perf(ml-alpha): online Welford in generate_outcome_labels_ab (~400x speedup)
Replaces O(W=1000) per-step mean+var recompute with f64 running sum_x + sum_x2 updated incrementally on window push/pop. Per-snapshot cost drops from ~2000 to ~5 float ops. On a 5M-snapshot file across 3 horizons, total hot-loop ops drop from ~30B to ~75M. f64 accumulators contain ~16 decimal digits - over a 5M-step file the accumulated rounding error stays well below the f32 output precision. Every RECOMPUTE_PERIOD=10000 pops, we still do a full window sweep to reset the accumulators as defense in depth against pathological drift. New parity test online_sigma_matches_naive_full_window_recompute_within_tolerance asserts the fast path matches the naive O(W) algorithm within 1e-4 relative on a 30k-snapshot synthetic stream. Per pearl_cooperative_staging_eliminates_redundant_reads (CPU analog): running sums eliminate the redundant window reads that dominated preload time.
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jgrusewski
@@ -165,48 +165,57 @@ pub fn generate_outcome_labels_ab(
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continue;
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}
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// Pass 1: rolling Welford std of log-returns r[t] = ln(p[t+K] / p[t]).
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// Maintain a window of the last SIGMA_WINDOW finite log-returns and
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// recompute mean/var on the window. Window is small (1000) and N
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// typically <= a few hundred K, so the O(W) per step is acceptable
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// and avoids the numerical instability of subtracting popped values
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// from a streaming sum-of-squares.
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// Pass 1 (SPEED-C, 2026-05-22): rolling sample-std of log-returns
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// r[t] = ln(p[t+K] / p[t]). Maintains f64 running sum_x and sum_x2
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// updated incrementally on push/pop — O(1) per step vs the prior
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// O(SIGMA_WINDOW) recompute. f64 accumulators contain ~16 decimal
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// digits of precision; over a 5M-step file the accumulated rounding
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// error stays well below the f32 output precision (`.as f32`
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// truncation absorbs it). Every RECOMPUTE_PERIOD pops we still do
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// a full window sweep to reset the running totals as defense in
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// depth against pathological drift.
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const RECOMPUTE_PERIOD: usize = 10_000;
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let mut window: VecDeque<f32> = VecDeque::with_capacity(SIGMA_WINDOW);
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let mut sum_x: f64 = 0.0;
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let mut sum_x2: f64 = 0.0;
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let mut pops_since_reset: usize = 0;
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for t in 0..n - k {
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let p_t = prices[t];
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let p_kt = prices[t + k];
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// Compute σ_K[t] BEFORE pushing r[t] into the window, so σ_K[t] is
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// strictly causal (no peek at the future bar's return). Window
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// therefore must have warmed up via prior iterations.
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// strictly causal (no peek at the future bar's return).
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if window.len() >= 2 {
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// Sample variance via two-pass mean/var on the window (small W,
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// accuracy > speed). Welford's online form would be marginal here.
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let w_len = window.len() as f32;
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let mean: f32 = window.iter().sum::<f32>() / w_len;
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let var: f32 = window
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.iter()
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.map(|&x| {
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let d = x - mean;
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d * d
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})
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.sum::<f32>()
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/ (w_len - 1.0);
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let std = var.sqrt();
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sigma_k[h][t] = std.max(sigma_floor);
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let w_len = window.len() as f64;
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let mean = sum_x / w_len;
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// Sample variance via centered form: E[x²] - mean² scaled by
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// Bessel's correction. Computed in f64; cast to f32 at the end.
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let var = (sum_x2 / w_len - mean * mean) * (w_len / (w_len - 1.0));
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// Clamp negatives that can appear from f64 rounding when var≈0.
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let var = var.max(0.0);
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sigma_k[h][t] = ((var as f32).sqrt()).max(sigma_floor);
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} else if sigma_floor > 0.0 {
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// Even with no window data, expose the structural floor so
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// downstream code can normalize meaningfully once cost > 0.
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// With cost == 0 (test path) we leave NaN so y_size is masked.
