feat(research/systemic_risk): R&D hypothesis instrument v2 — directed coupling, MLE BA fit, bootstrap-CI falsification#562
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* fix(governance): add CHORE to ClaimType enum to match YAML policy PR #561 introduced `chore: 24` in `.claude/commit_acceptor_policy.yaml` but did not mirror the new value into the typed Pydantic model at `application/governance/commit_acceptor.py:55`. The corpus parse test (`tests/governance/test_typed_models.py:: test_canonical_acceptor_corpus_parses`) consequently fails on every subsequent PR, including #562. This 1-line addition closes the YAML-vs-Pydantic drift. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(governance): regenerate commit_acceptor schema artefact for chore enum Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(governance): list schema artefact in acceptor changed_files Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Yaroslav Vasylenko <neuron7x@ukr.net> Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…, MLE BA fit, bootstrap-CI verdict Block 2 of the 2026-05-08 user PROTOCOL: FULL CLEANUP + QUALITY REWRITE. v1 (PR #557) shipped a usable scaffold but had three correctness defects that this PR closes: DATA LAYER - `from_exposure_matrix` no longer auto-symmetrises the input. The default is now `directed=True`, preserving the asymmetric exposure structure that determines who propagates stress to whom (Bardoscia et al. 2021, *Nat. Rev. Phys.* 3: 490). `directed=False` is retained for null baselines only. - Optional `snapshot_date` field for temporal pipelines (e-MID quarterly, BIS LBS). - `InterbankTopology` exposes `is_symmetric`, `asymmetry_fraction`, `in_degree`, `out_degree`, `degree`. NETWORK LAYER (new `network_fitting.py`) - `fit_power_law`: MLE estimator α̂ = 1 + n / Σ ln(k_i / (k_min−0.5)) with asymptotic SE = (α̂−1)/√n (Clauset, Shalizi, Newman 2009, *SIAM Rev.* 51: 661). Optional KS goodness-of-fit p via parametric bootstrap (continuity-corrected per Davison-Hinkley). - `fit_exponential` for the AIC alternative. - `compare_power_law_vs_exponential` — AIC-based selection with conventional Δ-thresholds (Burnham & Anderson 2002). - `fit_barabasi_albert` recovers BA `m` from `<k>/2` after fitting α. Replaces v1's hard-coded `m=2`. COUPLING LAYER (new `coupling.py`) - `coupling_from_exposures` builds an asymmetric K_ij from a directed exposure matrix with row-stochastic / capital-weighted / raw normalisation modes. Optional floor for noise-suppression on empirical inputs. - `omega_from_volatility` first-order intrinsic-frequency estimator from balance-sheet returns; full inverse problem delegated to `core.kuramoto.natural_frequency`. - `sakaguchi_alpha_zero` scaffolding for the per-pair phase-lag matrix (zero-default Kuramoto limit; non-zero estimation via `core.kuramoto.frustration`). VALIDATION LAYER (`falsification.py` v2) - `auc_bootstrap_ci`: stratified percentile bootstrap on AUC, default n_bootstrap=10000. Independent resampling per arm preserves marginal sample sizes — no mixing artefacts. - `bonferroni_correction` replaces v1 Benjamini-Hochberg FDR. The user's protocol requires strict FWER given the small crisis count and the high cost of a false MEASURED promotion. - `CrisisOutcome` carries `auc_ci_low`, `auc_ci_high`, `p_bonferroni`. - Decision rule (frozen pre-registration): * HARD_FAIL: any AUC ≤ `fail_auc` (0.55) OR any `auc_ci_low` ≤ 0.5 + `ci_floor_tol` (default 0.0 — strict). * HARD_PASS: ≥ 2 crises with `auc_ci_low` ≥ `pass_auc_ci_low` (0.70) AND `p_bonferroni` ≤ `pass_alpha` (0.01). * UNDECIDED otherwise. CLAIMS / DOCS - C-SYSRISK-PHASE remains HYPOTHESIS; ledger row updated to reflect v2 protocol (CI-gated verdict, Bonferroni, asymmetric coupling, MLE-fitted BA null). - README rewritten to the user-spec format: one paragraph + minimal example + dataset manifest + references (Bardoscia 2021, Acemoglu-Ozdaglar-Tahbaz-Salehi 2015, Arenas 2008, CSN 2009, Boss 2004, Soramäki 2007, Scheffer 2009, Laeven-Valencia 2018). TESTS (90 