feat(aura-engine): monte_carlo family — McFamily over a seed set (C12 axis 4)

`monte_carlo(base_point, seeds, run_one) -> McFamily`, the Monte-Carlo
orchestration family: a fixed base point run over a seed set, each seed a
disjoint C1 realization. C12 axis 4 — Monte-Carlo IS a sweep over seeds, so it
reuses the disjoint-parallel executor rather than forking a run loop.

The reuse is realized by extracting the index-parallel core out of
`sweep_with_threads` into a `pub(crate) run_indexed<T>` (generic over the
per-job result, over a 0..n job range); `sweep_with_threads` becomes a thin
adapter over it. Behaviour-preserving (C1/C11/C23): the three pre-existing sweep
tests stay green — they ARE the proof the refactor preserved behaviour. A small
incidental win: the adapter consumes `points` via zip, dropping the old
per-point clone.

mc.rs adds McDraw{seed,report} / McFamily{draws,aggregate} (draws in seed-INPUT
order, independent of thread completion) / McAggregate (mean + p5/p25/p50/p75/p95
of all three run metrics — the V1 decision: covers every metric, not a "chosen"
one) / MetricStats, a private type-7 linear-interpolation `quantile`, and
`McAggregate::from_draws` (a pure post-run reduction recomputable from the
retained raw draws). Eager-agnostic firewall (#71): the API takes seeds + a
per-draw closure `Fn(u64,&[Scalar])->RunReport`, never a materialized stream Vec;
seed->Source construction stays a closure-body concern. An empty seed set is a
debug-asserted precondition, preserving the no-Result `-> McFamily` signature.

7 RED-first mc::tests pin: one draw per seed in input order; determinism across
1 vs 8 threads (C1); draw == an independent seeded run; distinct seeds perturb
the family; aggregate == from_draws(draws); type-7 quantile/mean math on a known
fixture; quantile endpoints + singleton. Workspace green, clippy -D warnings
clean, cargo doc clean.

refs #68

crates/aura-engine/src/lib.rs

@@ -37,6 +37,7 @@ mod blueprint;
 mod builder;
 mod graph_model;
 mod harness;
+mod mc;
 mod report;
 mod sweep;
 
@@ -51,6 +52,7 @@ pub use harness::{
 };
 pub use report::{f64_field, summarize, RunManifest, RunMetrics, RunReport};
 pub use sweep::{sweep, GridSpace, SweepError, SweepFamily, SweepPoint};
+pub use mc::{monte_carlo, McAggregate, McDraw, McFamily, MetricStats};
 // #29: re-export the core scalar vocabulary a Blueprint builder needs
 // (SourceSpec.kind is a ScalarKind; sources/Recorder columns are Scalar /
 // Firing / Timestamp) so a graph builder has one import surface, not two.

