10d4614fb4
Three atomicAdd sites violated feedback_no_atomicadd (deferred Phase C/E fixes per file headers): - ppo_clipped_surrogate.cu lines 270-271: atomicAdd(loss_pi/loss_entropy) - dqn_distributional_q.cu line 285: atomicAdd(loss_out, ce/B) Float addition is non-associative; atomicAdd across blocks produces order-dependent sums. When inputs were extreme (l_v=6+ early in training driving wide PPO ratios), the order-dependence flipped finite→NaN non-deterministically — same SHA + same seed + same params produced different NaN outcomes (smoke bisect 2026-05-29). Fix is structural per the codebase pattern (compute_advantage_rms, rl_q_bias_correction): per-batch sole-writer outputs + a deterministic single-warp block-tree reducer. New kernel `mean_b_reduce.cu` performs the [B]→[1] mean via grid-stride loop + warp-shuffle reduce. Changes: - cuda/mean_b_reduce.cu (NEW): single-block single-warp deterministic mean reducer mirroring compute_advantage_rms.cu pattern - cuda/dqn_distributional_q.cu: remove atomicAdd; loss_per_batch[] already sole-writer, caller invokes mean_b_reduce after - cuda/ppo_clipped_surrogate.cu: replace `loss_pi`/`loss_entropy` scalar args with `l_pi_per_batch`/`l_ent_per_batch` per-batch sole-writer outputs; caller invokes mean_b_reduce twice after kernel - build.rs: register mean_b_reduce cubin - src/rl/dqn.rs: DqnHead loads mean_b_reduce_fn; backward_logits invokes it on loss_per_batch → loss_out_dev_ptr - src/rl/ppo.rs: PolicyHead loads mean_b_reduce_fn; surrogate_forward signature gains l_pi_per_batch + l_ent_per_batch buffer args; invokes mean_b_reduce on each → scalar dev ptrs - src/trainer/integrated.rs: IntegratedTrainer gains ss_pi_l_pi_per_batch_d + ss_pi_l_ent_per_batch_d scratch fields; allocated at b_size; passed to surrogate_forward Smoke status: l_q/l_v values now reproducible bit-for-bit across runs (verified by comparing two runs of the same seed). NaN at step 4 still reproduces — proximate cause is elsewhere (likely in V head forward dynamics, not the loss summation). The atomicAdd removal is still required: it was a real correctness violation independent of the NaN symptom, and the deterministic loss values are now a precondition for diagnosing the remaining instability. Co-Authored-By: Claude Opus 4.7 <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.