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> Claude is fuqing me in the ass. This is nothing but incomprehensible techno-jargon mumbo jumbo.

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#1 Yesterday 14:28:07

Claude is fuqing me in the ass. This is nothing but incomprehensible techno-jargon mumbo jumbo.

baseRmse is invariant to p. The DE optimizer can only improve calibScore by driving madThreshold → 3.0 and termStructureBandwidth → 0.30 — the hardcoded defaults. Phase 3 is not data-driven optimization; it is regularization disguised as optimization, and it only regularizes   
  toward the starting point. Caching SABR residuals would activate it.                                                                                                                                                                                       
                                                                                                       
  The real optimization of surface parameters requires rebuilding the vol surface with each candidate ivCleanerMadThreshold/termStructureBandwidth value, which is documented as too expensive. Since there is no tractable way to evaluate them in the DE loop, they should be removed from     
  TunableParams and TunableParamsOptimizer entirely, with the hardcoded defaults left in IvCleaner and TermStructureSmoother where they belong.


facepalm

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PelvicOarfish
#2 Yesterday 14:41:46

Re: Claude is fuqing me in the ass. This is nothing but incomprehensible techno-jargon mumbo jumbo.

Looks like your SABR calibration Phase 3 is chasing its own tail. If baseRmse is invariant to p, your objective function has a flat surface in that dimension and DE is just shuffling noise.

The tell is madThreshold and termStructureBandwidth converging to their starting values (3.0, 0.30). That is not optimization, that is the optimizer finding nothing better than initialization.

Cache the SABR residuals per-expiry before Phase 3 starts. Right now you are recomputing them inside the DE loop when they only depend on Phase 1/2 outputs. Once cached, your calibScore denominator actually varies with p and the optimizer has a gradient to follow.

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