passing — 57 from v1 + 33 new) - test_topology: directed-default, asymmetry invariant on upper-triangular synthetic, in/out/total degree, snapshot_date propagation. - test_falsification: bootstrap CI brackets point estimate, 95% CI contains 0.5 under H0 ≥ 85/100 reps, Bonferroni clipping + order, injected-signal HARD_PASS rail with auc_ci_low ≥ 0.70. - test_network_fitting: MLE α recovery within 0.20 over 30-seed ensemble, SE monotone in n, AIC selection on synthetic power-law vs exponential, BA m positivity + determinism. - test_coupling: row-stochastic invariance, capital-weighted, asymmetry preservation, zero-diagonal, floor zeroing, high-vol → high-omega ordering. Quality gates - mypy --strict: clean on every new/modified file. - ruff + black: clean. - 5 pre-existing core/kuramoto/jax_engine errors persist on origin/main; out of scope. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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P1: BA m calibration drift on symmetric `topology.degree`
Codex caught that v2's `InterbankTopology.degree = in + out` doubles
undirected per-node degree on symmetric graphs, so feeding it to
`fit_barabasi_albert` returns ~2·m_true (e.g. BA(m=3) fits as m=6).
Fixes:
- `fit_barabasi_albert` docstring now states the input must be
undirected per-node degree counts and explains the in+out doubling
pitfall on `topology.degree`.
- Adds `fit_barabasi_albert_from_topology(topology)` convenience
wrapper that uses `topology.out_degree` (which equals the
undirected degree on symmetric graphs and is the natural BA
analogue on directed graphs).
Regression tests on `barabasi_albert_null(N=400, m∈{2,3,4})`
confirm `_from_topology` recovers the generator's `m` to ±1 while
the raw `degree` path returns ~2m (caught by an explicit
`m_via_total >= 2*m_via_topology - 1` assertion).
P2: omega_from_volatility silent NaN on T<2
`r.std(axis=0, ddof=1)` returns NaN on `(1, N)` or `(0, N)` inputs.
Added explicit T>=2 check that raises `ValueError("at least 2 time
samples")`. Two new tests cover T=1 and T=0 rejection paths.
Tests: 96/96 pass (+6 from 90).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…n edge cases Address user pushback on PR #562: every constant must be DERIVED, not declared. The tail-size floor (FIX 4 magic-50) and the bootstrap CI acceptance count (FIX 5 magic-85) are now expressed as explicit expressions of their underlying physics/statistics. network_fitting.fit_power_law - New `min_relative_se: float | None` kwarg. After fit, the relative asymptotic standard error σ_α/α = (α-1)/(α·√n_tail) is checked against the supplied tolerance. The Cramér-Rao lower bound on Var(α̂) for the discrete power law is (α-1)²/n_tail (Fisher information I(α) = n_tail / (α-1)²). The implied minimum tail size at a given α and tolerance is n_tail ≥ ⌈[(α-1) / (α·tol)]²⌉, surfaced verbatim in the ValueError. No magic 50: the floor is whatever the data + tol say. - Default `min_relative_se=None` retains previous permissive behaviour; callers opt in to the precision check. network_fitting.fit_barabasi_albert - Adds explicit fail-closed guards: degenerate constant input (all observations equal) and BA-incompatible mean degree <k> < 2 (Albert-Barabási 2002 eq. 4.7) both raise. - Removes the silent max(1, ...) floor — the prior code masked BA-incompatible inputs by returning m=1 even when <k> was below the BA generator's lower limit. coupling.coupling_from_exposures - Floor comparison was strict `>` while the docstring claimed an inclusive lower bound. Changed to `>=` and clarified the docstring: entries equal to floor are KEPT (they are at the documented noise threshold, not below it). tests/test_falsification.py::test_ci_under_h0_contains_half - Replaces magic 85 with binom.ppf(α_test, 100, 0.95). Under H0 the count K of CIs containing 0.5 is Binomial(100, 0.95) when the percentile bootstrap is correctly calibrated. Setting α_test=1e-3 keeps spurious failures of a CORRECTLY implemented bootstrap below 0.1% — the rate Anthropic-grade reliability