dc3f948ee9
Critical safety mechanism that completes the EGF pearl: scales the wire
column of `dL/dx_concat [B, SH2 + 1]` (the gradient flowing FROM the
direction Q-head's first FC SGEMM TO `aux_softmax_diff`) by
`ISV[ALPHA_GRAD_SMOOTHED_INDEX = 393]`, computed by B.4's
`alpha_grad_compute_kernel` and orchestrated per-step in B.11.
`dL/dW[wire_col]` (Q-head's own weight gradient for the appended column)
is NOT scaled — the dW SGEMM `dY^T × x_concat` and the dX SGEMM
`dY × W^T` are independent, so scaling `dx[:, SH2]` AFTER both have
completed leaves dW unaffected. Q-head learns to USE the wire freely;
only the gradient PROPAGATING BACK to aux is gated.
Pre-B.11 (no producer wired) `ISV[393]` holds sentinel `0.0` →
wire force-closed (gradient zeroed) — the conservative safety state.
Post-B.11, B.4 writes the live gate output ∈ [0, 1] each step.
Closes the latent K-mismatch B.8/B.9 left in backward
============================================================
B.8 grew `w_b0fc` to `[adv_h, SH2 + 1]` end-to-end (Adam m/v +
spectral-norm vector + smoke fixtures); B.9 closed the forward dispatch
K-mismatch. The backward dW/dX SGEMMs for `d == 0` still used `K = SH2`
against the new `LDA = SH2 + 1` weight tensor — silently dropping the
last column of dW and zeroing the wire-col gradient. B.10 closes that
gap atomically with the wire-col scale per `feedback_no_partial_refactor`:
* `backward_branch_dw` for `d == 0` now uses `(dir_qaux_concat_ptr, SH2 + 1)`
instead of `(save_h_s2, SH2)` — matching the forward consumer
pattern from B.9.
* `backward_branch_dx` for `d == 0` now writes to
`d_dir_qaux_concat [B, SH2 + 1]` with `K = SH2 + 1` instead of
`scratch_d_h_s2 [B, SH2]` with `K = SH2`. Mirrors the magnitude
branch's wider-buffer pattern.
New artifacts
=============
* `sp14_scale_wire_col_kernel.cu`: one thread per batch row, scales
`dx_concat[b, SH2]` by `isv[393]` IN-PLACE. NaN-safe per the
`dqn_scale_f32_kernel` precedent (explicit `α==0 ⇒ 0` branch).
Pure per-thread map, no atomicAdd, no shared memory.
* `sp14_d_dir_qaux_concat: CudaSlice<f32>` `[B, SH2 + 1]` trainer-
struct field. Dx SGEMM destination; the wire-col scale acts on
this buffer; the strided accumulator copies the first SH2 columns
into `bw_d_h_s2` after the scale.
* `launch_sp14_scale_wire_col` launcher reads `self.isv_signals_dev_ptr`
and the new buffer's raw_ptr.
* `backward_full` signature grows two trailing `u64` args
(`dir_qaux_concat_ptr`, `d_dir_qaux_concat_ptr`); both
`backward_full` call sites (CQL aux + main online) wired
atomically per `feedback_no_partial_refactor`.
Post-call orchestration at trainer level
========================================
1. `launch_sp14_dir_concat_qaux(save_h_s2)` rebuilds the ONLINE
concat in `sp14_dir_qaux_concat_scratch` (the forward pass had
overwritten it with the TARGET concat at line ~25817). Same
one-step-lag semantic preserved — `aux_nb_softmax_buf` is
unchanged between forward and backward.
2. `cuMemsetD32Async` zero of `d_h_s2` — pre-B.10 the direction
branch (d==0) wrote it with beta=0; post-B.10 the dir-Q dX
lives in `d_dir_qaux_concat` and is gated + accumulated AFTER
`backward_full` returns, so the value-FC dx accumulator inside
`backward_full` (beta=1) needs an explicit zero baseline.
3. `backward_full` runs: dir branch → `d_dir_qaux_concat`,
mag/ord/urg branches → their concat dX buffers, value-FC →
`d_h_s2` (beta=1, on top of zeroed buffer).
4. `launch_sp14_scale_wire_col` gates col SH2 of `d_dir_qaux_concat`.
5. `accumulate_d_h_s2_from_concat` (beta=1) copies first SH2 cols
of `d_dir_qaux_concat` into `d_h_s2`. Wire col stays in
`d_dir_qaux_concat[:, SH2]`, untouched by this accumulator (its
destination range is [0, SH2)). Pre-B.11 the wire is already
zeroed by the sentinel-α gate; the orchestrator that propagates
the gated wire-col gradient back to the aux head's softmax CE
backward chain lives in B.11.
6. mag/ord/urg accumulators continue with beta=1 (comments updated).
Wire status
===========
* Forward dispatch: unchanged (B.9-complete).
* Backward dispatch: GATED on both call sites (CQL aux + main online).
* dW unchanged: the `dW = dY^T × x_concat` SGEMM writes
`grad_buf[goff_w_b0fc..]` BEFORE the scale-wire-col launches;
the scale operates ONLY on `d_dir_qaux_concat` (the dx buffer)
AFTER both dW and dX SGEMMs complete.
* Target net unaffected: Polyak EMA-only, no backward.
* CudaSlice wrapper path: passes `0u64` for both new args, falls
back to the legacy K=SH2 path. Consistent with the forward
wrapper's diagnostic-only residual.
Verified
========
* `SQLX_OFFLINE=true cargo check -p ml` clean, 18 warnings (baseline)
* `cargo test -p ml --test sp14_oracle_tests` 2 passed, 6 ignored (GPU)
* Audit doc `docs/dqn-wire-up-audit.md` updated per Invariant 7.
After this commit, the EGF pearl is architecturally complete; the
orchestration of when/how the alpha_grad gates fire happens in B.11
(producer chain orchestrator).
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.