HedgeFund: AI-Driven Stock Trading Platform Specification

Executive Summary

HedgeFund is a comprehensive AI-driven stock trading platform designed for personal algorithmic trading. This specification outlines the technical architecture, data sources, regulatory considerations, and implementation roadmap for a production-ready trading system that combines real-time market data, advanced AI/ML strategies, and automated execution capabilities.

1. Real-Time Market Data Analysis

Data Provider Comparison

Polygon.io (Recommended for Production)

Strengths: High-quality normalized data, WebSocket streaming, comprehensive options chains
Pricing: Free tier (5 calls/min), paid plans from $199/month for serious usage
Features: Real-time trades/quotes, Level 2 data, options chains, news sentiment
Latency: Excellent for retail algorithmic trading
Use Case: Primary data source for live trading

Alpaca Markets Data (Recommended for Development)

Strengths: Free real-time data for subscribers, excellent Python SDK, paper trading
Pricing: Free basic tier, $100/month for professional data
Features: WebSocket streaming, real-time bars/quotes/trades, commission-free trading
Integration: Native trading API integration
Use Case: Development, paper trading, and cost-effective live trading

Finnhub (Alternative/Supplementary)

Strengths: Generous free tier (60 calls/min), comprehensive fundamental data
Pricing: Free tier available, paid plans from $49.99/month
Features: Real-time quotes, analyst estimates, earnings data, news sentiment
Use Case: Supplementary data for fundamental analysis and news sentiment

Alpha Vantage (Development Only)

Strengths: Easy Python integration, good for learning
Limitations: Severe rate limits, delayed data on free tier
Use Case: Initial development and testing only

Data Architecture Recommendations

Primary Setup: Polygon.io + Alpaca Markets

Polygon.io for comprehensive market data and advanced analytics
Alpaca for trading execution and basic real-time data
Dual-source architecture provides redundancy and cost optimization

WebSocket vs REST:

WebSocket: Use for real-time price feeds, trades, quotes (sub-second latency)
REST: Use for historical data, fundamental data, account management
Hybrid Approach: WebSocket for live data, REST for backtesting and analysis

2. Historical Data Sources

Recommended Sources

Polygon.io Historical Data

Coverage: 20+ years of historical OHLCV data
Granularity: Tick-level to daily bars
Features: Adjusted prices, corporate actions, dividend data
Access: Same API as real-time data

Alpaca Market Data

Coverage: Several years of US equities
Granularity: Minute bars to daily
Advantage: Seamless integration with trading API
Cost: Included with trading account

Alpha Vantage (Supplementary)

Coverage: 20+ years for major indices
Features: Technical indicators, sector performance
Use Case: Supplementary fundamental analysis

Data Storage Strategy

Primary: Store raw OHLCV data locally for backtesting
Compressed: Use Parquet format for efficient storage
Backup: Cloud storage for disaster recovery
Retention: 5+ years for comprehensive backtesting

3. Broker/Trading Platform Analysis

Alpaca Markets (Primary Recommendation)

Strengths:
- Commission-free stock trading
- Excellent API documentation and Python SDK
- Paper trading environment identical to live trading
- Real-time market data included
- Options API in development (2024)
Limitations:
- US markets only
- Limited international exposure
Commission: $0 stocks/ETFs
API Quality: Excellent (9/10)
Best For: Personal algorithmic trading, development

Interactive Brokers (IBKR) (Professional Alternative)

Strengths:
- Global markets access
- Advanced order types
- Professional-grade platform
- Comprehensive API (TWS API, Web API)
- Low commissions ($0.0035/share, $1 minimum)
Limitations:
- More complex API setup
- Higher minimum account requirements for some features
- Data fees can add up
API Quality: Excellent but complex (8/10)
Best For: Professional trading, global markets

TD Ameritrade/Schwab (Consider for Future)

Status: API access uncertain post-merger
Recommendation: Monitor for 2025 developments

Tradier (Backup Option)

Strengths: Simple API, options trading
Limitations: Limited data quality, higher commissions
Use Case: Backup broker or options-specific strategies

4. Technical Architecture

Recommended Tech Stack

Backend: Python Ecosystem

Primary Language: Python 3.11+
Core Libraries:
- pandas/numpy: Data manipulation and analysis
- asyncio: Asynchronous WebSocket handling
- FastAPI: REST API for frontend communication
- Celery: Task queue for strategy execution
- Redis: Message broker and caching
- SQLAlchemy: Database ORM

Database: QuestDB (Primary Recommendation)

Performance: 12-36x faster than InfluxDB for ingestion
Query Speed: 43-418x faster for complex analytical queries
Features: Time-series optimized SQL, Postgres compatibility
Use Case: Real-time tick data, OHLCV storage, strategy backtesting

