5462743e80
Phase 2 P1 relocations — the intents behind two deleted reward shaping
terms now live at the correct layer: diagnostic monitoring, not reward.
1. training_sharpe_ema (was w_dsr reward input):
Field already existed. Added to HEALTH_DIAG log line as `sharpe_ema`.
Available for early-stopping, model selection, and exploration gating
without perturbing the objective.
2. action_entropy (was position_entropy_weight reward):
Computed from summary.action_counts at GPU summary download —
Shannon entropy normalized to [0, 1] by ln(n_bins). One-epoch lag
is fine for a diagnostic. Added to HEALTH_DIAG as `action_entropy`.
Stored in new trainer field `last_action_entropy: Option<f32>`.
New HEALTH_DIAG suffix: `diag [sharpe_ema={:.3} action_entropy={:.2}]`.
Verified visible on E1 smoke test epoch 18/19 output.
Also: honest recalibration of Phase 2 results added to the inventory
doc. The earlier commit messages framed "-150 → -17 Sharpe" as a 5×
improvement; in absolute terms Sharpe -17 is still catastrophic (you'd
blow the account). Phase 2 stopped active capital destruction but did
not find alpha. Sharpe_raw per bar went from -0.39 (lot of loss per bar)
to -0.09 (little loss per bar) — still net losing. q_gap=0.17 is a
capacity metric (network CAN differentiate), not profitability.
Path to actual profitability (Sharpe > 0) remains:
- TD propagation verification (Task #8)
- Unified env kernel (Phase 3)
- Possibly a different objective entirely
- Richer features (42 market + 20 OFI may be information-starved)
The rule going forward: before celebrating future improvements, ask
whether the result *crosses zero* (profitable) or is just *less
negative* (still losing).
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.