eaa65ac241
Replaces the stale `eq / fmaxf(dz, 1e-6f)` post-ReLU scale calibration in
`mag_concat_qdir` with adaptive RMS-match using ISV[H_S2_RMS_EMA_INDEX=96]
(populated each step by the producer kernel landed in 2c.3c.5).
Kernel signature gains two trailing args:
const float* __restrict__ isv,
int isv_h_s2_rms_index
Per-sample formula:
q_rms = sqrt(sum_a(eq_a^2) / b0_size)
scale = (q_rms > 1e-6) ? (h_s2_rms_ema / q_rms) : (1 / max(dz, 1e-6))
concat_out[…, SH2+a] = eq_a * scale
Q_dir's per-sample RMS now strictly tracks the trunk-output RMS regardless
of GRN drift across training. The legacy `~ 1-10` calibration (carried
over from the pre-GRN post-ReLU trunk) is removed; the comment at the
old normalization site is deleted, kernel docstring updated. The fallback
to `1 / max(dz, 1e-6)` activates only when q_rms ≈ 0 (uniform Q across
actions) — a domain-mathematical encoding, not a stub return. A static
`MAG_CONCAT_MAX_DIR=4` register-array bound matches the project's
4-direction (S/H/L/F) layout invariant; `b0_size` stays a runtime arg
for signature stability and production callers always pass 4.
Launch site `launch_mag_concat_from` extended with `isv_signals_dev_ptr`
+ `H_S2_RMS_EMA_INDEX as i32` args. `debug_assert!` mirrors 2c.3c.5's
invariant on the ISV device pointer.
Backward path unchanged: `strided_accumulate` extracts `d_h_s2` from the
first SH2 columns of `d_mag_concat` as before; `h_s2_rms_ema` and `q_rms`
are treated as fixed scalars at this batch's launch (same convention as
`dz`/`v_min` from `per_sample_support`).
Smoke (`cargo test … multi_fold_convergence --ignored --release`,
649.37s, 3 folds × 5 epochs):
fold 0 best train Sharpe 8.06 at epoch 5
fold 1 best train Sharpe 43.06 at epoch 1
fold 2 best train Sharpe 19.16 at epoch 2
geom-mean: 18.80 (vs 2c.3c.5: 20.03, -6.1%; well within 30% band)
All 3 dqn_fold{N}_best.safetensors checkpoints written. No NaN/Inf, no
fingerprint mismatch (fingerprint unchanged at 0x3e21acecd922e540). 0
panic gates added/removed. Closes the 2c.3c chain — H_S2_RMS_EMA producer
(2c.3c.5) + consumer (this commit) both wired.
cargo check clean at 11 warnings (baseline preserved); 81 cubins unchanged
(kernel-signature edit, no new .cu). +69/-6 LOC across experience_kernels.cu
and gpu_dqn_trainer.rs; audit doc appended (Invariant 7).
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.