0beccd5e82
Two wrong-scale assumptions in the test made it unachievable on the
RTX 3050 Ti / 4 GB laptop this smoke is meant to run on:
1. `train_baseline_rl` was invoked without `--training-profile`, so it
defaulted to `dqn-production`: batch_size=16384, buffer=500k,
num_atoms=52, hidden_dim_base=256. Fused-CUDA init OOMs at
`kan_d_coeff_per_elem alloc` on 4 GB, leaving `fused_ctx = None` and
every subsequent fold failing with "GPU experience collector MUST be
active for CUDA training". Fix: pass `--training-profile=dqn-smoketest`.
2. Default walk-forward windows (12 train / 3 val / 3 test / 3 step) only
yield 2 folds in the 24-month baseline dataset — fold 2's test-end
lands one month past `data_end`. The test's pass-gate is "≥2/3 folds
produce a checkpoint", so a test that can only ever generate 2 folds
is degenerate. Fix: explicit shorter windows (6 / 2 / 2, step 2) that
yield all 3 folds (`6 + 2*2 + 2 + 2 = 14 ≤ 24`, comfortable margin).
Also drops `--epochs 20` → `--epochs 5`. Each fold runs ~5500 batches at
~33 s/epoch on this GPU; 20 × 3 folds ≈ 33 min was exceeding the smoke
budget (kill observed around the 10-minute mark). 5 epochs is ample for
the checkpoint gate — `best_sharpe` saves on the first improving epoch
(epoch 1 in practice), so more epochs add no pass/fail signal, only
wall-clock.
Verified locally: 3/3 folds produce `dqn_fold{N}_best.safetensors`,
total wall-clock ~7 min.
[MULTI_FOLD] fold 0 checkpoint OK
[MULTI_FOLD] fold 1 checkpoint OK
[MULTI_FOLD] fold 2 checkpoint OK
test result: ok. 1 passed; 0 failed ... finished in 416.36s
Docstring updated to reflect new sizing and call out the 4 GB / 24-month
constraints explicitly so the next person reading this can see why the
numbers are what they are.
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.