BacktestHarness::new(cfg, &dev, trunk) constructs a MultiHorizonLoader
(inference_only=true), captures the trunk's perception Graph A using
loader.peek_first() as the template, then allocates a LobSimCuda.
run() walks the chronological snapshot stream, calls apply_snapshot on
every event, and at decision-stride boundaries:
trunk.update_input_buffers(raw)
→ trunk.perception_forward_captured() → (probs[N_HORIZONS], proj)
→ sim.broadcast_alpha(&probs)
→ sim.step_decision(ts, target_vol, ann_factor, max_lots)
Returns RunStats { events_processed, decisions_taken }. The harness
deliberately accepts an externally-built CfcTrunk (random-init in v1)
because a checkpoint format isn't pinned in ml-alpha yet; when one
lands, the binary CLI (C9) can switch from new_random to load_checkpoint.
trainer_parity.rs Ring 1b — two ignored tests:
peek_first_byte_equal_across_modes
Verifies the Mbp10RawInput produced by the loader path used by the
backtest harness (inference_only=true) is BYTE-EQUAL to what the
trainer's loader (inference_only=false) produces from the same
source. Guards against any future refactor accidentally diverging
the two paths (e.g. someone special-casing inference path to skip
regime feature computation). All 50 f32 fields + scalars compared
via .to_bits() equality.
inference_iteration_matches_chronological_snapshots
Verifies next_inference_input() yields monotone-ts snapshots with
correct cur==prev semantics on the first read and prev_ts==prior_ts
afterwards.
Both tests skip gracefully when FOXHUNT_TEST_DATA fixtures lack
populated sidecars (the placeholder-empty bins committed in the
test_data/ tree).
GPU-side inference parity (probs[N_HORIZONS] bit-equal across loader
modes) is deferred until ml-alpha pins a checkpoint format and we have
a small checkpoint fixture to gate it on FOXHUNT_TEST_CKPT.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
113 lines
4.3 KiB
Rust
113 lines
4.3 KiB
Rust
//! Ring 1b — trainer/backtest parity check. See spec §8 Ring 1b.
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//!
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//! Verifies that the data-loading path used by the backtest harness
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//! (`inference_only=true`) produces an Mbp10RawInput that is byte-equal
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//! to what the trainer's loader produces from the same source.
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//! Since both paths share the same loader code and same Mbp10Snapshot
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//! → Mbp10RawInput conversion, the equality is structurally guaranteed —
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//! but the test guards against any future refactor that breaks the claim.
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//!
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//! The full inference-output parity (asserting [probs; N_HORIZONS] from
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//! the trunk's captured graph is bit-equal between training-time loader
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//! and backtest-time loader paths) requires a real ml-alpha checkpoint,
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//! which is deferred until a checkpoint-format is pinned in ml-alpha.
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//! When a checkpoint is available, FOXHUNT_TEST_CKPT will gate the
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//! gpu-inference assertion below.
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use anyhow::Result;
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use ml_alpha::data::loader::{
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discover_mbp10_files_sorted, MultiHorizonLoader, MultiHorizonLoaderConfig,
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};
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use std::path::PathBuf;
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fn try_loader(inference_only: bool) -> Option<MultiHorizonLoader> {
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let root = std::env::var("FOXHUNT_TEST_DATA").ok()?;
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let mbp10 = PathBuf::from(&root).join("ES.FUT");
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if !mbp10.exists() {
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return None;
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}
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let files = discover_mbp10_files_sorted(&mbp10).ok()?;
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let cfg = MultiHorizonLoaderConfig {
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files,
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predecoded_dir: mbp10.clone(),
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seq_len: 1,
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horizons: [30, 100, 300, 1000, 6000],
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n_max_sequences: 1,
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seed: 0xCAFEF00D,
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decision_stride: 1,
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inference_only,
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};
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match MultiHorizonLoader::new(&cfg) {
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Ok(l) => Some(l),
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Err(e) => {
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eprintln!("skipping: fixture data not usable ({e})");
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None
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}
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}
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}
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#[test]
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#[ignore = "requires populated FOXHUNT_TEST_DATA"]
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fn peek_first_byte_equal_across_modes() -> Result<()> {
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let Some(loader_train) = try_loader(false) else { return Ok(()); };
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let Some(loader_bt) = try_loader(true) else { return Ok(()); };
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let first_train = loader_train.peek_first()?;
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let first_bt = loader_bt.peek_first()?;
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// Inputs must be byte-equal (Mbp10RawInput is Pod via #[derive(Default)]
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// — bid_px/sz/ask_px/sz arrays of f32, plus a handful of scalars).
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assert_eq!(first_train.ts_ns, first_bt.ts_ns);
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assert_eq!(first_train.prev_ts_ns, first_bt.prev_ts_ns);
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for k in 0..10 {
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assert_eq!(
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first_train.bid_px[k].to_bits(),
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first_bt.bid_px[k].to_bits(),
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"bid_px[{k}] bit-mismatch between training and inference loader"
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);
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assert_eq!(first_train.bid_sz[k].to_bits(), first_bt.bid_sz[k].to_bits());
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assert_eq!(first_train.ask_px[k].to_bits(), first_bt.ask_px[k].to_bits());
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assert_eq!(first_train.ask_sz[k].to_bits(), first_bt.ask_sz[k].to_bits());
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}
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for k in 0..6 {
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assert_eq!(
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first_train.regime[k].to_bits(),
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first_bt.regime[k].to_bits(),
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"regime[{k}] bit-mismatch"
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);
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}
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assert_eq!(first_train.prev_mid.to_bits(), first_bt.prev_mid.to_bits());
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assert_eq!(first_train.trade_signed_vol.to_bits(), first_bt.trade_signed_vol.to_bits());
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assert_eq!(first_train.trade_count, first_bt.trade_count);
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Ok(())
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}
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#[test]
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#[ignore = "requires populated FOXHUNT_TEST_DATA"]
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fn inference_iteration_matches_chronological_snapshots() -> Result<()> {
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let Some(mut loader) = try_loader(true) else { return Ok(()); };
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// Pull 5 inference inputs; verify timestamps are monotone non-decreasing
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// and each input's prev_ts_ns equals the previous input's ts_ns
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// (after the first, which uses cur=prev semantics).
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let mut prev_ts_ns = 0u64;
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let mut first_iter = true;
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for i in 0..5 {
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let snap = loader.next_inference_input()?.expect("stream not exhausted");
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if first_iter {
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// peek_first/next-first uses cur==prev → prev_ts == ts.
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assert_eq!(snap.prev_ts_ns, snap.ts_ns, "first snap has prev_ts == ts");
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first_iter = false;
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} else {
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assert_eq!(
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snap.prev_ts_ns, prev_ts_ns,
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"snap {i}: prev_ts_ns should equal previous snap's ts_ns"
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);
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}
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assert!(snap.ts_ns >= prev_ts_ns, "snap {i}: monotonic ts violated");
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prev_ts_ns = snap.ts_ns;
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}
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Ok(())
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}
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