Gamma Exposure (GEX)

GEX infers options-dealer hedging behavior — not dealer hedging positions, which are never public — from open interest, implied volatility, and one industry-standard but fundamentally unverifiable assumption. Every number this produces is an estimate under that assumption, and the single most common failure in this category is reporting it as a fact instead.

The gap this closes

GEX computations look deceptively easy to get right: sum some gammas, apply a scaling constant, done. Two things make it easy to get quietly wrong instead:

1. The sign convention is an assumption, not a physical law. The standard convention — customers are net buyers of options, dealers are net sellers, so call OI contributes positive gamma exposure and put OI contributes negative — is a public-GEX-calculator convention (the same one SqueezeMetrics-style tools use), not something derivable from the options data itself. Flip that one sign and the code still runs, still produces a smooth chart, and still gives a confident regime call — just the opposite one from what the data implies under the standard assumption. scripts/gex_gate.py --demo reproduces this exact bug on one synthetic chain: identical inputs, net_gex_total flips from -1.08M to +2.63M, regime flips from negative to positive.
2. The assumption itself is presented as measured fact. No public dataset shows dealers' actual positioning. A report that states "dealers are short gamma at 450" without the caveat is making a stronger claim than the data supports — the honest version is "under the standard assumption that dealers are net short customer flow, OI implies dealers are short gamma at 450."

Workflow

1. Get a real option chain — strike, open interest, and implied volatility for both legs at each strike, for one expiration. Free listed-equity data (e.g. yfinance) is enough; do not fabricate OI/IV to fill gaps.
2. Compute gamma per strike with the standard Black-Scholes closed form (scripts/gex_gate.py's black_scholes_gamma) — identical for calls and puts at the same (S, K, T, sigma), so there's no separate call/put gamma formula to get wrong.
3. Apply the standard sign convention (call OI: +, put OI: -) and the standard normalization (Γ × OI × contract_multiplier × S² × 0.01) — see references/methodology.md for why that specific scaling. Do not invent a different convention or scaling without calling it out as non-standard.
4. Attach both disclosures to the output every time, not just in an appendix: the dealer-positioning assumption, and what this snapshot does NOT cover (other expirations, intraday OI changes since the snapshot, OTC/index flow that free listed-equity data can't see).
5. Before sharing a GEX report, run it through the audit gate: python scripts/gex_gate.py --audit report.json. FAIL-severity flags (missing_dealer_assumption_disclosure, missing_coverage_limitation, gex_sum_mismatch, regime_sign_mismatch, flip_point_out_of_range, gamma_walls_mismatch) mean the report is either undisclosed or internally inconsistent — fix before sharing, don't caveat around it.
6. sparse_chain (WARN) does not block sharing, but disclose it: a zero-gamma flip or gamma-wall pick computed from fewer than 5 strikes has real resolution limits worth stating next to the number.
7. See the sign-bug and disclosure-gate mechanics with no data at all: python scripts/gex_gate.py --demo.

Guardrails

• Never state a GEX regime as an observed fact. It's always "under the standard assumption that dealers are net short customer flow, OI implies X" — not "dealers are X."
• Never silently choose a non-standard sign convention or scaling constant. If you have a specific reason to deviate from the industry standard, say so explicitly and explain why — don't let it look like the same convention everyone else uses.
• Never present GEX as a directional price forecast. It describes a hedging-flow regime (positive = dealer hedging tends to dampen volatility; negative = tends to amplify it), not a prediction of where price goes next.
• Never claim free listed-equity-options GEX covers a symbol's full dealer hedging book — OTC and index-option flow are frequently the larger piece and are invisible to this data source.
• A report with disclosures present is not automatically trustworthy — audit_gex_report also checks the numbers agree with each other (gex_sum_mismatch, regime_sign_mismatch) so a stale or hand-edited summary doesn't slip through just because the boilerplate caveat is there.

Bundled resources

• scripts/gex_gate.py — compute_gex() (Black-Scholes-based, standard sign convention and normalization) plus audit_gex_report(), the disclosure/consistency gate. --demo reproduces the sign-convention bug flipping a regime call on identical inputs, then shows the gate separate a compliant report from a hollow one and catch a tampered summary.
• references/methodology.md — the gamma formula, the normalization constant and why it's there, the sign convention and its limits, zero-gamma-flip interpolation, gamma-wall selection, and what this estimate structurally cannot see.