
• Max 40% per system
• 20% cash reserve

Stop Losses

• Clinical trials: 50% (options decay)
• Patent cliffs: 15% (thesis break)
• Insider plays: 10% (momentum loss)

Signal Validation

• Minimum confidence: 0.6
• Prefer 3+ clustered signals
• Always review contrarian signals

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📞 Support

Each system has detailed troubleshooting in its README. Common issues:

Database errors: Check PostgreSQL is running (pg_isready) API limits: Review rate limiting in configs Missing data: Check scraper logs in logs/ folders

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📜 License

This is proprietary investment software. Unauthorized distribution is prohibited.

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🏆 Success Metrics

Track these KPIs weekly:

System Performance:

• Data freshness < 24 hours
• Uptime > 99%
• Signal accuracy > 70%

Investment Performance:

• Win rate > 60%
• Average return > 25%
• Sharpe ratio > 1.5
• Max drawdown < 20%

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Built with: Python, PostgreSQL, Streamlit, Apache Airflow, Docker

Powered by: AI (Claude Sonnet, GPT-4), SEC EDGAR API, ClinicalTrials.gov API, USPTO API

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Let's generate alpha! 📈