f3e3ac3477
Three related bugs from the 2c.3a GRN trunk reshuffle that the bottleneck-Linear runtime sites silently inherited: 1. **on_w_ptrs[24/25] -> [33/34]** (4 sites in gpu_dqn_trainer.rs). 2c.3a's +9 shift migrated branch/value/VSN/GLU/KAN consumers but missed the bottleneck Linear's forward GEMM (line ~14098), forward bias-add (~14107), and backward dW/db offsets (~15109-15110). Post-2c.3a, indices 24/25 point at b_b1out (153 floats) and start of w_b2fc (4288 floats) — not w_bn (672 floats) / b_bn (16 floats). Forward + backward were self-consistent on wrong tensors; the actual w_bn/b_bn at documented indices 33/34 sat untouched as zeros for ~10 commits. Smoke kept passing because GRN's Linear_residual projection ran in parallel and provided a clean residual-only pathway around the corrupted bottleneck slot. 2. **Target net missing bottleneck path.** forward_target_raw passed raw next_states_buf (128-padded) to an encoder expecting [B, s1_input_dim=102]; it was reading [market|ofi|tlob|mtf] of next_states instead of [bn_market_proj|portfolio]. Fix: build tg_bn_concat_buf inline before forward_target_raw using tg_w_ptrs[33]/[34] (target's w_bn) on next_states_buf[market]; new buffer pair tg_bn_hidden_buf + tg_bn_concat_buf. 3. **DDQN argmax missing bottleneck path.** submit_forward_ops_ddqn had the same shape mismatch on the online-on-next_states pass. Fix: build on_next_bn_concat_buf using on_w_ptrs[33]/[34] (online weights, since DDQN argmax uses online net); new buffer pair on_next_bn_hidden_buf + on_next_bn_concat_buf. Three call sites of the existing bn_tanh_concat_kernel now: online-on- states (states + on_w_bn), target-on-next_states (next + tg_w_bn), and ddqn-online-on-next_states (next + on_w_bn). Each combination of weights × input states produces distinct features; sharing the kernel across distinct buffer pairs preserves the GPU-only cold-path contract. No fingerprint change (no ISV slot or param tensor added). Smoke validation (multi_fold_convergence, 700.43s, 3 folds x 5 epochs): fold 0 best Sharpe 6.53 at epoch 2 fold 1 best Sharpe 80.11 at epoch 4 fold 2 best Sharpe 66.72 at epoch 4 geom-mean: 39.27 (vs Task 3 20.03, 2c.3c.6 18.80) The Sharpe lift is consistent with the bottleneck Linear now seeing its actual weights and the target/DDQN nets seeing input-shape- consistent features for TD-target / argmax-action computations. +166 / -9 LOC all in gpu_dqn_trainer.rs. 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.