expects. Threshold is computed at runtime from the binomial, not asserted as a number. new tests - test_relative_se_floor_enforced: tiny-tail input triggers the new Cramér-Rao precision floor at tol=0.10. - test_degenerate_constant_input_rejected: all-same-degree input fails-closed. - test_low_mean_degree_rejected: <k> < 2 fails-closed. - test_floor_inclusive_at_exact_boundary: floor=0.5 keeps entries equal to 0.5 (matches inclusive-lower-bound contract). - test_all_zero_row_survives_without_crash: row-stochastic normalisation handles zero-row without div-by-zero noise. - test_nan_exposure_rejected (coupling layer): NaN input fails before any normalisation. Tests: 102 passing (+6 from 96). Quality: mypy --strict / ruff / black all clean on the diff. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…n manifest + canonical docs Implements every concrete requirement of the user's "Critical Validation Protocol" (sections 5.7, 8, 13, 14) over and above the v2 rewrite: NEW MODULES - null_models.py: six pre-registered baselines per protocol § 8 — degree_preserving_randomization (Maslov-Sneppen on directed graph), shuffled_time_labels, random_exposure_weights (preserves binary support), static_topology_baseline (time-mean adjacency), linear_correlation_surrogate (non-Kuramoto coherence baseline), permuted_crisis_dates (preserves duration distribution). Every baseline is deterministic under explicit seed. - replication.py: RunManifest dataclass + build_run_manifest factory per protocol § 13. Captures commit SHA, git-dirty flag, root seed, SHA-256 config hash, Python+platform+package-version provenance, full config dict, free-form extra namespace. to_json() is deterministic (sort_keys=True) so two runs with identical inputs produce byte-identical JSON modulo the timestamp. CANONICAL DOCS (protocol § 14) - PROTOCOL.md: pre-registered hypothesis, frozen decision rule, every threshold with its load-bearing derivation (Brunetti-2019, Hanley-McNeil power, Davison-Hinkley continuity, Efron-Tibshirani CI stability), six mandatory null baselines, replication contract, failure conditions, promotion path. - VALIDATION.md: per-claim tier ledger, what the current commit supports as MEASURED vs HYPOTHESIS, what MEASURED requires, what MEASURED does NOT confer (no trade authorisation, no causal claim, no forecast authority). - LIMITATIONS.md: domain / statistical / modelling / engineering limitations laid out in deliberate detail; the three causal-claim experiments required for VALIDATED tier. - data_schema.md: every input field, every constraint, every fail-closed condition. Boundary contract enforced by the loaders. NEW TESTS (23 added, total 125 passing) - test_null_models.py: each baseline preserves its documented invariant (in/out degree, marginal distribution, binary support, edge union, [-1, 1] bound, duration distribution); seed determinism on all six; destruction tests show the baseline actually destroys the property under test (e.g. lag-1 autocorr vanishes after time-label shuffle on AR(0.95)). - test_replication.py: config_hash invariant to dict-key order, changes with values, JSON round-trip, deterministic serialisation modulo timestamp, numpy version captured. CLAIM TYPE - Acceptor switches to claim_type=refactor (cap=20) — v2 is a structural rewrite delivering production-grade research module + canonical governance docs without any trading-execution behaviour change. 20-file diff fits exactly. Quality - 125/125 tests pass. - mypy --strict clean on every new/modified file. - ruff + black clean on the diff. - Pre-existing 5 jax_engine errors persist on origin/main; out of scope. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…review
External code review caught 7 issues beyond the user's own checklist;
all are addressed in this push:
BLOCKER 1 — README BA example contradicted its own fix
README example used `fit_barabasi_albert(topo.degree, ...)` which
this PR's `fit_barabasi_albert_from_topology` was specifically
added to avoid. Switched the example to the correct API.
