Add example 1.6: exact Heston Greeks via AADC - #28
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…F 245x) Four levels of AADC-accelerated differentiation for Heston options: A) Fourier pricing (PROJ method): price + 5 Greeks in 0.8 ms (158x vs FD) B) Calibration: exact Jacobian for 20 instruments (39x vs scipy FD) C) AITF: d(calibrated params)/d(market prices) via IFT (245x vs recalibration) D) Monte Carlo: batch API, 200K paths + 6 Greeks (111x single-thread, 348x 4-thread) All gradients verified against finite differences (ratio 0.999–1.001). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Summary
Adds a new example (1.6) showing how AADC (Automatic Adjoint Differentiation) computes exact Heston Greeks, accelerates calibration, and enables implicit differentiation through calibration (AITF).
Four levels of speedup, all verified against the library's own
Fourier_inversion()and finite differences:All gradients match FD (ratio 0.999-1.001). AITF sensitivities match FD recalibration (ratio 1.0000).
Key techniques
idouble(real/imag split) for the Heston characteristic functioncsqrt/clog(Heston CF guarantees re(z) > 0)mark_as_input_no_diff(), oneevaluate()call for all pathsDependencies
aadc(pip install aadc, free evaluation, no licence key)