crates/aura-engine/src/mc.rs

@@ -0,0 +1,281 @@
+//! Monte-Carlo orchestration family (C12 axis 4): Monte-Carlo as a sweep over
+//! seeds. `monte_carlo(base_point, seeds, run_one)` runs a fixed base point over
+//! a seed set — each seed a disjoint C1 realization — and collects an
+//! [`McFamily`]: the per-seed [`McDraw`]s in seed-input order plus a stored
+//! [`McAggregate`] (mean + quantiles of all three run metrics across the
+//! realizations). It reuses the disjoint-parallel [`run_indexed`](crate::sweep)
+//! core `sweep` drives — the varying dimension is the *seed*, not a tuning param.
+//! Eager-agnostic (C12/#71): the API takes seeds + a per-draw closure, never a
+//! materialized stream `Vec`; the seed -> `Source` construction is a closure-body
+//! concern.
+
+use crate::sweep::run_indexed;
+use crate::{RunMetrics, RunReport, Scalar};
+
+/// One Monte-Carlo realization: the seed that drove it and the full `RunReport`.
+/// Self-describing, analog to [`SweepPoint`](crate::SweepPoint).
+#[derive(Clone, Debug, PartialEq)]
+pub struct McDraw {
+    pub seed: u64,
+    pub report: RunReport,
+}
+
+/// The result family of a Monte-Carlo run — one [`McDraw`] per seed, in
+/// seed-**input** order (independent of thread completion), plus the stored
+/// [`McAggregate`]. Analog to [`SweepFamily`](crate::SweepFamily).
+#[derive(Clone, Debug, PartialEq)]
+pub struct McFamily {
+    pub draws: Vec<McDraw>,
+    pub aggregate: McAggregate,
+}
+
+/// Distribution summary of all three run metrics across the realizations: covers
+/// every metric (not a single "chosen" one). A pure post-run reduction over the
+/// draws — stored for the common robustness case; custom statistics read the raw
+/// draws directly.
+#[derive(Clone, Debug, PartialEq)]
+pub struct McAggregate {
+    pub total_pips: MetricStats,
+    pub max_drawdown: MetricStats,
+    pub exposure_sign_flips: MetricStats,
+}
+
+/// Mean + a fixed quantile set of one metric across the realizations. `p50` is
+/// the median (the two coincide by definition), so "mean/median/quantiles" is
+/// `mean` + `p50` + the surrounding quantiles, with no redundant field.
+#[derive(Clone, Debug, PartialEq)]
+pub struct MetricStats {
+    pub mean: f64,
+    pub p5: f64,
+    pub p25: f64,
+    pub p50: f64, // == median
+    pub p75: f64,
+    pub p95: f64,
+}
+
+impl McAggregate {
+    /// Pure reduction over the realizations — recomputable from `draws` alone (it
+    /// is exactly what [`monte_carlo`] stored). `draws` must be non-empty (a
+    /// Monte-Carlo over zero realizations has no defined mean/quantile); on a
+    /// non-empty slice every field is finite.
+    pub fn from_draws(draws: &[McDraw]) -> McAggregate {
+        let pick = |f: fn(&RunMetrics) -> f64| -> MetricStats {
+            let mut xs: Vec<f64> = draws.iter().map(|d| f(&d.report.metrics)).collect();
+            xs.sort_by(|a, b| a.partial_cmp(b).expect("run metrics are finite"));
+            MetricStats {
+                mean: xs.iter().sum::<f64>() / xs.len() as f64,
+                p5: quantile(&xs, 0.05),
+                p25: quantile(&xs, 0.25),
+                p50: quantile(&xs, 0.50),
+                p75: quantile(&xs, 0.75),
+                p95: quantile(&xs, 0.95),
+            }
+        };
+        McAggregate {
+            total_pips: pick(|m| m.total_pips),
+            max_drawdown: pick(|m| m.max_drawdown),
+            exposure_sign_flips: pick(|m| m.exposure_sign_flips as f64),
+        }
+    }
+}
+
+/// Linear-interpolation quantile (the numpy/Excel "type 7" default) over a
+/// pre-sorted, non-empty slice. `p` in `[0, 1]`. `rank = p * (n - 1)`;
+/// interpolate between the two bracketing order statistics. `n == 1` returns the
+/// sole value.
+fn quantile(sorted: &[f64], p: f64) -> f64 {
+    let n = sorted.len();
+    if n == 1 {
+        return sorted[0];
+    }
+    let rank = p * (n - 1) as f64;
+    let lo = rank.floor() as usize;
