1ffdf38ddc
Root cause of the long-running "catastrophic train/val Sharpe gap":
backtest_env_kernel was subtracting behavioral shaping (inventory penalty,
churn penalty, opportunity cost) from step_returns BEFORE the metrics layer
computed Sharpe / Sortino / WinRate. Validation was reporting "P&L minus
shaping" as if it were realized P&L.
Both single-step and batched variants of backtest_env_step had the bug.
The shaping terms exist for a reason — they steer the training policy toward
risk-aware behavior. They belong in TRAINING reward, where they shape the
gradient. They do NOT belong in VALIDATION step_returns, which is the
measurement we use to judge whether the model would be profitable in
production. Production deployment doesn't pay an inventory penalty for
holding a position — it pays the actual market P&L of holding it.
Equivalent semantically to running experience_env_step with shaping_scale = 0
(the Phase 3 control scalar landed in commit 3f6eb006c).
Smoke-test verification (TD-propagation, RTX 3050 Ti, 20 epochs):
metric before after
val_Sharpe range -17 to -33 -1.24 to +2.34
epochs val_Sharpe > 0 0 / 20 10 / 20
Best (training) Sharpe ~15-19 +21.04
train Sharpe trajectory unchanged unchanged
The ~30-point Sharpe gap that motivated the entire env-unification effort
was ~80% measurement bug and ~20% legitimate train/val differences. The
remaining gap (val WinRate still anomalously low, 1.5–4.7% vs training
15–23%) suggests one more accounting issue in the val win-rate counter
but is non-blocking — Sharpe is now an honest production-equivalent
measurement.
Kernel signature kept stable (holding_cost_rate, churn_threshold,
churn_penalty_scale, opp_cost_scale args still present, suppressed via
(void) casts) so the Rust launch site does not need to change. Clean
deletion of those args is a follow-up after validation that no other
caller depends on the ABI.
Files touched:
crates/ml/src/cuda_pipeline/backtest_env_kernel.cu (-48 / +35)
Verified: SQLX_OFFLINE=true cargo check -p ml --lib --tests passes.
TD-propagation smoke test runs cleanly end-to-end (33s).
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.