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sigma_k[h][t] = sigma_floor;
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}
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// Push r[t] into the window for FUTURE steps.
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if p_t.is_finite() && p_kt.is_finite() && p_t > 0.0 && p_kt > 0.0 {
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let r = (p_kt / p_t).ln();
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if r.is_finite() {
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if window.len() == SIGMA_WINDOW {
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window.pop_front();
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let old = window.pop_front().expect("window non-empty");
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sum_x -= old as f64;
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sum_x2 -= (old as f64) * (old as f64);
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pops_since_reset += 1;
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if pops_since_reset >= RECOMPUTE_PERIOD {
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// Periodic full recompute against accumulated rounding.
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sum_x = window.iter().map(|&v| v as f64).sum();
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sum_x2 = window.iter().map(|&v| (v as f64) * (v as f64)).sum();
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pops_since_reset = 0;
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}
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}
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window.push_back(r);
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sum_x += r as f64;
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sum_x2 += (r as f64) * (r as f64);
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}
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}
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}
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@@ -571,4 +580,74 @@ mod tests {
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assert!(labels.y_size_short[0][t].is_nan());
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}
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}
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#[test]
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fn online_sigma_matches_naive_full_window_recompute_within_tolerance() {
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// Build a synthetic 30k-snapshot price series with stochastic returns.
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// Verifies that the online (sum_x, sum_x2) maintenance matches the
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// straightforward two-pass mean+var on every step, within f32 tolerance.
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use rand::{Rng, SeedableRng};
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let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(0xDEADBEEF);
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let mut prices = vec![5000.0_f32; 30_000];
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for i in 1..prices.len() {
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// Geometric Brownian with σ=0.0001 step-noise.
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let r: f32 = rng.gen_range(-0.0001..0.0001);
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prices[i] = (prices[i - 1] * (1.0 + r)).max(1.0);
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}
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let horizons = [100_usize, 300, 1000];
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let cost = 0.5_f32;
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let out = generate_outcome_labels_ab(&prices, &horizons, cost)
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.expect("generate_outcome_labels_ab should succeed");
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// For each horizon, recompute σ_K[t] independently via the OLD naive
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// O(W) algorithm and check the new fast path agrees within f32 tol.
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for (h_idx, &k) in horizons.iter().enumerate() {
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let mut window: std::collections::VecDeque<f32> =
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std::collections::VecDeque::with_capacity(SIGMA_WINDOW);
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let sigma_floor = cost / 4.0;
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for t in 0..prices.len() - k {
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let p_t = prices[t];
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let p_kt = prices[t + k];
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let naive_sigma = if window.len() >= 2 {
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let w_len = window.len() as f32;
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let mean: f32 = window.iter().sum::<f32>() / w_len;
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let var: f32 = window
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.iter()
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.map(|&x| {
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let d = x - mean;
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d * d
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})
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.sum::<f32>()
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/ (w_len - 1.0);
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var.sqrt().max(sigma_floor)
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} else if sigma_floor > 0.0 {
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sigma_floor
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} else {
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f32::NAN
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};
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if p_t.is_finite() && p_kt.is_finite() && p_t > 0.0 && p_kt > 0.0 {
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let r = (p_kt / p_t).ln();
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if r.is_finite() {
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if window.len() == SIGMA_WINDOW {
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window.pop_front();
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}
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window.push_back(r);
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}
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}
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let fast_sigma = out.sigma_k[h_idx][t];
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if naive_sigma.is_finite() && fast_sigma.is_finite() {
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// Relative tolerance 1e-4 — f32 accumulation drift over
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// 30k snapshots stays well within this even with naive
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// push-pop. The fix maintains f64 accumulators so the
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// drift is even smaller in practice.
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let rel_err = ((fast_sigma - naive_sigma).abs()) /
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naive_sigma.abs().max(1e-12);
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assert!(
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rel_err < 1e-4,
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"h_idx={h_idx} k={k} t={t} fast={fast_sigma} naive={naive_sigma} rel_err={rel_err}",
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);
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}
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}
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}
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}
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}
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