+    let frac = rank - lo as f64;
+    if lo + 1 < n {
+        sorted[lo] + frac * (sorted[lo + 1] - sorted[lo])
+    } else {
+        sorted[n - 1]
+    }
+}
+
+/// Run `run_one(seed, base_point)` over every seed, disjointly in parallel (C1),
+/// and collect the family in seed-input order plus the aggregate. The varying
+/// dimension is the *seed* (C12 axis 4: MC = sweep over seeds), not a tuning
+/// param; `base_point` is constant across draws. Eager-agnostic: the seed ->
+/// `Source` construction lives inside `run_one`, never a materialized stream
+/// `Vec` in this API (#71).
+///
+/// Precondition: `seeds` is non-empty (a Monte-Carlo over zero realizations has
+/// no defined aggregate). A `debug_assert!` guards it; the `-> McFamily`
+/// signature is preserved (no `Result`), matching [`sweep`](crate::sweep).
+pub fn monte_carlo<F>(base_point: &[Scalar], seeds: &[u64], run_one: F) -> McFamily
+where
+    F: Fn(u64, &[Scalar]) -> RunReport + Sync,
+{
+    let nthreads = std::thread::available_parallelism().map(|n| n.get()).unwrap_or(1);
+    monte_carlo_with_threads(base_point, seeds, nthreads, run_one)
+}
+
+/// The thread-count-explicit core of [`monte_carlo`]. Module-private: the public
+/// `monte_carlo` derives the count, while the tests drive it at 1 and at N to pin
+/// determinism under concurrency (C1). A thin adapter over
+/// [`run_indexed`](crate::sweep): it runs each seed disjointly and zips the
+/// reports back onto their seeds in seed-input order.
+fn monte_carlo_with_threads<F>(
+    base_point: &[Scalar],
+    seeds: &[u64],
+    nthreads: usize,
+    run_one: F,
+) -> McFamily
+where
+    F: Fn(u64, &[Scalar]) -> RunReport + Sync,
+{
+    debug_assert!(!seeds.is_empty(), "monte_carlo requires a non-empty seed set");
+    let reports = run_indexed(seeds.len(), nthreads, |i| run_one(seeds[i], base_point));
+    let draws: Vec<McDraw> = seeds
+        .iter()
+        .zip(reports)
+        .map(|(&seed, report)| McDraw { seed, report })
+        .collect();
+    let aggregate = McAggregate::from_draws(&draws);
+    McFamily { draws, aggregate }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::test_fixtures::composite_sma_cross_harness;
+    use crate::{f64_field, summarize, RunManifest, SyntheticSpec, Timestamp};
+    use aura_core::Scalar;
+
+    /// Build + bootstrap + run + drain + summarize one (seed, point) into a
+    /// `RunReport`. A free `fn` (Copy + Sync) so it serves as the `monte_carlo`
+    /// closure AND a direct reference for the "draw == independent run"
+    /// comparison. The stream is generated from `seed` (seed-as-input, #66), and
+    /// `seed` is recorded into the manifest.
+    fn run_draw(seed: u64, point: &[Scalar]) -> RunReport {
+        let (bp, rx_eq, rx_ex) = composite_sma_cross_harness();
+        let mut h = bp
+            .bootstrap_with_params(point.to_vec())
+            .expect("base point is kind-checked against param_space");
+        let spec = SyntheticSpec { start: 1.0, len: 64, step: 1 };
+        let window = (Timestamp(1), Timestamp((spec.len as i64 - 1) * spec.step + 1));
+        h.run(vec![Box::new(spec.source(seed))]);
+        let equity = f64_field(&rx_eq.try_iter().collect::<Vec<_>>(), 0);
+        let exposure = f64_field(&rx_ex.try_iter().collect::<Vec<_>>(), 0);
+        RunReport {
+            manifest: RunManifest {
+                commit: "test".to_string(),
+                params: Vec::new(),
+                window,
+                seed,
+                broker: "test".to_string(),
+            },
+            metrics: summarize(&equity, &exposure),
+        }
+    }
+
+    fn base_point() -> Vec<Scalar> {
+        // matches the composite_sma_cross param_space order: fast, slow, scale
+        vec![Scalar::I64(2), Scalar::I64(4), Scalar::F64(0.5)]
+    }
+
+    fn draws_with_metrics(values: &[f64]) -> Vec<McDraw> {
+        values
+            .iter()
