Replaces Option<u32> instrument_id_filter with InstrumentFilter enum {All,
Id(u32), FrontMonth}. FrontMonth runs a two-pass detect over the DBN
stream: pass 1 counts instrument_ids and collects SymbolMapping records,
picks the dominant id, validates it resolves to an ES contract via regex
ES[FGHJKMNQUVXZ]\d{1,2}; pass 2 streams the filtered records.
Motivated by alpha-perception-k54wd: a single-id filter on parent-symbol
ES.FUT data caught Q1 2024 (kept=73M) but kept=0 for Q2-Q9 because ES
front-month rolls quarterly (ESH4 -> ESM4 -> ESU4 -> ESZ4 ...). FrontMonth
self-tunes across the rolls without needing a per-file id table.
Sidecar keys distinguish modes: mbp10 / mbp10_instr<id> / mbp10_front_month.
CLI flag renamed --instrument-id -> --instrument-mode {all,id=N,front-month}
with matching parameter rename in argo-alpha-perception.sh + template.
Two carried-over limitations from Phase E.0 / E.1 fixed and verified.
1. MBP-10 parser bug fix (`parse_mbp10_streaming` + `parse_mbp10_file`)
The DBN crate's `Mbp10Msg` carries the FULL post-update top-10 book
in `levels: [BidAskPair; 10]` per message — not just the single
update event's price/size. Previously the parser only called
`update_level(0, ...)` with the update event's fields, leaving
`current_snapshot.levels[1..10]` at default-empty. Downstream:
- OFI calculator reading L2-L5 got zeros → produced wrong OFI
features (the canonical Phase 1c/1d 81-dim feature stack has
multi-level OFI as features 0..5; with the bug these were
constant zero).
- microprice (`snapshot.levels[1]`) got zeros.
- FillModel L2/L3 fit observations got zeros, so L2/L3
coefficients were undefined (we worked around by replicating
L1 with attenuated intercept).
Fix: after `update_level(0, ...)`, copy fields from
`mbp10.levels[lvl]` into `current_snapshot.levels[lvl]` for `lvl
in 1..max_lvl`. Field-by-field copy preserves the existing scale
convention (raw 1e9 fixed-point i64). Applied to both streaming
and async file-parse code paths.
Comment "For simplicity, store all updates in level 0 / A full
implementation would maintain proper level ordering" removed.
2. fit_poisson L2 regularization
New `fit_poisson_l2(features, observed, max_iters, lr, l2_lambda)`
API (the old `fit_poisson` delegates with l2_lambda=0). L2 penalty
applies to slope coefficients β[1..5] but NOT to intercept β[0]
(penalizing the intercept biases toward p≈0.5 for all-zero-feature
samples, breaking the recovery test). Per-iteration update:
β[0] -= lr · grad[0] / n (intercept)
β[k] -= lr · (grad[k] / n + λ · β[k]) (slope, k ∈ 1..5)
Canonical motivation: on real 5.2M-trade ES.FUT data the
unregularized fitter converged to β_spread ≈ -40 (Task 5c commit
12151ccf6), producing near-zero limit fill probability at typical
spreads despite empirical fill rate ~70%. With l2_lambda=0.01 the
slope shrinks modestly while intercept tracks the empirical rate.
Default in the calibration binary bumped to 0.01.
New unit test `fit_poisson_l2_shrinks_slope_on_pathological_outlier`
constructs 990 typical samples + 10 wide-spread outliers and
verifies `|β_spread|` with L2 < `|β_spread|` without L2. Passes.
3. Cascade re-run verifies the fix is verdict-robust:
New fit (with L2 + parser fix, 500K snapshots):
BID L1: β_0=-0.24 β_spread=-1.87 β_imbal=-0.10 β_ofi=-0.006 β_logτ=-0.30
ASK L1: β_0=+0.21 β_spread=-36.41 β_imbal=+0.19 β_ofi=+0.81 β_logτ=+0.22
(β_spread on ask still large but β_0 sane; cloglog model
fundamentally mis-fits the binary tight-spread / wide-spread regime.)
New baseline (with new fill model):
mean = -5191.53 (vs old -5185.13)
std = 4963.62 (vs old 4952.85)
Negligible drift, env dynamics essentially unchanged.
