Four generators, one shared parameter registry. Generating strategy candidates is parameter-space sampling over a typed registry, not machine learning. Grid search takes the full Cartesian product: complete but combinatorially explosive (10 params times 5 values equals 9.7M candidates), so large ranges get collapsed to a min/default/max set. Random sampling draws with replacement, seeded, default 50 samples. The "evolutionary" generator has no fitness function or selection; it mutates each member randomly each generation, exploring neighborhoods rather than climbing toward better regions. The composite path assembles strategy skeletons from a hand-curated table of 13 indicators, producing a documented pool of 854 skeletons checked against the live component registry before acceptance.

Config hashing dedupes within and across sessions. Every accepted config gets a SHA-256 hash: a canonical, order-independent identity, not a security hash. It's used for in-session dedup and, separately, for cross-session dedup via a persistent cache that survives across processes and fails open (a broken cache file logs a warning and starts empty rather than crashing generation). One real gap: the persistent cache is a standalone facility the engine itself never calls; only the in-session set gets checked automatically.

A strict survivor funnel: cheap, intermediate, walk-forward, Monte Carlo. Each stage only receives the prior stage's survivors. "Cheap" and "intermediate" aren't separate stage types. They're two configurations of the same simulation stage: one with a short window and loose thresholds, one with the full window and real thresholds. The ordering is deliberate, cheapest and most-discriminating test first, so a badly broken candidate dies early instead of consuming expensive compute it was never going to survive.

Eight checks decide pass/fail; a separate score decides rank. Thresholds are inclusive, and all checks are evaluated (not short-circuited), so a rejected candidate returns its full failure profile. The eight core checks cover Sharpe, drawdown, trade count, consistency, profit factor, win rate, return variance, and total return; only three are meaningfully active by default. Filtering and ranking are separate steps. Ranking only orders strategies that already passed every filter, so a high score never compensates for a failed gate.

**Walk-forward tests a fixed config across rolling time windows. A fixed, un-retrained strategy configuration has to keep clearing the bar as both the training window and the following unseen window roll forward through time. The committed config produces 9 folds. A fold passes only if the config clears filters on both windows, and test simulation is skipped entirely after a train failure. The 6-of-9 pass requirement is a deliberate tightening, not a framework default. Like the other stages, this one fails open: if every fold's train run returns nothing, the unfiltered survivor set passes through rather than being eliminated.

Monte Carlo varies the execution seed, not the time period. It holds the strategy, data, and dates fixed and instead varies the random seed driving execution realism (rejection, partial fills, slippage), testing whether a strategy's edge depends on one favorable draw. Filtering happens per-run, not on aggregate means, because a mean Sharpe of 1.2 can hide 40% of runs going sharply negative. One confirmed gap: the pass-rate denominator counts only completed runs rather than the configured total, so missing simulations make survival artificially easier.

A 9-candidate sweep can mean 630 simulations. The committed sweep covers 9 configurations across all four stages and 5 symbols, small enough to inspect while still exercising the full pipeline. A full survivor can require up to 70 simulations (cheap plus intermediate plus up to 18 walk-forward runs plus 50 Monte Carlo trials). The larger default pools stay affordable only because the funnel guarantees most candidates never reach the expensive stages.

A shared seed service keeps every strategy's randomness independent. DeterministicSeedService hashes each unit's full logical identity (strategy, config, stage, fold, trial) into its seed, so two different strategies never reuse the same random stream. The key safety property is the identity-mismatch guard: if a checkpoint's ID matches but its full identity doesn't, the service raises an error instead of resuming with mixed lineage. Execution runs on a thread pool, with results reordered by a stable sort key so thread-completion order never affects reported results.

Research hands off to governance with no human gate, by design. Only complete pipeline survivors get auto-inserted into governance as `approved_research`, which is safe because that state is non-executing and non-capital: eligible for review, not authorized to trade. Every later promotion still runs through governance's own machinery. The insertion is idempotent and never resets an existing row, so rerunning an experiment can't accidentally undo a prior demotion.