f94d857ebc
Removes 3 panic gates inserted by 2c.3a:
- BatchedBackward::backward_full
- gpu_dqn_trainer::apply_iqn_trunk_gradient
- gpu_dqn_trainer::apply_ensemble_diversity_backward
GRN backward chain (per block, mirrors encoder_forward_only):
d_y → backward_raw_phase1 (LN_dx + LN_dgdb + GLU_bwd)
→ cuBLAS Linear_b dW + dX
→ backward_raw_phase2 (ELU_bwd)
→ cuBLAS Linear_a dW + dX
→ for h_s1: Linear_residual dW (no_bias_lda) + dX accumulation
→ for h_s2: saxpy_inplace identity residual into d_h_s1
New helper: BatchedForward::encoder_backward_chain orchestrates both
GRN blocks. Used by main backward (writes to grad_buf), CQL backward
(writes to cql_grad_scratch), and the two auxiliary paths (write to
iqn_trunk_m then SAXPY into grad_buf).
apply_ensemble_diversity_backward additionally fixes value-head
indices: w_v1 4→13, w_v2 6→15 (legacy indices were post-2c.3a stale).
iqn_trunk_m grown from legacy 4-tensor size to 13-GRN-tensor padded
size (matches grad_buf layout for the trunk portion so the SAXPY
mix-in lands at the correct per-tensor offsets).
batched_backward::launch_dw_only_no_bias_lda added for Linear_residual
backward with padded states stride (Linear_residual has no bias, so
the bias-grad kernel cannot run with NULL db).
ReLU masks on h_s1 / h_s2 are gone — the GRN trunk ends in LayerNorm
which has no truncation derivative. Value-head FC retains its ReLU
mask (value head still uses ReLU activation).
launch_cublas_backward_to changed from &self to &mut self because the
GRN backward needs to scribble per-block partial-reduction scratch
inside grn_h_s{1,2}_online; the borrow split lets the trainer hand
&mut BatchedForward + &BatchedBackward to encoder_backward_chain.
Smoke validation (multi_fold_convergence, 3 folds × 5 epochs, 599.73s):
fold 0: best Sharpe 7.52 at epoch 2, val_Sharpe 0.51
fold 1: best Sharpe 60.94 at epoch 4, val_Sharpe 0.64
fold 2: best Sharpe 10.40 at epoch 2, val_Sharpe 0.82
All 3 dqn_fold{N}_best.safetensors checkpoints written. PF=5.29
on fold 2 epoch 5 with +988% return — gradients flow cleanly through
the GRN composition without NaN/Inf.
Audit doc updated (Invariant 7). // ok: suppressions added to 5
defensive null-guard sites in read_isv_signal_at /
read_atom_utilization / compute_q_spectral_gap (pre-existing,
documented in fn doc comments).
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.