3f6eb006ca
Foundation for the unified train/val env kernel (Phase 3 of the env-unification
design). Adds two pinned device-mapped scalars to the training kernel that gate
the asymmetries currently separating training from validation:
exploration_scale ∈ [0, 1] gates training-only stochastic perturbations:
- Saboteur cost noise: sab_eff = 1 + exploration_scale × (sab - 1)
→ identity at scale=0, full saboteur at scale=1
- Counterfactual flip rate: effective_cf = exploration_scale × cf_ratio
- Plan-params position scaling: only active when scale ≥ 0.5
shaping_scale ∈ [0, 1] gates additive reward-shaping bundles:
- Drawdown penalty (× shaping_scale)
- Inventory penalty (× shaping_scale)
- Churn penalty (× shaping_scale)
- Micro-reward composite (× shaping_scale)
- Holding-cost fallback (× shaping_scale)
Capital-floor reward and segment-completion P&L are NOT gated — those
are physics/safety, not behavioral shaping.
Both scalars live in pinned host memory mapped to the device, following the
existing pattern used for cost_anneal_pinned, isv_signals_dev_ptr, etc.
Default value is 1.0 (full training mode); zero memcpy on update — the kernel
reads the current value on its next launch via cuMemHostGetDevicePointer.
Setters exposed:
GpuExperienceCollector::set_exploration_scale(f32)
GpuExperienceCollector::set_shaping_scale(f32)
Backward compatibility: scalars default to 1.0, kernel pointers are
NULL-tolerant (falls back to 1.0 inside the kernel if pointer is NULL).
The existing TD-propagation smoke test passes unchanged with default scales
(Best Sharpe 19.34 at epoch 17 in this run; was 15.19 baseline — within
run-to-run variance, no regression).
What this unblocks (deferred to next session, task #18):
- Wire validation backtest paths to call experience_env_step with both
scalars at 0.0 instead of using the separate backtest_env_kernel.
- Verify step_returns are byte-equivalent between scales=0 path and the
legacy backtest kernel (one source of truth for env physics).
- Delete backtest_env_kernel.cu (~678 LOC) and its launcher.
Files touched:
crates/ml/src/cuda_pipeline/experience_kernels.cu (+62 / -19)
crates/ml/src/cuda_pipeline/gpu_experience_collector.rs (+56)
Verified: SQLX_OFFLINE=true cargo check -p ml --lib --tests passes.
TD-propagation smoke test runs cleanly end-to-end (32.89s).
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.