2e9e276a0c
Wires the 3 Layer D producer kernels into the production hot path AND removes the host-side EMA arithmetic for sharpe/max_dd/low_dd_ratio in the same commit per feedback_no_partial_refactor + feedback_no_cpu_compute_strict. Sites migrated (host → GPU kernel + ISV slot): training_sharpe_ema → launch_training_metrics_ema → ISV[294] max_dd_ema → launch_training_metrics_ema → ISV[295] low_dd_ratio → launch_training_metrics_ema → ISV[296] LearningHealth.compose → launch_health_composition → ISV[290..294) trade-PnL aggregation → launch_sp5_pnl_aggregation → ISV[286..290) Behavior preserved within float precision: every EMA β decay constant, warmup/clamp wrapper, and downstream consumer formula migrates verbatim. The kernels reproduce the host computations bit-for-bit (verified by the GPU-gated unit tests landed in D1/D2/D3 atf42b5fff8and the D1-rewrite re-verification at 66f7e64d1; smoke-test-7pv9v PASSED with the additive kernels loaded but unwired). The additional Pearl A+D smoothing layer (ALPHA_META=1e-3) is the SP5 architectural change the entire Layer D programme commits to (cf. pearl_first_observation_bootstrap + pearl_wiener_optimal_adaptive_alpha). Removed: host-side EMA arithmetic at training_loop.rs:5043-5099 (~57 lines covering max_dd α=0.1 + low_dd_ratio α=0.15 + training_sharpe adaptive-α + sentinel branches). Struct fields self.training_sharpe_ema, ::_initialized, self.max_dd_ema, self.low_dd_ratio are KEPT because external consumers exist (HEALTH_DIAG emit at training_loop.rs:3899, adaptive-DSR aux-weight controller at 3772, smoke tests at td_propagation.rs:126/135/155 + generalization.rs:102/103, registry log emit at 6646); they now hold ISV-Pearl-smoothed values populated by read_isv_signal_at(294/295/296) after the kernel chain completes. Also added: GpuDqnTrainer::synchronize_isv_stream() — public cold-path stream-sync helper at gpu_dqn_trainer.rs:~18475. Single cuStreamSynchronize of the training stream, mirroring the existing per_branch_q_gap_ema() embedded sync. Used once per epoch by D4's read-back path. Out of scope (deferred to D5): - HealthEmaTrackers::update host-side α=0.1 EMA (metrics.rs:23-25): produces D2's inputs; D2's launcher takes already-EMA'd scalars. Eliminating this needs a 4th kernel; out of D4 scope. - LearningHealth::update warmup wrapper + [0.2, 0.95] clamp (learning_health.rs:114-124): may have non-DQN consumers (PPO, eval); deferred to a separate refactor. - compute_epoch_financials per-bar equity walk: stays as host-side input source for D1; D1 reproduces it on GPU as a PARALLEL producer publishing to ISV. Verification: - cargo check + build clean - sp5_isv_slots + state_reset_registry unit tests 8/8 pass - cargo test -p ml --lib: 933 pass / 14 fail (identical to pre-D4 baseline; failing tests are pre-existing GPU-context-required tests, not regressions) - `git grep "= EMA_BETA *|= (1.0 - EMA_BETA)"` returns 0 (was already 0 — gate is a no-op for SP5 because production uses literal coefficients, not a named EMA_BETA constant). Meaningful gate `git grep "0.9 \* self.max_dd_ema|0.85 \* self.low_dd_ratio"` also returns 0 — confirms host EMA arithmetic deletion. - Order-of-ops: launches chained on training stream before consumer reads; stream-event sync only (synchronize_isv_stream once per epoch for cold-path host scalar refresh; producer→consumer paths on the same stream observe FIFO order without explicit fence). Mapped-pinned discipline (per feedback_no_htod_htoh_only_mapped_pinned): D1's step_returns + done_flags consumed via fresh MappedF32Buffer allocations populated by host_slice_mut — no HtoD copy. Once-per-epoch cold-path; matches existing pattern at training_loop.rs:1318. L40S smoke validation deferred to next dispatch — D4 is structurally risky enough (HEALTH_DIAG visibility behaviour change + ISV-Pearl- smoothing on host-scalar reads + first-epoch cold-start interaction with Pearl A) to validate separately. Closes feedback_no_cpu_compute_strict sweep for SP5 scope (modulo D5 follow-up). Refs: SP5 plan §D Task D4. Builds on D1-rewrite (66f7e64d1), D2 (e49756ac9), D3 (f42b5fff8). First D4 attempt was blocked by D1 per-trade vs per-bar mismatch; resolved by D1-rewrite. 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.