679881fa53
Root cause of the L40S 10-epoch smoke Fold 1 loss explosion
(train-7r9zf, final_loss=1.6e16, grad_norm=62B): update_eval_v_range
recomputed [v_min, v_max] every epoch from eval_q_mean_ema ± gap_width
with NO upper bound, while the bounded theoretical range in
config.v_min/v_max (derived from reward_scale/(1-gamma)*1.2, clamp [20, 300])
was only honored at buffer initialization.
The runaway path (visible in the epoch-by-epoch trace):
Epoch 3: Q-range=[-10, 10] — pegged at config bounds ✓
Epoch 4: Q-range=[-10, 14.16] — Q escapes the configured window
Epoch 5: Q-range=[-10, 22.30], loss=43,660 (up from 30)
Epoch 10: loss=1.6e16, grad_norm=62B, Fold 1 converged=false
Positive feedback loop:
Q overestimate → eval_q_mean_ema drifts up → v_max follows unbounded
→ C51 atom support widens → TD targets grow → Q targets grow
→ network chases → gradient explodes → repeat.
Fold 0 stayed in the safe region only because its reward dynamics never
pushed Q far enough to escape ±10; Fold 1's larger window (3.7M bars vs
2.9M) contained the trigger event.
Fix: gpu_dqn_trainer::update_eval_v_range now clamps BOTH the half-width
to (config.v_max - config.v_min) / 2 AND the centre to the range
[config.v_min + half, config.v_max - half], guaranteeing the adaptive
window always fits inside the theoretical bounds. Single source of truth
— the config-level clamp is now actually authoritative.
Verified: multi-trial smoke 5/5 pass, median_q_gap=2.07 (beats 2.00
baseline from commit cb2015ab2), mean_sharpe_ema=7.82. No regression on
Fold 0's normal-range behaviour.
Closes task #31.
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.