58ffb3a48e
Single coordinated commit per `feedback_no_partial_refactor`. All SP3-era hardcoded magnitude multipliers (10×, 100×, 1e3×, 1e6×) and ε floors (.max(1.0)) replaced by per-slot ISV reads with consumer-side EPS_CLAMP_FLOOR=1.0 numerical safety. Mechanism mapping: - Mech 1 target_q clamp: 10 × Q_ABS_REF.max(1.0) → ISV[TARGET_Q_BOUND] - Mech 2 atom-position clamps (3 sites × 4 branches): 10 × Q_ABS_REF → ISV[ATOM_POS_BOUND[branch]] - Mech 5 fused diagnostic: per-slot ISV reads in `dqn_nan_check_fused_f32_kernel` (kernel takes `isv_signals*` instead of `q_abs_ref_eff` / `h_s2_rms_ema_eff` host args) - Mech 6 adaptive_clip upper_bound: 100 × slow_ema × Q_ABS_REF → ISV[GRAD_CLIP_BOUND] - Mech 9 post-Adam weight_clamp (5 Adam kernels): 100 × Q_ABS_REF → ISV[WEIGHT_BOUND[group]] - Mech 10 h_s2 clamp: 100 × H_S2_RMS_EMA → ISV[H_S2_BOUND] - AdamW weight_decay (5 kernels): config field → ISV[WD_RATE[group]] - L1 lambda (trunk only): 1e-3 → ISV[L1_LAMBDA_TRUNK_INDEX] DQN main Adam split into 3 per-group sub-launches (DqnTrunk / DqnValue / DqnBranches) per `feedback_no_quickfixes`. Overrides the plan's "max/min-of-3 single-launch shortcut" recommendation. Each sub-launch reads its own WEIGHT_BOUND[group], WD_RATE[group], and (trunk only) L1_LAMBDA_TRUNK_INDEX. Pearl C engagement-counter deferral from A14/A15 resolved in this same commit — per-group split means each sub-launch writes per-block counts at its own offset, and `pearl_c_post_adam_engagement_check` is invoked per group from fused_training.rs (DqnTrunk/DqnValue/DqnBranches separate calls). `weight_decay` field removed from: - DQNHyperparameters (crates/ml/src/trainers/dqn/config.rs) - GpuDqnTrainConfig (crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs) - GpuIqnConfig (crates/ml/src/cuda_pipeline/gpu_iqn_head.rs) - GpuIqlConfig (crates/ml/src/cuda_pipeline/gpu_iql_trainer.rs) - TrialOverrides + PSO search-space (crates/ml/src/training_profile.rs) - apply_family_scaling (`weight_decay *= li` line removed) Aux trainers outside SP4 8-group taxonomy (DT, ofi_embed, denoise, sel/recursive_conf) keep `weight_clamp_max_abs = 0.0` disable — mirrors the existing DT pattern. They have no individual ISV producer, so they don't read SP4 bounds. Files-touched (17): atoms_update_kernel.cu, iql_value_kernel.cu, experience_kernels.cu, dqn_utility_kernels.cu, gpu_dqn_trainer.rs, gpu_iqn_head.rs, gpu_iql_trainer.rs, gpu_attention.rs, gpu_tlob.rs, fused_training.rs, training_loop.rs, constructor.rs, config.rs, generalization.rs (smoke), training_profile.rs, train_baseline_rl.rs, dqn-wire-up-audit.md. Verification (local, RTX 3050 Ti): - `cargo check -p ml --offline`: clean. - `git grep -nE "10\.0_f32 \* q_abs_ref|10\.0f \* q_abs_ref|100\.0_f32 \* q_abs_ref|100\.0f \* q_abs_ref|1e6_f32 \* q_abs_ref|1e3_f32 \* q_abs_ref|100\.0_f32 \* h_s2|100\.0f \* h_s2_rms" crates/ml/src/`: ZERO matches. - `git grep -nE "weight_decay:\s*f64|l1_lambda:" crates/ml/src/trainers/dqn/`: ZERO matches. - `git grep -n "self.config.weight_decay" crates/ml/src/`: only TFT remains (separate trainer outside SP4 scope). - `git grep -n "q_abs_ref_eff|h_s2_rms_ema_eff" crates/ml/src/cuda_pipeline/dqn_utility_kernels.cu`: ZERO matches. - 8 SP4 lib tests pass (sp4_wiener_ema, sp4_isv_slots, state_reset_registry). - 14 SP4 producer GPU tests pass on RTX 3050 Ti (no behavior change at producer level — consumer-side migration only). - `cargo test -p ml --lib --offline`: 928 passed, 14 failed (all 14 pre-existing on HEAD `1389d1c81`; no new failures). Validation deferred to Layer C smoke. Expected: F0/F1/F2 all complete 5 epochs; F1 trains past step 1000; F0 ≥ 37.5; F2 ≥ 55; slot 49 quiet. 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.