4ef1d8ebb7
Closes the F1 ep1 catastrophic-overshoot gap exposed by smoke-test-s9h4h
(F0 succeeded with Best Sharpe = 36.03; F1 ep1 grad_norm = 355,009 — 5
orders of magnitude larger than F0 steady-state ~10 — leading to NaN
propagation and grad-clamp-to-zero early stop). Combined fix: (1) Adam
shrink-and-perturb at fold boundary (replace m=0/v=0 with m*=0.1, v*=0.01
to preserve direction while damping magnitude), and (2) a single
adaptive ISV signal driving BOTH lr_eff and clip_eff dampening over
the fold's first ~50 steps.
K — Adam shrink-and-perturb in `GpuDqnTrainer::reset_adam_state`:
- m *= 0.1, v *= 0.01 via existing `dqn_scale_f32_kernel` (loaded as
`scale_f32_ungraphed`); t_pinned still zeroed so bias correction
restarts. Architectural constants (preserve direction / lose magnitude
history) per `feedback_isv_for_adaptive_bounds.md` Invariant 1
carve-out — not tuned. Composes with existing param shrink-and-perturb
(`alpha=0.8`) in `FusedTrainingCtx::reset_for_fold`. Root cause for
F1 overshoot: m=0,v=0 → first Adam step ≈ lr × g / ε → 6 OoM
amplification.
New CUDA kernel `fold_warmup_factor_kernel.cu`:
- Single-block single-thread cold-path producer mirroring
`q_drift_rate_ema_kernel.cu` / `moe_lambda_eff_kernel.cu` shape.
- Reads two grad-norm EMAs (fast α=0.1, slow α=0.001) plus host-passed
step counter; writes ISV[FOLD_WARMUP_FACTOR_INDEX=130] = clamp(fast/slow, 0, 1).
- Bootstrap branches (steps_observed < 200, slow EMA < 1e-6) emit
factor=1.0 (no damping during cold-start). No atomicAdd; no DtoH.
New ISV slot Q_DRIFT_RATE_INDEX → FOLD_WARMUP_FACTOR_INDEX = 130:
- ISV_TOTAL_DIM 130 → 131; layout fingerprint shifts (checkpoint-
incompatible per `feedback_no_legacy_aliases.md`, expected for a
real architecture change).
- FoldReset entries: `isv_fold_warmup_factor` → 0.0 and companion
`isv_grad_norm_fast_ema` → 0.0 (lockstep reset per
`feedback_no_partial_refactor.md`); slow EMA persists across folds
as the cross-fold steady-state baseline.
- Two new mapped-pinned scalars on GpuDqnTrainer (grad_norm_fast_ema_pinned,
grad_norm_slow_ema_pinned) fed by `update_adaptive_clip` from the
same `gr.raw_grad_norm` observation source as the existing adaptive
clip EMA.
Two consumers, both monotone (only dampen, never excite):
- lr_eff = cosine_effective_lr × max(MIN_WARMUP_LR_FRAC=0.05, factor)
via `set_lr` per-step. New `cosine_effective_lr_base` field
on DQNTrainer composes the cosine schedule's per-epoch
baseline with the warmup factor's per-step damping (rather
than overriding the cosine schedule).
- clip_eff = clip_base × (MIN_CLIP_FRAC=0.1 + 0.9 × factor) via new
`set_active_clip` setter on FusedTrainingCtx + GpuDqnTrainer.
Composes with the EMA-derived `clip_base = grad_norm_ema × 2`
that `update_adaptive_clip` just wrote to the pinned slot.
Numerical-stability bounds 0.05 / 0.1 are Invariant 1
carve-outs.
Steady-state behaviour unchanged: factor=1 → lr_eff=lr_base,
clip_eff=clip_base. Fold-boundary behaviour: factor starts at 0 →
lr_eff = 0.05 × lr_base, clip_eff ≈ 0.1 × clip_base; rises to 1 over
~50 steps as the fast EMA catches up to the slow steady-state EMA.
Predicted impact on Plan C smoke F1: 355,009-magnitude transient grad
clipped to ~clip_base × 0.1 ≈ 1.0 (vs 10), Adam state shrunk instead
of zeroed → first step update bounded; grad recovers normally over
~50 steps. Companion to A.1 (prev_epoch_q_mean reset), A.2 (adaptive
Polyak-tau), A.3 (gradient_collapse_counter reset), F+H (kill-criterion
robustness) — completes the fold-boundary state-reset family.
Per `pearl_adaptive_moe_lambda.md` (kernel + ISV slot + bootstrap +
reset + observability template), `pearl_cold_path_no_exception_to_gpu_drives.md`
(GPU-stays-on-GPU even at cold-path cadence),
`pearl_blend_formulas_must_have_permanent_floor.md` (lr/clip floors
are permanent minimums), `feedback_adaptive_not_tuned.md` (lr+clip
ISV-driven), `feedback_isv_for_adaptive_bounds.md` (factor IS the
bound; consumers compose at runtime), `feedback_no_atomicadd.md`
(single-thread reduce), `feedback_cudarc_f64_f32_abi.md` (slot index
passed as i32), `feedback_no_partial_refactor.md` (kernel + slot +
reset + producer + 2 consumers all land together),
`feedback_no_quickfixes.md` (replaces brittle full-reset with
adaptive damping; not threshold relaxation).
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.