Alternative: TimescaleDB

Pros: PostgreSQL compatibility, mature ecosystem
Cons: Slower than QuestDB for high-frequency data
Use Case: If PostgreSQL familiarity is critical

Real-Time Processing Pipeline

Architecture:
WebSocket Data Ingestion → Redis Stream → QuestDB
                        → Strategy Engine → Order Management → Broker API

AI/ML Stack

Core ML Libraries:
- scikit-learn: Traditional ML models
- TensorFlow/PyTorch: Deep learning models
- Stable-Baselines3: Reinforcement learning
- LightGBM/XGBoost: Gradient boosting
- TA-Lib: Technical indicators

Model Architecture:
- LSTM: Sequential price prediction
- Transformer: Multi-asset pattern recognition
- CNN: Chart pattern recognition
- Reinforcement Learning: Dynamic strategy optimization

Alert/Notification System

Telegram Integration: Real-time alerts to Henry's Telegram
Email Backup: Secondary notification channel
Push Notifications: Mobile app integration (future)
Dashboard: Real-time web interface

5. Trading Strategy Framework

Backtesting Framework: Backtrader (Primary)

Strengths:
- Event-driven backtesting
- Live trading integration
- Extensive documentation
- Active community
Use Case: Primary backtesting and strategy development

Alternative: VectorBT Pro

Strengths: Vectorized backtesting, massive speed improvements
Limitations: Different paradigm, paid license
Use Case: Large-scale parameter optimization

Core Strategy Categories

1. Mean Reversion Strategies

Concept: Price tends to return to average
Implementation: Bollinger Bands, RSI divergence, pairs trading
Risk: Works in ranging markets, fails in trending markets
ML Enhancement: LSTM to predict mean reversion timing

2. Momentum Strategies

Concept: Trends continue in the short term
Implementation: Moving average crossovers, breakout strategies
Risk: Vulnerable to sudden reversals
ML Enhancement: CNN for chart pattern recognition

3. Statistical Arbitrage

Concept: Exploit statistical relationships between assets
Implementation: Pairs trading, basket trading
Risk: Relationship breakdown risk
ML Enhancement: Dynamic correlation modeling

4. Sentiment-Based Strategies

Concept: News/social media sentiment drives prices
Implementation: NLP on news feeds, social media analysis
Risk: False signals, sentiment lags
ML Enhancement: Transformer models for sentiment analysis

AI/ML Strategy Implementation

LSTM Networks (Primary Focus)

Use Case: Price prediction, volatility forecasting
Architecture: 60-day lookback window, multi-layer LSTM
Features: OHLCV + volume + technical indicators
Performance: Shows promise for 1-5 day price predictions

Reinforcement Learning (Advanced)

Framework: Stable-Baselines3 with custom trading environment
Algorithm: PPO (Proximal Policy Optimization) or SAC (Soft Actor-Critic)
State Space: Price history, portfolio state, market indicators
Action Space: Buy/Sell/Hold with position sizing

Ensemble Methods

Approach: Combine multiple models for robust predictions
Models: LSTM + XGBoost + Traditional technical analysis
Weight: Dynamic model weighting based on recent performance

Risk Management Framework

Position Sizing

Kelly Criterion: Optimal bet sizing based on win rate and average returns
Fixed Fractional: Risk fixed percentage per trade (1-2% of portfolio)
Volatility Adjusted: Position size inversely correlated with volatility

Risk Controls

Maximum Drawdown: Halt trading if portfolio drops >15%
Daily Loss Limit: Stop trading if daily loss exceeds 3%
Concentration Risk: Maximum 10% allocation to single position
Correlation Limits: Avoid highly correlated positions

Stop-Loss and Take-Profit

Adaptive Stops: ATR-based stop losses that adjust to volatility
Trailing Stops: Capture profits while limiting losses
Time-Based Exits: Close positions held beyond optimal holding period

6. Regulatory and Legal Considerations

Pattern Day Trader (PDT) Rules

Current Rules (Subject to 2026 Changes)

Definition: 4+ day trades within 5 business days in margin account
Requirement: $25,000 minimum equity for pattern day traders
Implications: Restricts trading frequency for smaller accounts

Proposed Changes (2026)

FINRA Proposal: Eliminate $25,000 minimum requirement
New Structure: Risk-based intraday margin requirements
Timeline: Pending regulatory approval, implementation TBD
Recommendation: Design system to work under both regimes

Compliance Strategy

Cash Account Option: Avoid PDT rules entirely (T+2 settlement)
Account Monitoring: Track day trades automatically
Position Management: Spread trades across multiple days if needed

SEC/FINRA Automated Trading Rules

Registration Requirements

Individual Traders: No special registration required for personal trading
Algorithm Development: No registration for personal use algorithms
Supervision: FINRA Rule 3110 applies to firm algorithms only

