Sigma Surf Communications - August 22, 2025¶
Overview¶
Key strategic communications between Daniel Shanklin and Collin Bird regarding the ML-Driven Volatility Trading System development and implementation roadmap.
Action Items & Research Tasks¶
Luke Research Assignment¶
Alpaca Trading API Integration - Option chain data collection via Alpaca Markets API - Large trade alerts monitoring - API endpoints: - Option Latest Trades - Option Chain Data
Monte Carlo Efficiency Enhancement¶
Sobol Sequence Implementation - Target: 6x-8x improvement in Monte Carlo efficiency - Technical reference: Sobol Sequence Wikipedia - KDB implementation: KX Options Pricing - External counsel available: Andrew Fritz (RBC Capital) - Top 50 globally in KDB/high-frequency trading databases
Three-Phase Strategic Roadmap¶
Phase 1: Terminology Standardization¶
Core Definitions: - Z-score: Number of standard deviations an asset/metric deviates from mean (e.g., +2.5 when IVOL is 2.5σ above historical mean) - Sigma (σ): Value of one standard deviation (e.g., σ = 6% IVOL) - Margin: Required posted capital (Reg-T: Max Loss for spreads, ~20% stock price minus premiums for naked shorts) - Max Loss: Strike width minus Net Credit - Net Credit: Sold premiums minus purchased premiums
Margin Calculations: Slate coordinating with BTIG for detailed margin calculations to support VaR and CVaR analysis
Phase 2: Data Collection Infrastructure (8-20 hours)¶
Bloomberg Integration: - Two-tab spreadsheet: prices and option chain data - Macro automation for Hedgeye long ideas universe - Historical data collection for stock prices and IVOL surfaces
Python Calculations Implementation: - Implied vs Realized volatility spread - Skew analysis - IV Rank/Percentile - RSI indicators - Bollinger distance metrics - Enhanced backtesting with expanded dataset
Phase 3: AIC Data Science Repository v1.0 (8-12 hours)¶
Core Components: - Trade calculators and Streamlit prototypes - Documentation and whitepapers with standardized terminology - Web launcher (6 hours): AIC-wide access to Streamlit prototypes - Technical whitepaper (2-4 hours): Complete calculations and assumptions documentation
Technology Stack Rationale: Streamlit chosen over Next.JS for faster prototyping vs. enterprise Meridian platform
Strategic Trading Framework¶
Collin's Strategic Vision¶
Core Philosophy: "Pragmatizing and not missing these opportunities is what makes this strategy worthwhile from an absolute return standpoint."
Backtesting Enhancement Parameters: - Absolute Vol Thresholds: Volatility levels that justify annual yield generation - Historical Vol Comparison: Percentage volatility over historical averages - IVOL/RVOL Premium Analysis: Implied vs realized volatility spread identification
Put Spread Strategy Development¶
Entry Signal Combinations: - IVOL Spikes: Identify when market participants are "overpaying for protection" - Price Selloff Triggers: - Simple percentage declines - Volatility-adjusted Bollinger band breaches - RSI oversold conditions - Fundamental Overlay: Essential component for trade validation - Abnormal Options Flow: Bulltard-style dataflow monitoring for unusual call/put volume activity
Options Flow Intelligence¶
Volume Anomaly Detection: "Would add Bulltard style dataflow looking for abnormal call/put volume on names. Insider trading is real and big players don't take uncalculated, uninformed risks on those large option positions."
Monitoring Framework: - Abnormal call volume spikes on individual names - Unusual put volume patterns indicating institutional positioning - Large block option trades outside normal flow patterns - Cross-referencing volume anomalies with fundamental catalysts
Strategic Universe & Investment Philosophy¶
Target Universe: - Primary: Hedgeye Long Ideas - Secondary: High-quality companies with consistent "priced to perfection" valuations - Screening Criteria: Equity risk premiums <2%
Investment Conviction: "One of my few highly held convictions is that buying quality on dips is an outperforming strategy in the long run."
Strategic Benefits: - Market Exposure: Interesting SPY beta/quality exposure - Patient Capital: "Getting paid to wait to buy quality on the dip at the price you want" - Mathematical Advantage: Theta burn working in favor of position - Purchase Discipline: Enhanced price entry requirements - Hurdle Enhancement: Increased standards for quality stock acquisition
Risk Management Framework¶
Critical Requirements: - Strict Notional Exposure Controls: Total position size limits at all times - Quality-First Approach: Focus on high-quality underlying assets - Systematic Execution: Predetermined entry/exit criteria - Capital Efficiency: Optimized margin utilization while maintaining risk controls
Implementation Status¶
Current System Capabilities¶
- Sigma Surf Algorithm: Systematic volatility inefficiency exploitation
- Bear Call Spreads: Defined risk with premium capture
- Backtesting: Initial AutoZone profitability validation
- AI Development: 20x productivity boost (11,000 lines Python in 1 day)
Next Phase Requirements¶
- IVOL history expansion (hundreds of stocks)
- Million-trade backtesting scale
- ML component enhancement for large-scale simulations
Communication Participants: Daniel Shanklin, Collin Bird, William Holloway, Slate DeMuth, Luke Huntley
Date Range: August 20-21, 2025
Strategic Focus: Volatility trading system operationalization and risk management enhancement