+            .enumerate()
+            .map(|(i, &v)| McDraw {
+                seed: i as u64,
+                report: RunReport {
+                    manifest: RunManifest {
+                        commit: "t".to_string(),
+                        params: Vec::new(),
+                        window: (Timestamp(0), Timestamp(0)),
+                        seed: i as u64,
+                        broker: "t".to_string(),
+                    },
+                    metrics: RunMetrics {
+                        total_pips: v,
+                        max_drawdown: v,
+                        exposure_sign_flips: v as u64,
+                    },
+                },
+            })
+            .collect()
+    }
+
+    #[test]
+    fn monte_carlo_runs_one_draw_per_seed_in_input_order() {
+        // spec §Testing 1: N seeds -> N draws, seeds carried in INPUT order.
+        let family = monte_carlo(&base_point(), &[1, 2, 3], run_draw);
+        assert_eq!(family.draws.len(), 3);
+        assert_eq!(
+            family.draws.iter().map(|d| d.seed).collect::<Vec<_>>(),
+            vec![1, 2, 3],
+        );
+    }
+
+    #[test]
+    fn family_is_deterministic_across_thread_counts() {
+        // spec §Testing 2: order = seed input, not completion (C1).
+        let point = base_point();
+        let seeds: Vec<u64> = (1..=8).collect();
+        let one = monte_carlo_with_threads(&point, &seeds, 1, run_draw);
+        let many = monte_carlo_with_threads(&point, &seeds, 8, run_draw);
+        assert_eq!(one, many);
+        assert_eq!(one, monte_carlo(&point, &seeds, run_draw));
+    }
+
+    #[test]
+    fn draw_equals_independent_seeded_run() {
+        // spec §Testing 3: each draw == an independent run of that (seed, point)
+        // — MC adds enumeration + execution, never a metrics change (C1).
+        let point = base_point();
+        let family = monte_carlo(&point, &[10, 11, 12], run_draw);
+        for draw in &family.draws {
+            assert_eq!(draw.report, run_draw(draw.seed, &point));
+            assert!(draw.report.metrics.total_pips.is_finite());
+        }
+    }
+
+    #[test]
+    fn distinct_seeds_produce_distinct_draws() {
+        // spec §Testing 4: the seed actually perturbs each run — the family does
+        // not collapse to one metric.
+        let family = monte_carlo(&base_point(), &[1, 2, 3, 4, 5], run_draw);
+        let first = family.draws[0].report.metrics.total_pips;
+        assert!(
+            family.draws.iter().any(|d| d.report.metrics.total_pips != first),
+            "a multi-seed family must not collapse to one metric",
+        );
+    }
+
+    #[test]
+    fn aggregate_is_a_pure_reduction() {
+        // spec §Testing 5: the stored aggregate is recomputable from the draws.
+        let family = monte_carlo(&base_point(), &[1, 2, 3, 4], run_draw);
+        assert_eq!(McAggregate::from_draws(&family.draws), family.aggregate);
+    }
+
+    #[test]
+    fn aggregate_stats_on_known_fixture() {
+        // spec §Testing 6: type-7 quantile + mean over known metric values
+        // [0,1,2,3,4]: mean=2.0, p50=2.0, p5≈0.2, p95≈3.8.
+        let agg = McAggregate::from_draws(&draws_with_metrics(&[0.0, 1.0, 2.0, 3.0, 4.0]));
+        assert_eq!(agg.total_pips.mean, 2.0);
+        assert_eq!(agg.total_pips.p50, 2.0);
+        assert!((agg.total_pips.p5 - 0.2).abs() < 1e-9, "p5 = {}", agg.total_pips.p5);
+        assert!((agg.total_pips.p95 - 3.8).abs() < 1e-9, "p95 = {}", agg.total_pips.p95);
+    }
+
+    #[test]
+    fn quantile_endpoints_and_singleton() {
+        // spec §Testing 7: singleton returns the sole value; p=0 -> min, p=1 -> max.
+        assert_eq!(quantile(&[42.0], 0.0), 42.0);
+        assert_eq!(quantile(&[42.0], 0.5), 42.0);
+        assert_eq!(quantile(&[42.0], 1.0), 42.0);
+        let xs = [1.0, 2.0, 3.0, 4.0, 5.0];
+        assert_eq!(quantile(&xs, 0.0), 1.0);
+        assert_eq!(quantile(&xs, 1.0), 5.0);
+    }
+}