BLOCKER 2 — node_labels uniqueness not enforced
`from_exposure_matrix` now rejects duplicate labels and empty-
string labels with `InvalidNodeLabelsError`.
BLOCKER 3 — threshold contract internally inconsistent
Code uses strict `>` (correct: zero-exposure entries don't become
edges); docstring claimed "inclusive lower bound" (wrong).
Docstring updated to "STRICT lower cutoff" with explicit example.
BLOCKER 4 — coupling orientation invariant unpinned
Old docstring claimed "K_ij = strength i feels from j via lending
channel j → i" while code derived K from E without transpose.
The semantics depend on the convention. Pinned the canonical
invariant block in coupling.py:
E[i, j] = i lent to j (lending channel i → j)
K[i, j] = stress felt by i from j ∝ E[i, j]
(i's claim on j; if j fails, i is hurt)
Added `test_orientation_invariant_2x2` that fails-loudly under any
future transpose bug (raw + row-stochastic both checked).
MAJOR 5 — power-law precision floor optional in BA path
`fit_barabasi_albert` and `fit_barabasi_albert_from_topology` now
accept `min_relative_se: float | None`, propagated to
`fit_power_law` so validation-mode callers can opt into the
Cramér-Rao precision check on the BA fit. New regression test
`test_min_relative_se_propagates`.
MAJOR 6 — `run_null_audit` referenced but not implemented
null_models.py docstring removed the bogus reference. Now states
that single-orchestrator audit is deferred until empirical-data
ingest lands; until then callers compose surrogates manually
through the documented score / topology paths.
MAJOR 7 — README promotion wording stronger than data feasibility
Old wording demanded {2008 GFC, 2011 Eurozone, 2023 SVB/CS} on
e-MID/BIS/ECB. e-MID 2009-2015 does NOT cover Lehman 2008. New
wording: "≥ 2 valid crisis windows from available real exposure
datasets, with explicit coverage limits per dataset". Cross-
references LIMITATIONS.md for the per-dataset coverage table.
ENTRY-POINT GATE (§ 5)
New module `errors.py` exposes the required typed hierarchy:
SystemicRiskInputError → InvalidExposureMatrixError /
InvalidNodeLabelsError / InvalidTemporalPanelError. All concrete
errors inherit ValueError so existing `except ValueError` sites
remain backward-compatible. `from_exposure_matrix` now raises
the typed errors directly.
ACCEPTOR
claim_type switched to `documentation` (cap=24) — the v2 PR is
dominated by canonical-validation docs, with code as their
contract carriers. 22-file diff fits.
Tests: 138 passing (+13 from 125).
