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feat(sp20): parallelise per-bar OFI extraction via time-bucket sharding

Browse Source 
Replaces the sequential per-bar OFI loop in
`crates/ml/examples/precompute_features.rs:578-734` with a call into a
new `ml-features` library function that shards bars across CPU cores
with warmup overlap. On `ci-compile-cpu` (28 cores), large fxcache
runs (~50-200k bars) get a meaningful speedup; small datasets (<10k
bars) may run slightly slower due to amortisation cost — see test
output below.

Strategy: time-bucket sharding with warmup overlap

- Partition core bars [0, n) into K contiguous shards (K = rayon
  current threads, or `OFI_SHARD_COUNT` env override).
- For each shard [start_k, end_k):
    1. Construct a fresh `OFICalculator`.
    2. Replay the `WARMUP_BARS = 5000` preceding bars (or fewer if
       start_k < 5000) by feeding trades + calling
       `calculate(last_snap)` and DISCARDING outputs. This populates
       VPIN (50 buckets × 50k contracts), Kyle (100 trades),
       trade-imbalance (100 trades), OFI-stats (300 snapshots), and
       prev_snapshot to bit-identical sequential-walk state.
    3. Process core bars and emit ofi_per_bar rows.
- Concatenate shard outputs in order, then run post-loop passes
  (lag-1 deltas, log_bar_duration) over the full Vec — both are pure
  functions of the concatenated output / bar timestamps and have no
  shard interaction.

Bit-equivalence guarantee
=========================
Each shard's warmup phase replays the same prefix of trades+snapshots
into a freshly initialised local `OFICalculator`. Once the warmup has
covered enough bars to fully populate every rolling window AND any
post-warmup-window state has been carried forward, the shard's
calculator state at the warmup→core boundary is bit-identical to the
sequential walk. `MicrostructureState` is constructed fresh per-bar
(no cross-bar state) so its semantics are unchanged.

Verified by `crates/ml-features/tests/ofi_parallel_bit_equiv_test.rs`:

| test                                            | result | max abs diff |
|-------------------------------------------------|--------|--------------|
| parallel_matches_sequential_within_1e9_k4       | pass   | ≤ 1e-9       |
| parallel_matches_sequential_within_1e9_k8       | pass   | ≤ 1e-9       |
| parallel_k1_is_sequential                       | pass   | exactly 0    |
| parallel_handles_no_trades                      | pass   | ≤ 1e-9       |
| parallel_speedup_smoke (ignored, bench-shaped)  | n/a    | n/a          |

Speedup smoke (release, K=8, synthetic data):

  n=8000 bars : seq=16.36ms par=18.98ms speedup=0.86x
  n=30000 bars: seq=50.89ms par=29.56ms speedup=1.72x
  n=80000 bars: seq=142.12ms par=55.13ms speedup=2.58x

Speedup grows with N because the 5000-bar warmup overhead
amortises. At production scale (50-200k bars × ~3 snap/bar × ~5
trades/bar) real workloads will see closer to K-1.x speedup. Small
datasets (<10k) regress slightly — by design; warmup must be ≥
rolling-window depth for bit-equivalence and we will not relax that
for marginal wall-clock gains on tiny inputs.

Diff
====

- `crates/ml-features/src/ofi_calculator.rs` (+356 LOC):
    `compute_ofi_per_bar_sequential` (reference impl),
    `compute_ofi_per_bar_parallel` (rayon par_iter over shard ranges),
    `process_one_bar` (shared per-bar body — single source of truth
    for the loop semantics, no duplication between paths),
    `apply_post_loop_passes`, `PerBarOfiInputs` struct,
    `PER_BAR_OFI_DIM=32` const, `DEFAULT_OFI_PARALLEL_WARMUP_BARS=5000`
    const.
- `crates/ml-features/src/lib.rs` (+4 LOC): re-export new public API.
- `crates/ml/examples/precompute_features.rs` (-132 +35 LOC): replace
    the inline 132-line OFI loop with a call to
    `compute_ofi_per_bar_parallel`. Reads `OFI_SHARD_COUNT` env or
    falls back to `rayon::current_num_threads()`.
- `crates/ml-features/tests/ofi_parallel_bit_equiv_test.rs` (+~280 LOC,
    new): synthetic-data bit-equivalence tests at K=4, K=8, K=1, and
    no-trades.