crates/aura-engine/src/sweep.rs

@@ -121,34 +121,34 @@ where
     sweep_with_threads(space, nthreads, run_one)
 }
 
-/// The thread-count-explicit core of [`sweep`]. Module-private: the public
-/// `sweep` derives the count, while the tests drive it at 1 and at N to pin
-/// determinism under concurrency (C1). Workers pull point indices from a shared
-/// atomic cursor (work-stealing load-balances uneven per-point cost); each tags
-/// its result with the point index, and the family is assembled by sorting on
-/// that index after the scope joins — so the order is the enumeration order, not
-/// the completion order. Only the cursor is shared; the results side is lock-free.
-fn sweep_with_threads<F>(space: &GridSpace, nthreads: usize, run_one: F) -> SweepFamily
+/// Run `n` disjoint jobs in parallel and collect their results in **job-index
+/// order** — the shared disjoint-parallel core both `sweep` (over grid points)
+/// and `monte_carlo` (over seeds) drive (C1: order is the input order, not the
+/// completion order). Workers pull job indices from a shared atomic cursor
+/// (work-stealing load-balances uneven per-job cost); each tags its result with
+/// the index, and the results are sorted on that index after the scope joins.
+/// Only the cursor is shared; the results side is lock-free. `nthreads` is
+/// clamped to `[1, n.max(1)]` (a 0-job call yields an empty vec; a 0 thread
+/// count coerces to 1).
+pub(crate) fn run_indexed<T, F>(n: usize, nthreads: usize, run_one: F) -> Vec<T>
 where
-    F: Fn(&[Scalar]) -> RunReport + Sync,
+    T: Send,
+    F: Fn(usize) -> T + Sync,
 {
-    let points = space.points();
-    // `points.len() >= 1` always (GridSpace rejects empty axes), so the clamp
-    // lower bound never exceeds the upper; this also coerces a 0 to 1 worker.
-    let nthreads = nthreads.clamp(1, points.len());
+    let nthreads = nthreads.clamp(1, n.max(1));
     let cursor = AtomicUsize::new(0);
 
-    let mut results: Vec<(usize, RunReport)> = std::thread::scope(|scope| {
+    let mut results: Vec<(usize, T)> = std::thread::scope(|scope| {
         let handles: Vec<_> = (0..nthreads)
             .map(|_| {
                 scope.spawn(|| {
-                    let mut local: Vec<(usize, RunReport)> = Vec::new();
+                    let mut local: Vec<(usize, T)> = Vec::new();
                     loop {
                         let i = cursor.fetch_add(1, Ordering::Relaxed);
-                        if i >= points.len() {
+                        if i >= n {
                             break;
                         }
-                        local.push((i, run_one(&points[i])));
+                        local.push((i, run_one(i)));
                     }
                     local
                 })
@@ -158,10 +158,25 @@ where
     });
 
     results.sort_by_key(|&(i, _)| i);
+    results.into_iter().map(|(_, t)| t).collect()
+}
+
+/// The thread-count-explicit core of [`sweep`]. Module-private: the public
+/// `sweep` derives the count, while the tests drive it at 1 and at N to pin
+/// determinism under concurrency (C1). A thin adapter over [`run_indexed`]: it
+/// enumerates the grid points, runs each disjointly, and zips the reports back
+/// onto their points in enumeration (odometer) order.
+fn sweep_with_threads<F>(space: &GridSpace, nthreads: usize, run_one: F) -> SweepFamily
+where
+    F: Fn(&[Scalar]) -> RunReport + Sync,
+{
+    let points = space.points();
+    let reports = run_indexed(points.len(), nthreads, |i| run_one(&points[i]));
     SweepFamily {
-        points: results
+        points: points
             .into_iter()
-            .map(|(i, report)| SweepPoint { params: points[i].clone(), report })
+            .zip(reports)
+            .map(|(params, report)| SweepPoint { params, report })
             .collect(),
     }
 }