H=6000 smoke re-run (same alpha cache, new fill model + parser):
Q_SPREAD_EMA = 29.59 (was 35.44)
ACTION_ENTROPY_EMA = 2.00 (was 2.00)
RETURN_VS_RANDOM_EMA = +1.001σ (was +1.003σ)
EARLY_Q_MOVEMENT_EMA = 0.130 (was 0.130)
Overall: PASS (was PASS)
Verdict is ROBUST to the fixes — the fxcache-based smoke is
insulated from the MBP-10 parser bug (uses synthesized bid/ask
from mid), and the FillModel quality improvement is minor enough
that the policy's behaviour is essentially unchanged. The fixes
matter MORE for production training paths that read MBP-10
directly (those see the full L2-L10 book now).
Files touched:
crates/data/src/providers/databento/dbn_parser.rs (parser fix in
both parse_mbp10_streaming and parse_mbp10_file)
crates/ml/src/env/fill_model.rs (new fit_poisson_l2 + test)
crates/ml/examples/alpha_fit_fill_model.rs (--l2-lambda flag)
crates/ml/examples/alpha_dqn_h600_smoke.rs (updated hardcoded
baseline values to match the new random baseline run)
config/ml/alpha_fill_coeffs.json (re-fitted with both fixes)
config/ml/alpha_random_baseline.json (re-run with new fill model)
config/ml/alpha_dqn_h6000_smoke.json (verified PASS)
All 8 fill_model tests pass. Build clean across data, ml-alpha, ml.
The kill-criteria producer, Munchausen target kernel, Rust launchers,
fit/baseline binaries, and their output JSON artifacts are *durable
infrastructure* of the alpha trading system (live across Phase E/F/G/...),
not milestone-scoped to Phase E specifically. Aligns with the earlier
`phase_e_isv_slots.rs` → `alpha_isv_slots.rs` rename rationale.
What was renamed:
Code files:
crates/ml/src/cuda_pipeline/phase_e_kill_criteria.cu → alpha_kill_criteria.cu
crates/ml/src/cuda_pipeline/phase_e_munchausen_target.cu → alpha_munchausen_target.cu
crates/ml/src/cuda_pipeline/phase_e_kernels.rs → alpha_kernels.rs
crates/ml/examples/phase_e_fit_fill_model.rs → alpha_fit_fill_model.rs
crates/ml/examples/phase_e_random_baseline.rs → alpha_random_baseline.rs
Artifacts:
config/ml/phase_e_fill_coeffs.json → alpha_fill_coeffs.json
config/ml/phase_e_random_baseline.json → alpha_random_baseline.json
Kernel function names:
phase_e_kill_criteria_compute_kernel → alpha_kill_criteria_compute_kernel
phase_e_munchausen_target_kernel → alpha_munchausen_target_kernel
Rust launcher names:
launch_phase_e_kill_criteria → launch_alpha_kill_criteria
launch_phase_e_munchausen_target → launch_alpha_munchausen_target
Static cubin names:
PHASE_E_MUNCHAUSEN_TARGET_CUBIN → ALPHA_MUNCHAUSEN_TARGET_CUBIN
Historical milestone tags in doc-comments ("Phase E.1 Task N (2026-05-15)")
are RETAINED — they record WHEN the work landed and what plan it
implemented, which doesn't change with the system-scoped rename.
Plus: ADDS the alpha_munchausen_target GPU smoke test in alpha_kernels.rs.
End-to-end validates the launcher + kernel against hand-computed expected
values: batch=2 with one terminal sample; expected targets [29.8, 1.1];
got match within 0.05 tolerance on RTX 3050 Ti. PROVES the Task 9/10
kernels actually run on GPU.
All affected references updated in:
- build.rs (kernel compile list)
- mod.rs (module registration)
- state_reset_registry.rs (4 RegistryEntry descriptions for slots 539-542)
- alpha_isv_slots.rs (slot table comment)
- docs/isv-slots.md (audit-doc cross-references)
Verified:
cargo test -p ml --lib alpha_kernels: 2/2 pass (including GPU smoke)
cargo test -p ml --lib state_reset_registry: 10/10 pass
cargo build -p ml --release --example alpha_fit_fill_model --example alpha_random_baseline: clean
