Engine · Receipt pending #288615 · 2026-05-01

CME V0.2-Perps

Seven-constraint coherence-violation engine for crypto derivatives. Closes the “anchor process” gap named in MNX's 2026-03-25 “Why Perps?” post: a perpetual contract needs a credible index or settlement process to anchor against. Coherence violations are the quantitative signal that the anchor is broken.

Receipt manifest #288615Filed 2026-05-01Freeze commit adb99d6V0.1 ancestor

The seven constraints

Each constraint enforces a no-arbitrage relation across the crypto-derivative graph. A persistent violation, after subtracting depth-aware execution costs, is a static or quasi-static arbitrage signal.

Cash-and-carry parity

perp − spot = funding basis over the funding interval

Canonical perp-spot anchor. Violation = arbitrageable basis between the perp and spot legs after expected funding cost. Default emission threshold: 50 bps cost-adjusted.

Triangle parity

Same-underlying perp pair vs stablecoin FX must be self-consistent

ETH-USDC perp / ETH-USDT perp must price-imply the same USDC/USDT FX, after fees. Violation = 3-leg synthetic-FX arbitrage. Default threshold: 30 bps cost-adjusted.

Put-call parity

C − P = S − K · exp(−r·T) on European options

Synthetic forward (long call + short put at the same strike) must equal the actual forward (spot minus discounted strike). Violation = 3-leg arb across call, put, underlying. Default threshold: 30 bps cost-adjusted.

Butterfly non-negativity (Carr-Madan generalized)

(K3 − K2)·C(K1) − (K3 − K1)·C(K2) + (K2 − K1)·C(K3) ≥ 0

Generalized for any K1 < K2 < K3 (no symmetry required). Reduces to the classical symmetric butterfly when K2 is the midpoint. Violation = static arb at expiry; non-negative payoff replicated for negative cost. Default threshold: 20 bps cost-adjusted.

Calendar-spread monotonicity (Litterman-Scheinkman PCA)

Forward funding curve decomposes into level + slope + curvature factors

Three-factor PCA on historical funding curves. Anomalous curvature factor (rolling z-score above threshold) signals a non-monotone “hump” in the term structure that is statistically unusual relative to the historical regime.

Cross-venue 4-corner coherence

DEX perp + CEX perp + options synthetic forward + spot reference must agree

Highest-EV constraint in the set: leverages cross-venue capital inefficiency that single-venue traders cannot exploit. Spread between max and min corner price (after depth-aware cost) is the violation magnitude. Default threshold: 60 bps.

Vertical-spread monotonicity

0 ≤ C(K1) − C(K2) ≤ K2 − K1 for K1 < K2

Both bounds enforce no-arb on adjacent-strike call options. Lower-bound violation: K2 call worth more than K1 call (impossible under no-arb). Upper-bound violation: call spread exceeds the strike differential. Per Carr-Madan 2005 Theorem 2 jointly with the butterfly constraint. Default threshold: 15 bps cost-adjusted.

Cost adjustment

Every constraint subtracts a depth-aware execution cost before emitting a signal. When the caller provides top-of-book spread + depth + target notional, the cost model uses a linear-impact slippage path (Almgren-Chriss 2001 + Cont-Kukanov-Stoikov 2014). When depth fields are unavailable, the model falls back to fixed fees + slippage per leg.

per_leg_entry_bps = fees_bps
                  + (top_spread_bps / 2)                       # half-spread crossing
                  + (top_spread_bps · depth_consumed_fraction) # linear-impact slippage
round_trip_cost = 2 · per_leg_entry_bps                          # entry + exit

Audit chain

Every emitted signal carries a deterministic UUIDv5 derived from a canonical-JSON SHA-256 hash of its violation provenance. Re-running the orchestrator on the same inputs produces identical signal IDs, making the emit ledger idempotent under replay. Resolution outcomes hash forward: prev_hash of each row equals the previous row's row_hash, forming a tamper-evident chain that anyone can verify in browser at /research/verify.

Backtest receipt pending

Manifest #288615 records the methodology + hypotheses, with external AsPredicted public receipt pending. The 90-day walk-forward backtest runs after the daily emit cron has accumulated signal data and resolutions. Three formal hypotheses:

  • H1 (signal alpha): Net annualized Sharpe ratio > 1.0 after Hyperliquid + Binance + Deribit fees, depth-aware slippage, funding cost, and capital opportunity cost. Permutation null at p < 0.05.
  • H2 (capacity ceiling): Sum of modeled net EV < 50,000 USD/day on average. Bounds the trader-tier monetization ceiling.
  • H3 (per-constraint significance): Each of the seven constraint kinds contributes positive Sharpe individually with a 95% bootstrap CI excluding zero. Failing constraints get dropped from the production orchestrator.

Data sources & rights

The reference values on this page are derived from publicly observable market data, used as modeled cross-venue inputs. Convexly does not assert any license over the underlying data. Where redistribution rights have not been positively confirmed, they are noted as under review.

Hyperliquidpublic_observable

Source: Hyperliquid public on-chain and API market data (redistribution rights under review).

Binancepublic_observable

Source: Binance public market data, used as a modeled cross-venue reference (redistribution rights under review).

Deribitpublic_observable

Source: Deribit public options and market data, used as a modeled reference input (redistribution rights under review).

Methodology rights. The seven-constraint engine, cost model, coefficient set, and emitted coherence signals are Convexly's own research output. The underlying market inputs above retain their own rights status with the respective venues.

Trademark. Venue names are trademarks of their respective owners. Convexly is independent and not affiliated with or endorsed by Hyperliquid, Binance, or Deribit.

References

  • Carr, P. and Madan, D. (2005). “A Note on Sufficient Conditions for No Arbitrage.” Finance Research Letters 2(3), 125-130. Equation (2) is the generalized butterfly form; Theorem 2 jointly characterizes butterfly + vertical-spread no-arb conditions.
  • Litterman, R. and Scheinkman, J. (1991). “Common Factors Affecting Bond Returns.” Journal of Fixed Income 1(1), 54-61. Level / slope / curvature factor decomposition.
  • Almgren, R. and Chriss, N. (2001). “Optimal execution of portfolio transactions.” Journal of Risk 3, 5-39. Linear-impact framework underlying the depth-aware cost model.
  • Cont, R., Kukanov, A., and Stoikov, S. (2014). “The price impact of order book events.” Journal of Financial Econometrics 12(1), 47-88.
  • Hull, J. (2021). Options, Futures, and Other Derivatives, 11th ed. Sections 5.6, 11.3, 11.5 (term structure, put-call parity, vertical-spread bounds).
  • López de Prado, M. (2018). Advances in Financial Machine Learning. Wiley. Ch. 7 (purged k-fold) for fold geometry in the H1 backtest.
  • Convexly Coherent Markets Engine V0.1 (prediction-market ancestor) at /research/coherent-markets-engine-v1.
  • Convexly Edge Score V2-Perps composite (per-trader skill ranking; sister methodology) at /research/v2-perps.

This work used AI tools (Claude, GPT-4) as research aids during methodology design and pre-publication review. All claims on this page are reproducible from the freeze-commit code modules under services/api/app/engine/cme_v0_2_perps/ with the documented random seed. No claim on this page is taken as true on the basis of an AI tool's output; every quantitative result is recomputable from the freeze commit.