Daniel S. E. Hill

Broker-agnostic trade execution layer: signal validation chain, pre-trade risk gateway with registerable checks, order/fill/position lifecycle, a seeded simulated broker, and a three-state outcome-level circuit breaker with drawdown-scaled position sizing. Contains no strategy by construction: signals arrive as injected objects, so the engine can be tested without one.

LLM provider failover: same model, same history, next provider when one fails. Per-gateway circuit breakers with exponential cooldown, an error taxonomy separating gateway-level from model-level failures, SSE stream-restart semantics, and an OpenAI-compatible proxy. Adding a provider is one YAML entry.

Hot/warm/cold object store with per-payload codec selection and a FastAPI control plane. Benchmarked with the causes written up, including a 288× store/retrieve asymmetry traced to triple compression in auto mode. The Rust core ships as source, documented as never compiled; no claim is made for it.

A Tkinter desktop app and a dependency-free offline-first PWA writing one dataset with no server between them. Devices converge through per-item last-write-wins merging and deletion tombstones; the merge is order-independent and idempotent, and the tests prove it.

Dependency-free AST-based cyclomatic complexity analyser: per-function scoring, risk banding, JSON/Markdown reports, and trend tracking so complexity drift is visible across runs. Documents exactly where it deviates from strict McCabe, and ships an analysis of its own source as the worked example.

Data standardization plus a four-tier degradation ladder: retry, then a fallback calculation, then a simplified operation, then an emergency default. Fallbacks are domain code registered per operation type. Includes quality scoring and an IQR outlier budget that rejects bad data rather than silently repairing it.

Runs the range-based volatility estimators (Parkinson, Garman-Klass, Rogers-Satchell, Yang-Zhang) side by side so the numbers can disagree visibly. Adds adaptive Bollinger, ATR and Keltner analysis, and diagnostics for when GARCH-family models are, and are not, appropriate. A 215-method suite.

Advanced statistics for noisy series: L-moments, robust statistics, bootstrap analysis, extreme-value analysis, nonparametric tests, and a Bayesian block: conjugate posteriors, change-point detection, Bayes-factor model comparison, and posterior predictive checks.

An autonomous mathematics research engine. Seven pluggable modules (Collatz, Goldbach, prime distribution, graph theory, symbolic regression, cellular automata, dynamical systems) run under expected-value task scheduling with psutil-enforced compute budgets. An independent verification engine re-derives findings rather than trusting the module that produced them. Ran unattended for 3 days / 107 research cycles.

A binary-lattice physics laboratory: measured emergent inverse-square attraction (exponent 2.09), mass proportionality r = 0.891, FFT-accelerated long-range solves, a ring-decomposed retarded potential (causality at one cell per tick, static-grid equivalence to 1e-14), and a THEORY_CONFLICTS.md recording where the results contradict the theory.

From-scratch two-state Gaussian HMM, a strictly walk-forward regime gate with bit-exact no-lookahead tests, and a Politis–Romano stationary bootstrap for Sharpe confidence intervals. Ships the negative result it was built to find: every gated variant underperformed the ungated strategy (+0.33 vs +0.21/+0.20/+0.24).