dde38d800d
Per-group VSN MLP parameter tensors and per-group importance-mask EMA ISV
slots land additively — no production callers in this commit. The
forward orchestrator + cuBLAS Linear_1/Linear_2 wire-in lands in 1B-iii;
backward orchestrator + autograd integration lands in 1B-iv.
Param tensors (24 new, indices [95..119)):
* Per-group quad (`vsn_w1_g{g}`, `vsn_b1_g{g}`, `vsn_w2_g{g}`,
`vsn_b2_g{g}`) for each `FEATURE_GROUP_RANGES` entry
(market / ofi / tlob / mtf / portfolio / plan_isv).
* `VSN_HIDDEN_DIM = 16`. Per-group element count =
16*group_dim_g + 33; sum = 16*121 + 6*33 = 2134 floats.
* Xavier init on weights, zero on biases — initial logits ≈ 0 yield
softmax mask ≈ 1/SL_NUM_FEATURE_GROUPS (uniform cold-start).
* `NUM_WEIGHT_TENSORS` 95 → 119; `compute_param_sizes()` and
`xavier_init_params_buf()` rewritten with a runtime per-group loop
reading `FEATURE_GROUP_RANGES` from `ml_core::state_layout`
(Task 1A's prerequisite).
* Adam state, target params, and grad buffers grow automatically
since their allocations use `compute_total_params(cfg) +
cutlass_tile_pad`.
ISV slots (6 new, indices [105..111)):
* `VSN_MASK_GROUP_0_EMA_INDEX = 105` (market) through
`VSN_MASK_GROUP_5_EMA_INDEX = 110` (plan_isv).
* Cold-start `1.0 / SL_NUM_FEATURE_GROUPS = 1/6` (uniform prior — no
group preference at init).
* FoldReset reapplies the same uniform value at fold boundary; 6 new
`RegistryEntry` rows + a single dispatch arm in
`training_loop.rs::reset_named_state` covering all six names.
* Producer kernel `vsn_mask_ema_update` (1B-i) overwrites these slots
once the launch site is wired in 1B-iii.
Fingerprint:
* Pair shifted 103/104 → 111/112; `ISV_TOTAL_DIM` 105 → 113.
* `layout_fingerprint_seed()` extended with the 6 new ISV slot names
+ 24 new `PARAM_VSN_*` entries terminating at
`PARAM_TOTAL_TENSORS=119`.
* New `LAYOUT_FINGERPRINT_CURRENT = 0x1b28321bb816f246`
(was `0x5789155b683ab59c`). Checkpoint break by intent — fail-fast
at constructor load on mismatch, no migration path per
`feedback_no_legacy_aliases.md`.
Cubin refs:
* `VSN_FEATURE_SELECTION_CUBIN` and `VSN_MASK_EMA_CUBIN` added as
`pub(crate) static` `#[allow(dead_code)]` byte arrays — the
`include_bytes!()` runs at compile time, but no `load_cubin(...)` /
`load_function(...)` call exists yet (deferred to 1B-iii).
Mirrors the 2c.3a pattern (param tensor reshuffle without runtime
callers — that landed cleanly because the runtime sites were panic-
gated; here, the runtime sites simply don't exist yet so no gating
needed).
cargo check clean at 11 warnings (workspace baseline preserved); cargo
build compiles 62/62 cubins clean (unchanged — no new `.cu` files).
Smoke deferred — behaviour byte-identical to the f3e3ac347 /
0x5789155b683ab59c post-bottleneck-fix baseline (700.43s, 3 folds × 5
epochs, per-fold best train Sharpe 6.53 / 80.11 / 66.72, geom-mean
39.27) since no consumer reads the new params or ISV slots; smoke
re-validation lands at 1B-iii.
Audit doc updated.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Foxhunt
Production HFT trading system in Rust.
Architecture
The workspace contains 32 crates organized as follows:
Core Libraries (16)
| Crate | Purpose |
|---|---|
trading_engine |
Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing |
risk |
VaR, Kelly, circuit breakers, kill switches, compliance |
risk-data |
Risk data types and shared structures |
trading-data |
Trading data types |
ml |
DQN Rainbow, PPO, TFT, Mamba2, ensemble inference |
ml-data |
ML data types and feature definitions |
data |
Market data ingestion and storage |
backtesting |
Replay engine, strategy tester |
adaptive-strategy |
Ensemble execution, microstructure analysis |
common |
Shared types, resilience, error handling |
storage |
S3 and local model storage |
model_loader |
Model serialization and loading |
market-data |
Market data feed handlers |
database |
PostgreSQL access layer (SQLx) |
config |
Configuration management |
tli |
CLI commands and tooling |
Services (8)
| Service | Purpose |
|---|---|
backtesting_service |
gRPC backtesting service |
broker_gateway_service |
FIX routing, broker connectivity |
trading_service |
Core trading operations |
ml_training_service |
Model training orchestration |
data_acquisition_service |
Market data acquisition |
trading_agent_service |
Autonomous trading agents |
api_gateway |
gRPC API gateway with auth |
web-gateway |
Axum REST + WebSocket gateway |
Frontend
web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.
Building
# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace
# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib
# Clippy
SQLX_OFFLINE=true cargo clippy --workspace
ML Models
Four production model architectures on Candle v0.9.1 with CUDA:
- DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
- PPO -- Proximal Policy Optimization with GAE and LSTM policies
- TFT -- Temporal Fusion Transformer for multi-horizon forecasting
- Mamba2 -- State space model for sequence prediction
Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.
Infrastructure
- Git: Gitea at
git.fxhnt.ai(Tailscale-only), Scaleway DEV1-S - Observability: OpenTelemetry OTLP (env
OTEL_EXPORTER_OTLP_ENDPOINT) - Database: PostgreSQL with SQLx offline mode for CI
License
Proprietary. All rights reserved.