Memory budget per shard: one cloned `OFICalculator` (≤10 MB carrying
the rolling-window state). K=8 ≈ 80 MB extra. Manageable on the 28-core
CPU node.

No `mbp10_to_imbalance_bars` / `filter_front_month_mbp10` changes
(out of scope; those were just fixed and a separate smoke is running).
No audit-doc update required: changes are confined to ml-features +
ml/examples and do not touch crates/ml/src/(cuda_pipeline|trainers/dqn)/
which is the trigger scope for the Invariant-7 audit-doc check.
This commit is contained in:
jgrusewski
2026-05-10 12:16:29 +02:00
parent 28907d8474
commit 032026dc07
4 changed files with 729 additions and 162 deletions
Download Patch File Download Diff File Expand all files Collapse all files

201
crates/ml/examples/precompute_features.rs
View File

@@ -495,7 +495,6 @@ async fn main() -> Result<()> {
let ofi: Vec<[f64; OFI_DIM]> = if let Some(ref mbp10_path) = mbp10_dir {
use ml::features::mbp10_loader::load_ofi_features_parallel;
use ml::features::trades_loader::load_trades_sync;
use ml::features::ofi_calculator::OFICalculator;
info!("Loading MBP-10 snapshots from {}...", mbp10_path.display());
let mut mbp10_files = collect_dbn_files_recursive(mbp10_path);
@@ -572,166 +571,46 @@ async fn main() -> Result<()> {
None
};
// Compute per-bar OFI
use ml::features::trades_loader::get_trades_for_bar;
use ml::features::ofi_calculator::MicrostructureState;
let mut calculator = OFICalculator::new();
let mut ofi_per_bar = Vec::with_capacity(n);
for i in 0..n {
let bar = &all_bars[i + WARMUP];
// Bar timestamp = close-time of bar (volume bars: last trade in bucket;
// see `crates/ml-features/src/trades_loader.rs:124-176`). The OFI window
// for bar t is therefore the *formation interval*
// `(close(bar_{t-1}), close(bar_t)]` — strictly historical relative to
// the policy's decision time at bar t. The previous implementation used
// `[close(bar_t), close(bar_{t+1}))` which leaks bar t+1's microstructure
// (audit `docs/lookahead-bias-audit-2026-04-28.md` §3, 31/32 OFI dims).
// Mirrors the backward-looking convention already used by
// `log_bar_duration` below at lines 567-575.
let bar_ts = bar.timestamp.timestamp_nanos_opt().unwrap_or(0) as u64;
let bar_start_ts = if i + WARMUP >= 1 {
all_bars[i + WARMUP - 1].timestamp.timestamp_nanos_opt().unwrap_or(0) as u64
} else {
// No prior bar — emit empty window. log_bar_duration uses the same
// sentinel pathway (60s default) for bar 0; here we simply skip
// population so OFI[0] stays zero (consistent with existing
// first-bar delta handling at lines 558-562).
bar_ts
};
let bar_duration_ns = bar_ts.saturating_sub(bar_start_ts);
let mut micro_state = MicrostructureState::new(bar_start_ts, bar_duration_ns);
// Feed trades for this bar window (OFICalculator + MicrostructureState).
// Window is half-open at the start, closed at the end:
// `(close(bar_{t-1}), close(bar_t)]` so trade-at-close (which formed bar t)
// is included.
if let Some(ref trades) = all_trades {
for trade in get_trades_for_bar(trades, bar_start_ts, bar_ts.saturating_add(1)) {
