19bb3bc3c8
Smoke train-k95mj epoch 0 + epoch 1 printed
`trade_outcome_ce=0.000e0` — K=3 CE EMA stuck at Pearl A sentinel
across all training steps.
Root cause chain:
1. insert_batch is called with bs = total = 4_096_000 (cf-mult-
expanded), replay cap = 300_000 (L40S GpuProfile). Tail-clip:
off = 3_796_000, slice(off..) takes the last 300K elements.
2. Prior fix (256a5fa5a) filled CF half [base_total, total) with
-1 mask via cuMemsetD32Async. Last 300K elements at
[3_796_000, 4_096_000) are entirely in this CF half → 100%
mask.
3. Replay ring fills with 300K mask labels. PER gather samples
batches → every batch has label == -1 for all samples →
aux_trade_outcome_loss_reduce returns
loss = 0 / fmaxf(valid=0, 1) = 0 every step.
4. aux_outcome_ce_ema_update bootstrap requires `loss > 0.0f`;
never fires → ISV[538] pinned at 0.0 sentinel.
Fix: CF half mirrors the on-policy half (matches K=2 sibling
aux_sign_labels which writes the same bar-resolved label to both
halves). Replace cuMemsetD32Async(-1) + single memcpy with TWO
memcpy_dtod calls (on-policy half + CF half), both sourced from
the same exp_aux_to_label_per_sample[base_total].
Semantic justification: the K=3 outcome label depends on the bar's
trade-close event, not the action. The CF action's hypothetical
trade outcome at the same bar approximates to the same label. The
B4b-1 kernel writes -1 to non-close slots (~97%) and 0/1/2 to
close slots (~3%); duplicating into CF preserves this sparsity →
ring tail retains ~7-9K real labels per 300K-element fill →
Pearl A bootstraps → K=3 head trains.
Lib suite 1016/0 green. Bug was runtime-only (small-batch lib
tests don't trigger the off > 0 tail-clip path). Audit doc
updated.
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.