Extend Validation to Individual Equities (5-7 Tickers)

[iAmGiG]

Context

Advisor request: "Will you be able to first extend this analysis to individual equities? In this way, we will have a complete picture of the current research and can present the new version of a journal"

Purpose: Extend pattern validation beyond SPY (index) to individual equities to demonstrate cross-asset generalization before journal submission.

Technical Feasibility: ✅ Ready

Infrastructure Status:

• ✅ GEX calculation code is symbol-agnostic
• ✅ Validation pipeline accepts --symbol parameter
• ✅ Database schema supports multiple symbols
• ✅ Data sources (Polygon.io) cover individual equity options

Minimal Code Changes Required - just run existing validation with different symbols.

Proposed Equity Selection (5-7 Tickers)

Selection Criteria:

1. High options volume (liquid markets)
2. Diverse sectors (avoid sector bias)
3. Different volatility profiles (test robustness)

Recommended Tickers:

Ticker	Sector	Avg Daily Options Volume	Volatility Profile (IV)
AAPL	Technology	~1.5M contracts	Low-Medium (15-25%)
TSLA	Consumer Cyclical	~2M contracts	High (40-60%)
NVDA	Technology/AI	~1.8M contracts	High (35-55%)
JPM	Financials	~400K contracts	Low (18-25%)
XOM	Energy	~300K contracts	Low-Medium (20-30%)
AMZN (optional)	Consumer Cyclical	~900K contracts	Medium (25-35%)
AMD (optional)	Technology	~1.2M contracts	High (35-50%)

Rationale:

• AAPL: Largest options market (benchmark)
• TSLA: Extreme volatility (stress test)
• NVDA: AI-driven volatility (2024 relevant)
• JPM: Financial sector (different dynamics)
• XOM: Commodities-linked (oil exposure)

Timeline Estimate (HPCC Processing)

Task	Time Required	Notes
Data Collection	1-2 days	Fetch 2024 options chains for 5-7 equities
Database Build	1 day	Parallel processing per symbol
Validation Runs	2-3 days	~250 days × 5 equities × 3 patterns ≈ 3,750 validations
Analysis & Comparison	2-3 days	Cross-equity comparison, pattern consistency
Total	6-9 days	Assuming HPCC access and no data gaps

Expected Research Outcomes

What We'll Learn:

1. Pattern Generalization:

   • Does 100% detection hold across individual equities?
   • Or is dealer constraint less binding for single stocks?

2. Liquidity Effects:

   • AAPL (very liquid) vs. XOM (less liquid) comparison
   • Does pattern work only in deep markets?

3. Volatility Regime Interaction:

   • TSLA (high vol) vs. JPM (low vol)
   • Does LLM adapt detection to volatility profile?

4. Sector Differences:

   • Tech (AAPL/NVDA) vs. Financials (JPM) vs. Energy (XOM)
   • Are dealer constraints sector-specific?

Execution Plan

Phase 1: Data Availability Check (Day 1)

Verify data coverage for each equity (threshold: ≥80% per Issue #84)

Phase 2: Database Build (Days 2-3)

Build historical GEX database for each equity, verify spot prices are realistic (not 450.0 per Issue #81)

Expected spot price ranges (approximate):

• AAPL: $150-240
• TSLA: $140-420
• NVDA: $400-950
• JPM: $140-250
• XOM: $95-125

Phase 3: Validation Runs (Days 4-6)

Run validation for each equity × each pattern:

• Patterns: gamma_positioning, stock_pinning, 0dte_hedging
• Symbols: AAPL, TSLA, NVDA, JPM, XOM
• Total: 15 validation runs

Phase 4: Cross-Equity Analysis (Days 7-9)

Generate comparison tables:

• Detection rates by equity
• Predictive accuracy by volatility profile
• Liquidity effects on pattern strength

Risks & Mitigation

Risk 1: Data Gaps for Individual Equities

• Mitigation: Pre-check data availability, use only equities with ≥80% coverage
• Fallback: Document coverage limitations per equity

Risk 2: Lower Detection Rates

• This is GOOD: Shows methodology discriminates
• Paper angle: "LLM correctly identifies when constraints are weaker"
• Expected: SPY (100%) > Mega-cap (80-90%) > Others (60-80%)

Risk 3: Heterogeneous Results

• This is EXPECTED: TSLA ≠ JPM dynamics
• Paper contribution: "Demonstrates LLM adapts to market structure"

Success Criteria

✅ All 5 equities have ≥80% data coverage for 2024
✅ Spot prices realistic (not 450.0), no corruption
✅ All 15 validation runs complete
✅ Detection rates vary meaningfully across equities
✅ Cross-equity tables generated for paper
✅ Paper updated with generalization section

Deliverables for Paper

1. Extended results table (6 assets: SPY + 5 equities)
2. Liquidity effect analysis
3. Volatility profile analysis
4. Sector comparison
5. Updated paper sections (abstract, results, discussion)

Dependencies

• Issue Verify Validation Results on HPCC Database #86: SPY validation complete
• Issue Critical: Obfuscation Not Applied in run_experiment() - Issue #79 Results May Be Tainted #81: Database corruption fix verified
• Issue Validation Pipeline Design Flaw: Only Tests Cached Dates #84: Data coverage validation
• HPCC access with sufficient compute

Priority

Medium-High - Not blocking symposium (10 days), but needed for journal paper (30-45 days)

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Estimated Effort: 6-9 days of compute + analysis time
Target Completion: Before journal submission (30-45 days)

[iAmGiG]

Paper #3 Direction Confirmed (Oct 25, 2025)

This issue relates to Paper #3: Cross-Asset Generalization in the research roadmap.

Updated Scope

Research Questions:

• Does obfuscation testing generalize beyond SPY index options?
• Do dealer constraints differ between index and single-name options?
• Can LLMs detect stock-specific vs market-wide patterns?

Methodology:

• Test on 10-20 individual stocks (high liquidity)
• Use sequential analysis if Paper 🔴 Environment Setup & Configuration #2 validates it
• Compare SPY (index) vs AAPL, MSFT, NVDA, etc. (single-name)
• Test pattern persistence across asset classes

Timeline: Q2 2026 submission

Dependencies:

• Paper Data Pipeline: SPY/SPX Historical Options Chain Collection #1 acceptance
• Paper 🔴 Environment Setup & Configuration #2 submission (determines if sequential methodology is validated)

Prioritization: After Paper #2 (Sequential GEX Analysis - Issue #89)

See full details: docs/papers/research_roadmap.md