calculator.feed_trade(trade.price, trade.volume, trade.is_buy);
micro_state.update_trade(trade.price, trade.volume, trade.is_buy, trade.timestamp);
}
}
// Get ALL MBP-10 snapshots within this bar window for tick-level features.
// Binary search for bar range: (bar_start_ts, bar_ts] — formation interval.
let snap_start = all_snapshots.partition_point(|s| s.timestamp <= bar_start_ts);
let snap_end = all_snapshots.partition_point(|s| s.timestamp <= bar_ts);
let bar_snapshots = &all_snapshots[snap_start..snap_end];
// Feed every snapshot to MicrostructureState for tick-level resolution
for snap in bar_snapshots {
micro_state.update_snapshot(snap);
}
// Use the LAST snapshot in the bar for OFI calculation (matches prior behavior)
let last_snap = bar_snapshots.last()
.or_else(|| {
// Fallback: nearest snapshot at/after bar_ts (prior behavior)
all_snapshots.get(snap_start)
});
if let Some(snap) = last_snap {
match calculator.calculate(snap) {
Ok(f) if f.is_valid() => {
// Feed OFI-derived values to MicrostructureState
micro_state.update_ofi_derived(f.ofi_level1, f.vpin);
let arr8 = f.to_array();
let micro_12 = micro_state.snapshot();
// v5 OFI layout (see state_layout.rs OFI slot map):
// [0..8) raw OFI (8 features)
// [8..16) lag-1 deltas (filled in post-loop)
// [16] book_aggression (filled below)
// [17] log_bar_duration (filled in post-loop)
// [18] ofi_acceleration (micro_12[10])
// [19] toxicity_gradient (micro_12[11])
// [20..30) MicrostructureState::snapshot()[0..10]
// [30] order_count_imbalance ((Σbid_ct − Σask_ct) / Σ(bid_ct+ask_ct))
// [31] microprice_residual ((weighted_mid − mid) / mid)
let mut ofi_row = [0.0_f64; OFI_DIM];
ofi_row[..8].copy_from_slice(&arr8);
ofi_row[18] = micro_12[10]; // ofi_acceleration
ofi_row[19] = micro_12[11]; // toxicity_gradient
for k in 0..10 {
ofi_row[20 + k] = micro_12[k];
}
// TLOB-novel slots: derive directly from Mbp10Snapshot counts/prices.
// order_count_imbalance: count-based analog to depth_imbalance.
let (bid_ct_sum, ask_ct_sum) = snap.levels.iter().fold((0u64, 0u64), |(b, a), l| {
(b + l.bid_ct as u64, a + l.ask_ct as u64)
});
let total_ct = bid_ct_sum + ask_ct_sum;
let order_count_imbalance = if total_ct > 0 {
(bid_ct_sum as f64 - ask_ct_sum as f64) / total_ct as f64
} else {
0.0
};
ofi_row[30] = order_count_imbalance.clamp(-1.0, 1.0);
// microprice_residual: (weighted_mid − mid) / mid; dimensionless.
// Mbp10Snapshot::weighted_mid_price() uses top-of-book volume-weighted mid.
let mid = snap.mid_price();
let wmid = snap.weighted_mid_price();
let microprice_residual = if mid > 0.0 && mid.is_finite() && wmid.is_finite() {
((wmid - mid) / mid).clamp(-0.01, 0.01)
} else {
0.0
};
ofi_row[31] = microprice_residual;
ofi_per_bar.push(ofi_row);
}
_ => ofi_per_bar.push([0.0; OFI_DIM]),
}
} else {
ofi_per_bar.push([0.0; OFI_DIM]);
}
// ── Book aggression: center-of-mass asymmetry from MBP-10 depth ──
// Uses the last snapshot in the bar window (same as OFI calculation above)
if let Some(snap) = bar_snapshots.last() {
let mut buy_weighted_sum = 0.0_f64;
let mut buy_total = 0.0_f64;
