104fe81ca8
Root cause of the step-4 NaN that has blocked the Phase 2.1 smoke gate since session start. `window_tensor_d` is allocated [B, K, ENCODER_INPUT_DIM=56]: - Features [0..40) are per-snapshot market features (snap_feature_assemble_batched) - Features [40..56) are per-batch broadcast context (rl_encoder_context_broadcast writes via offset `idx * 56 + 40`) But `variable_selection_fwd` and `_bwd` read x with stride `VSN_FEATURE_DIM=40`. For row 0 the stride happens to align with the [B, K, 56] layout. For row 1 onwards the kernel reads MIXED data across row boundaries — broadcast context bleeds into VSN's "snap features" view, and snap features bleed across K boundaries. The bleed produces nonsense for most steps but is bounded enough to train through. Around step 4, accumulated trade_context magnitudes (time_in_trade, unrealized_R, position_lots) get large enough to overflow VSN's softmax in the bleed-affected rows → NaN cascades through the encoder forward → backward computes NaN gradients → AdamW applies them → step 5's forward graph sees NaN weights → G8 abort. Fix: introduce `VSN_X_ROW_STRIDE = 56` (= ENCODER_INPUT_DIM) and use it for all input reads of `x` in both forward and backward: - variable_selection_fwd:41 (x_row = x + row * 56) - variable_selection_bwd:173 (x_i read at stride 56) - variable_selection_bwd:204 (xj read at stride 56) Output strides (gates_out, y, grad_W, grad_b, grad_x) remain at VSN_FEATURE_DIM=40 because: - gates_out / y feed Mamba2 L1 which reads at in_dim=40 stride - grad_W / grad_b accumulate into [FEATURE_DIM, FEATURE_DIM] / [FEATURE_DIM] - grad_x is write-only (audit: `vsn_grad_x_d` has zero downstream consumers per grep of crates/ml-alpha/) Verification: - 10/10 runs clean at seed=16962, n-steps=10 (was 4-7/10 NaN before) - 1000-step smoke clean at seed=16962: l_q≤1.76, l_v≤1.18, wr climbing 0.36→0.55 (the surfer pattern emerging as expected per pearl_dd049d9a4_surfer_baseline_verified) - Phase 2.1 smoke gate (l_q<3.0, l_v<2.0, no NaN over 1000 steps) PASSED Per pearl_atomicadd_masks_v_instability commits 60e96bf55..4e629830b for the full diagnostic chain (49 nan_scan labels, 4 sub-agent dispatches, 13 commits localizing the bug from "somewhere in the trainer" down to this single line). Co-Authored-By: Claude Opus 4.7 <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.