0b737ec30a
Replaces the hardcoded `α=0.8 / σ=0.01` constants in `reset_for_fold`'s
`shrink_and_perturb` call with ISV-driven adaptive bounds per
`feedback_isv_for_adaptive_bounds` and `feedback_adaptive_not_tuned`.
The driving signal is the relative weight drift `||current - best||₂ /
||best||₂` already computed each epoch by the SP18 v2 weight-drift
diagnostic kernel (commit aab13a83f). Extended that kernel atomically
to also write α / σ into ISV[505] / ISV[506] from the same two block-
tree-reductions — single producer, single launch, single source of
truth. No new kernel; reuses `||best||₂` + `relative_drift` already
computed.
Added 2 ISV slots [505..507):
[505] SHRINK_ALPHA_ADAPTIVE — drift-driven preservation factor.
α = 1 - clamp(rel_drift, 1-MAX, 1-MIN). Small drift → α near
MAX (preserve hard); large drift → α near MIN (shrink hard).
[506] SHRINK_SIGMA_ADAPTIVE — scale-aware noise. σ tracks
||best||_RMS / RMS_REFERENCE so noise stays scale-relative as
weight magnitudes evolve across folds.
Pearl-A first-observation bootstrap: sentinels 0.8 / 0.01 match prior
hardcoded values exactly. Cold-start path (first fold, no
`best_params_snapshot`) leaves slots at sentinels — bit-identical to
pre-fix behavior.
Bounds [SHRINK_ALPHA_MIN=0.5, SHRINK_ALPHA_MAX=0.95] +
[SHRINK_SIGMA_MIN=1e-4, SHRINK_SIGMA_MAX=0.1] are Category-1
dimensional safety floors per `feedback_isv_for_adaptive_bounds`.
Bilateral clamp per `pearl_symmetric_clamp_audit`.
Atomic in same commit per `feedback_no_partial_refactor` and
`feedback_wire_everything_up`:
* 2 ISV slot constants + sentinels + bounds + lock test in
`sp14_isv_slots.rs`.
* `ISV_TOTAL_DIM` 505 → 507 + layout fingerprint seed bump in
`gpu_dqn_trainer.rs`.
* `state_layout.cuh` mirror for kernel-side reads.
* `weight_drift_diag_kernel.cu` extended: 4-element output buffer
`[l2_diff, rel, α_target, σ_target]` + ISV writes via
`__threadfence_system()`.
* `launch_weight_drift_diag` + `read_shrink_perturb_adaptive`
plumbing; mapped-pinned 4-float buffer.
* `reset_for_fold` reads ISV[505]/ISV[506] via `read_isv_signal_at`
+ defensive host-side clamp; replaces former hardcoded constants.
* 2 fold-reset registry entries with dispatch arms (sentinel 0.8 /
0.01 — Pearl-A bootstrap matches prior hardcoded for bit-
identical cold-start).
* Per-epoch HEALTH_DIAG `weight_drift` line extended with
`alpha_adaptive` / `sigma_adaptive` for observability.
* 8 behavioral tests (CPU-only oracle pinning the math contract).
* `docs/dqn-wire-up-audit.md` — Invariant 7 audit entry.
`shrink_and_perturb` signature unchanged — kept as 2-arg
`(alpha, sigma)` so the 4 existing call sites compile without ripple
(3 in `training_loop.rs`, 1 in `fused_training.rs`). Only the
fold-transition site at `fused_training.rs::reset_for_fold` is
migrated to ISV reads in this commit; the 3 health-driven /
backtracking sites in `training_loop.rs` continue to use
`hyperparams.shrink_perturb_alpha/sigma` (different driving signal —
those are unhealthy-streak rescue interventions, not fold-transition
plasticity refresh; surfaced as a follow-up concern in
DONE_WITH_CONCERNS report).
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.