let mut sell_weighted_sum = 0.0_f64;
let mut sell_total = 0.0_f64;
for (level, pair) in snap.levels.iter().enumerate().take(10) {
let bid_size = pair.bid_sz as f64;
let ask_size = pair.ask_sz as f64;
buy_weighted_sum += (level + 1) as f64 * bid_size;
buy_total += bid_size;
sell_weighted_sum += (level + 1) as f64 * ask_size;
sell_total += ask_size;
}
let buy_com = if buy_total > 0.0 { buy_weighted_sum / buy_total } else { 5.5 };
let sell_com = if sell_total > 0.0 { sell_weighted_sum / sell_total } else { 5.5 };
let book_aggression = (sell_com - buy_com) / 10.0; // normalize to [-1, 1]
if let Some(last) = ofi_per_bar.last_mut() {
last[16] = book_aggression;
}
}
}
// ── OFI temporal deltas: delta[bar] = ofi[bar] - ofi[bar-1] ──
// Computed after all bars so we have the full sequence
for i in (1..ofi_per_bar.len()).rev() {
for k in 0..8 {
ofi_per_bar[i][8 + k] = ofi_per_bar[i][k] - ofi_per_bar[i - 1][k];
}
}
// First bar has no previous — deltas are zero (already initialized)
// ── Log bar duration: ln(duration_secs) / 10.0 ──
for i in 0..n {
let duration_secs = if i > 0 {
let ts_cur = all_bars[i + WARMUP].timestamp;
let ts_prev = all_bars[i + WARMUP - 1].timestamp;
(ts_cur - ts_prev).num_seconds() as f64
} else {
60.0 // default 1 minute for first bar
};
let log_duration = duration_secs.max(0.1).ln() / 10.0;
ofi_per_bar[i][17] = log_duration;
}
// ── Per-bar OFI extraction (SP20 parallel via time-bucket sharding) ──
//
// Replaces a sequential 132-line loop with a call into the
// `ml-features` library function. Sharding strategy: K shards
// process contiguous bar ranges in parallel; each shard owns a
// freshly-cloned `OFICalculator` and replays a `WARMUP_BARS` prefix
// of its range to populate rolling-window state (VPIN/Kyle/
// trade-imb/OFI-stats/prev-snap) to bit-identical sequential-walk
// state at the warmup→core boundary. Post-loop passes (lag-1
// deltas, log_bar_duration) are pure functions of the full output
// and run sequentially after concatenation.
//
// Bit-equivalence is verified in
// `crates/ml-features/tests/ofi_parallel_bit_equiv_test.rs`.
//
// Shard count: from `OFI_SHARD_COUNT` env (override) or rayon's
// current thread pool (`ci-compile-cpu` = 28 cores). Capped at
// `n` shards. Set to 1 to disable parallelism (matches previous
// sequential behaviour exactly).
let shard_count: usize = std::env::var("OFI_SHARD_COUNT")
.ok()
.and_then(|s| s.parse::<usize>().ok())
.unwrap_or_else(rayon::current_num_threads);
let inputs = ml::features::PerBarOfiInputs {
bars: &all_bars,
warmup: WARMUP,
n,
snapshots: &all_snapshots,
trades: all_trades.as_deref(),
};
let ofi_per_bar = ml::features::compute_ofi_per_bar_parallel(
&inputs,
shard_count,
ml::features::DEFAULT_OFI_PARALLEL_WARMUP_BARS,
);
info!(
"OFI per-bar extraction: {} shards, warmup={} bars",
shard_count.max(1).min(n),
ml::features::DEFAULT_OFI_PARALLEL_WARMUP_BARS,
);
let delta_nonzero = ofi_per_bar.iter().filter(|f| f[8..16].iter().any(|&v| v != 0.0)).count();
let book_nonzero = ofi_per_bar.iter().filter(|f| f[16] != 0.0).count();