Market Manipulation Prevention

Wash Sale Rules: Avoid buying/selling identical securities within 30 days for tax loss
Self-Trading Prevention: Not applicable to individual accounts
Order Flow Monitoring: Maintain logs of all algorithmic decisions

Tax Implications

Trader vs. Investor Status

Trader Status Benefits:
- Business expense deductions
- Mark-to-market accounting option
- No wash sale rule limitations
Qualifications:
- Substantial trading activity (>4 hours/day)
- Frequent and continuous trading
- Trading as primary income source

Tax Optimization Strategies

Tax-Loss Harvesting: Automatically realize losses to offset gains
Holding Period Management: Prefer long-term capital gains when possible
Account Structure: Consider separate accounts for different strategies

7. Implementation Roadmap

Phase 1: MVP (Months 1-3)

Core Infrastructure

Basic data ingestion from Alpaca Markets
QuestDB setup and OHLCV data storage
Simple mean reversion strategy implementation
Backtrader integration for backtesting
Paper trading with Alpaca
Basic Telegram notifications

Deliverables

Working paper trading system with one strategy
Basic backtesting capability
Real-time data collection and storage
Simple web dashboard for monitoring

Phase 2: Enhanced Trading (Months 4-6)

Advanced Features

Polygon.io integration for premium data
Multiple strategy framework (3-5 strategies)
LSTM price prediction model
Advanced risk management system
Portfolio optimization algorithms
Live trading transition (small account)

Deliverables

Production-ready trading system
AI-enhanced strategies
Comprehensive risk controls
Performance analytics dashboard

Phase 3: Advanced AI (Months 7-12)

AI/ML Enhancement

Transformer model for multi-asset analysis
Reinforcement learning trading agent
News sentiment analysis integration
Dynamic strategy allocation system
Advanced portfolio optimization
Mobile application for monitoring

Deliverables

Fully automated AI trading system
Multi-strategy portfolio management
Comprehensive performance tracking
Mobile monitoring capabilities

Phase 4: Scale and Optimize (Year 2)

Scaling Features

Multi-broker support (IBKR integration)
Options trading strategies
International markets access
High-frequency capabilities
Advanced alternative data sources
Tax optimization automation

8. Cost Analysis

Development Phase (Months 1-3)

Data: Alpaca (Free) + Polygon.io Basic ($199/month) = $199/month
Infrastructure: AWS/GCP hosting (~$100/month)
Development: Personal time investment
Total: ~$300/month during development

Production Phase (Months 4+)

Data: Polygon.io Professional ($399/month) + News feeds ($100/month)
Infrastructure: Enhanced hosting ($200/month)
Broker Commissions: Alpaca ($0) + spread costs (~$50/month for typical volume)
Total: ~$750/month operational costs

ROI Expectations

Target: 15-20% annual return on deployed capital
Break-even: ~$45,000 deployed capital to cover $750/month costs
Recommendation: Start with $100,000+ to ensure profitability after costs

9. Risk Assessment

Technical Risks

Data Quality: Primary vendor outage or data corruption
Mitigation: Dual data sources, data validation systems

Market Risks

Model Failure: AI models perform poorly in changing market conditions
Mitigation: Ensemble methods, model retraining, human oversight

Operational Risks

System Downtime: Critical trading periods without system access
Mitigation: Redundant infrastructure, mobile alerts, manual override capabilities

Regulatory Risks

Rule Changes: PDT rules, new algorithmic trading regulations
Mitigation: Flexible system design, legal consultation, compliance monitoring

10. Success Metrics

Performance Metrics

Sharpe Ratio: Target >1.5 (risk-adjusted returns)
Maximum Drawdown: Keep below 15%
Win Rate: Target >55% for mean reversion strategies
Profit Factor: Target >1.3 (gross profit / gross loss)

Operational Metrics

System Uptime: >99.5% during market hours
Order Execution Speed: <2 seconds average
Data Latency: <100ms for critical price updates

Business Metrics

ROI: Target 15-20% annual return
Cost Efficiency: Operational costs <1.5% of managed capital
Scalability: System capable of managing $1M+ within year 2

Conclusion

HedgeFund represents a comprehensive approach to personal algorithmic trading, combining institutional-quality infrastructure with cutting-edge AI/ML techniques. The recommended tech stack (Python + QuestDB + Alpaca/Polygon.io) provides an optimal balance of performance, cost, and ease of development.

The phased implementation approach ensures rapid initial progress while building toward a sophisticated, fully-automated trading system. With proper risk management and regulatory compliance, this platform can provide consistent alpha generation for personal trading objectives.

Next Steps:

Set up development environment with Alpaca paper trading account
Implement basic data pipeline and storage
Develop and backtest initial mean reversion strategy
Begin Phase 1 implementation following the detailed roadmap

This specification provides Henry with a clear, actionable roadmap for building a professional-grade AI trading platform suitable for personal use while remaining compliant with all relevant regulations.