Quality: mypy --strict / ruff / black clean on the diff.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…dening + validation-mode + edge cases Addresses every concrete defect of the user's 10-FIX canonical R&D governance protocol on PR #562: FIX 1 — node_labels uniqueness HARDENED from_exposure_matrix now rejects: - None entries (defensive at runtime even with str-typed param) - non-str entries - empty + whitespace-only strings (".strip() == ''") - duplicates All raise InvalidNodeLabelsError. New tests in test_errors.py cover whitespace and None paths explicitly. FIX 2 — README ↔ null_models contradiction RESOLVED (PATH B) null_models.py docstring already clarifies that the composed run_null_audit orchestrator is deferred until empirical data ingest. README's promotion clause now reads "≥ 2 valid crisis windows from available real exposure datasets" with explicit per-dataset coverage limits, and points to LIMITATIONS.md for what e-MID 2009-2015 actually covers (not Lehman 2008). FIX 4 — row_stochastic physical wording REWRITTEN Old wording mixed "outgoing propagation" + "stress propagates to borrowers" — ambiguous w.r.t. the canonical orientation invariant in the module docstring. Rewritten to: "K[i, j] = E[i, j] / Σ_j E[i, j]; per the canonical orientation invariant, K[i, j] is the share of bank i's total exposure concentrated in counterparty j — i.e. the fraction of i's claims at risk if j defaults". Removed "outgoing propagation" and "lender-to-borrower" language. FIX 5 — power-law tail adequacy POLICY HYBRID network_fitting.py now exposes a strict validation entry point: fit_power_law_validation(degrees, ...) with internal fail-closed bounds: - MIN_TAIL_SIZE_VALIDATION = 50 (CRLB-derived; module doc block shows the σ_α/α ≈ 0.085 calculation at α=2.5) - MIN_RELATIVE_SE_VALIDATION = 0.10 (Clauset-Shalizi-Newman 2009 fig. 3 PL-vs-exp AIC-Δ > 4 boundary) fit_power_law (exploratory) keeps min_relative_se opt-in. Two new tests: rejects n < 50, passes on n=2000 with σ_α/α ≤ 0.10. FIX 6 — SciPy pin VERIFIED pyproject.toml already lists scipy>=1.16.2 in the canonical dependency block. test_falsification.py's binom.ppf import is backed by a pinned dep. FIX 7 — claim-governance audit Forbidden-word grep across research/systemic_risk/*.md and *.py: \b(production-ready|production-grade|empirically established| trading edge|trading signal|predictive system| early-warning system|proven|confirmed)\b → 0 matches. ("validated" appears only as an enum tier name in PROTOCOL.md / VALIDATION.md status diagrams, allowed.) FIX 8 — edge-case test expansion Added: single-node graph, all-zero exposure matrix (empty graph), whitespace-only label, None label, omega-inf input, omega-zero- variance returns zero finite ω, validation-mode tail-size rejection, validation-mode pass at sufficient n. FIX 9 — reproducibility bundle DOCUMENTED PROTOCOL.md § 5 already lists the full RunManifest contract. LIMITATIONS.md flags what is NOT yet implemented (real-data ingest, walk-forward). FIX 10 — PR final status PRESERVED C-SYSRISK-PHASE remains HYPOTHESIS in CLAIMS.md. README, PROTOCOL.md, VALIDATION.md all preserve the HYPOTHESIS / INSTRUMENTATION status. Tests: 146 passing (+8 from 138). Quality: mypy --strict / ruff / black clean on the diff. Pre- existing 5 jax_engine mypy errors persist on origin/main; out of scope. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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…es + temporal-panel boundary (#564) Closes the post-merge canonical R&D review on PR #562: PR #562 TITLE RENAMED (post-merge edit via gh API) Old: "production-grade v2 ..." New: "R&D hypothesis instrument v2 — directed coupling, MLE BA fit, bootstrap-CI falsification" Rationale: per § 2 of the canonical checklist, "production-grade" is forbidden language until a real-data run + replication exist. GOVERNANCE GATES (new module governance.py) - assert_claim_tier(claimed, evidence) — refuses promotion - build_validation_readiness_report(...) — explicit per-axis flags - run_premerge_science_gate(docs_root, readiness) — composite verdict - FORBIDDEN_OVERCLAIM_TERMS — regex tuple Plus a real-module test that asserts research/systemic_risk/ itself passes the overclaim grep at HYPOTHESIS / INSTRUMENTED tier. Any future commit that introduces forbidden language fails CI. SCOPE-EXPLICIT FALSIFICATION ALIASES - run_score_level_falsification — alias of run_falsification with a name that makes the scope auditable in caller code. - run_end_to_end_falsification — NotImplementedError stub. Fails-closed until the empirical-data ingest and the composed null-audit orchestrator land. No partial pipeline can be misread as end-to-end evidence. TEMPORAL-PANEL BOUNDARY (new module temporal_panel.py) validate_temporal_exposure_panel(panels, node_labels) — fail-closed contract for the eventual end-to-end ingest. Enforces: - non-empty panel - strictly-increasing date keys (no duplicates) - per-snapshot squareness, finiteness, non-negativity - same node universe across snapshots (no silent entry/exit) - label-side contract identical to from_exposure_matrix VALIDATION-MODE BA FIT fit_barabasi_albert_validation_from_topology(topology) — strict wrapper enforcing both n_tail ≥ 50 AND σ_α/α ≤ 0.10 with no escape hatches. DOCS — score-level vs end-to-end boundary made explicit - README.md gains a top-level boundary block stating the executable falsification operates at score-series level and the full pipeline is not yet end-to-end executable. - PROTOCOL.md status string updated to "HYPOTHESIS / SCORE-LEVEL INSTRUMENTATION COMPLETE; END-TO-END VALIDATION PENDING". TESTS — 169 passing (+24 new) - test_governance.py: readiness profile derivation, claim-tier enforcement, overclaim grep on synthetic + real module trees, canonical forbidden-terms list. - test_temporal_panel.py: empty/duplicate/whitespace/None labels, size-mismatch, non-square, NaN, negative — every fail path. - test_falsification.py: scope-alias parity (run_score_level_* matches run_falsification on the same seed), end-to-end stub fails-closed. - test_network_fitting.py: BA validation wrapper rejects small topology, passes on n=3000 BA(m=3, seed=42) with auto-selected k_min ≈ 25, n_tail ≈ 56, σ_α/α ≈ 0.086. Quality - mypy --strict / ruff / black: clean on every new/modified file. - 5 pre-existing core/kuramoto/jax_engine errors persist on origin/main; out of scope. Tier: C-SYSRISK-PHASE remains HYPOTHESIS. Co-authored-by: Yaroslav Vasylenko <neuron7x@ukr.net> Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Summary
Block 2 of the 2026-05-08 PROTOCOL: FULL CLEANUP + QUALITY REWRITE. Closes three correctness defects in v1 (PR #557): silent symmetrisation that destroyed direction signal, missing CI on the verdict statistic, and FDR control where FWER is required.
What changed
Data layer
from_exposure_matrix(..., directed=True)is the new default — preserves asymmetric exposure structure (Bardoscia et al. 2021, Nat. Rev. Phys. 3: 490).directed=Falseretained for null baselines only.snapshot_datefor temporal pipelines (e-MID quarterly, BIS LBS).InterbankTopologyexposesis_symmetric,asymmetry_fraction,in_degree,out_degree,degree.Network layer (new
network_fitting.py)fit_barabasi_albertrecovers BA m from<k>/2instead of v1's hard-codedm=2.Coupling layer (new
coupling.py)omega_from_volatilityfirst-order intrinsic-frequency estimator.sakaguchi_alpha_zeroscaffolding for per-pair phase lag.Validation layer (
falsification.pyv2)CrisisOutcomeaddsauc_ci_low,auc_ci_high,p_bonferroni.auc_ci_low≤ 0.5.auc_ci_low≥ 0.70 AND p_BONF ≤ 0.01.Test plan
pytest tests/research/systemic_risk/: 90 passed (57 from v1 + 33 new)mypy --strictclean on all new/modified filesruff+blackcleanauc_ci_low≥ 0.70Tier
C-SYSRISK-PHASEremainsHYPOTHESIS. Promotion toMEASUREDrequiresHARD_PASSon ≥ 2 of {2008 GFC, 2011 Eurozone, 2023 SVB/CS} with real interbank exposure data.🤖 Generated with Claude Code