8a5e7d316c
C51 Q-var slot + q_abs_ref outlier clamp
Bundles 4 ISV signal-quality fixes from the audit at
docs/superpowers/specs/2026-04-23-isv-signal-quality-audit.md.
1. Regime signals (slots 8-11) now batch-aggregate over all B samples
instead of reading sample 0 only. Adds `int batch_size` kernel arg
and fixes a latent stride bug (launch passed STATE_DIM=104 but
states_buf has stride STATE_DIM_PADDED=128 — sample 0 worked by
luck because both strides land at the same offset for row 0).
99.994% information loss closed.
2. Health (slot 12) now couples to outcomes: sigmoid(0.1 × sharpe_ema)
EMA-blended. New ISV slot 22 = SHARPE_EMA_INDEX persists the
Rust-side training_sharpe_ema. ISV_DIM 22→23. Prior component-
aggregation health formula was ANTI-correlated with Sharpe
(r=-0.765 per audit) because its components saturated at 0/1
boundaries (q_gap=1.0 / q_var=1.0 for 19/20 epochs, grad_stable
stuck at 0.0 for 20/20 epochs). New formula's sensitive sigmoid
region [-10, +10] Sharpe matches the observed magnitude range.
The Rust-side write_isv_signal_at is preserved as a fallback
initializer at epoch boundaries — the kernel then overwrites
slot 12 every training step based on slot 22's current value.
3. Slot 3 now carries C51 Q-distribution variance (wired via third
c51_loss_reduce launch, same pattern as td_error fix 7f92fa242).
Previously zero-initialised with no writer. Renamed from
"ensemble_var_scratch" to "q_var_scratch" to reflect actual
semantics — it is the batch mean of q_var_buf_trainer (atom-
spread variance from the C51 distributional head), NOT multi-head
ensemble disagreement. True multi-head ensemble variance remains
a separate follow-up if/when a per-head ensemble is wired.
Slot 4 (velocity derivative of slot 3) becomes meaningful
automatically.
4. q_dir_abs_ref (slot 21) and q_abs_ref (slot 16) gain outlier
clamps before EMA update: clamped = min(raw, 10×current + 1) to
prevent single ±10⁵ Q-excursions from poisoning 20 epochs. The
+1 floor handles the cold-start case where current EMA is near 0.
Slot 21 is especially load-bearing because it feeds the Kelly
conviction denominator (q_range / q_dir_abs_ref).
ISV_DIM bump 22→23 changes the flat-param buffer size (w_isv_fc1
tensor [68] grows from [16,22] to [16,23] → +16 floats). Xavier
init at gpu_dqn_trainer.rs:13819 picks up the new dimension
automatically. Pinned allocs scale via ISV_DIM * size_of::<f32>()
expressions. No safetensors / checkpoint format currently encodes
ISV layout directly — checkpoint_state_dim/num_actions/hidden_dims
are the only hashed architectural fields — but the flat param
buffer content differs, so existing live checkpoints will require
rebase. Acceptable for the current pre-production dev state.
Tests: workspace cargo check --workspace --tests passes clean. Ran
magnitude_distribution smoke 3 times locally (RTX 3050 Ti); health
in HEALTH_DIAG now tracks negative Sharpe regime (0.50→0.38
trajectory over 20 epochs with mean sharpe_raw=-7) rather than
climbing monotonically as in the audit's train-mdh86 logs
(0.50→0.64 against Sharpe +34→-67). The magnitude_distribution
eval-dist H10 gate sometimes passes, sometimes fails depending on
the training seed — the pre-existing H10 flakiness persists. The
pre-existing 14 ml-lib test failures are unrelated (OFI features
missing / production profile config mismatch / test_batch_size
test environment issues — all fail on HEAD as well).
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.