2937da8898
Two smoke-driven fixes for issues surfaced by smoke v1 (train-grfcw).
Fix 1: inflated Return (financials.rs)
- Prior `exp(sum(log(1+r_i))) - 1` produced `Return=+8.730e19%` on
~4M step_returns/epoch. Math correct but metric meaningless.
- Fix: ANNUALIZED compounded return (CAGR). For n_returns ≥
bars_per_year, scale log_growth by `bars_per_year / n_returns`
then exp. Short rollouts (tests/warmup) fall back to total
compounded. Clamped to log-space `[-23, +20]` for display sanity.
- Smoke v1 epoch 3 with fix: 24.7% annualized (was +e19%).
- Documented v1→v2→v3 history in comment.
Fix 2: missing dqn_fold{N}_best.safetensors (training_loop.rs)
- Smoke v1 evaluate phase failed with "Failed to load DQN
checkpoint" for fold 0 and fold 1.
- Root cause: async best-worker swallowed errors non-fatally; with
3 epochs and checkpoint_frequency=10, periodic never fired; if
async failed, NO checkpoint existed.
- Fix: guaranteed final save at training end. After async drain,
restore_best_gpu_params + serialize + sync callback(is_best=true).
Idempotent if async already wrote; authoritative if it failed.
All errors here non-fatal (training succeeded; eval reports its
own missing-ckpt at proper boundary).
Files changed:
- crates/ml/src/trainers/dqn/financials.rs: v3 annualized return
- crates/ml/src/trainers/dqn/trainer/training_loop.rs: final save
- docs/dqn-wire-up-audit.md: 2026-05-11 audit entry
Verification (passing):
- cargo check -p ml --tests --features cuda: 0 errors
- cargo test -p ml --lib financials: 7/7
- cargo test -p ml --lib sp21_isv_slots: 4/4
- sp20_aggregate_inputs_test: 12/12
- sp20_phase1_4_wireup_test: 2/2
- sp20_emas_compute_test: 4/4
- sp20_controllers_compute_test: 7/7
- sp21_per_trade_predicted_q_test: 3/3
Total: 39 tests, 0 failures.
Smoke v2 (train-rl5x2) is on commit ad99b79e0 and won't have these
fixes. A v3 dispatch after this commit validates both fixes end-
to-end.
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.