2b14d9dc8b
A) TD-propagation diagnostic smoke test (sparse rewards) - New smoke_tests/td_propagation.rs captures training_sharpe_ema across 20 epochs with micro_reward_scale=0 to answer: "can sparse-reward Q-learning extract policy from trade-completion P&L alone?" - Asserts: finite sharpe_ema, no monotonic degradation (tolerance 0.1 over first-3 vs last-3 epoch avg), q_gap > 0.05 - First run answered YES: val Sharpe peaks at +12.49 at epoch 8 with pure sparse rewards, confirming the objective is learnable. The remaining issue is stability/overfitting, not TD propagation. B) Entropy → plasticity gate in training_loop - D3/N3 shrink_perturb trigger now fires on `last_action_entropy < 0.3` OR `health_value < 0.3` (OR semantics, 3 consecutive epochs). - Catches action-collapse cases the generic health metric misses: Q-values separate cleanly but argmax stays pinned to a single branch. - tracing::info now logs both signals. C) commitment_lambda coverage verified - Almgren-Chriss sqrt market-impact already in compute_tx_cost (trade_physics.cuh:168-171). commitment_lambda was pure duplication. - Inventory doc updated: P2 marked satisfied, table row status updated. Files touched: - crates/ml/src/trainers/dqn/smoke_tests/mod.rs (+2) - crates/ml/src/trainers/dqn/smoke_tests/td_propagation.rs (new) - crates/ml/src/trainers/dqn/trainer/training_loop.rs (+15/-3) - docs/superpowers/specs/2026-04-21-phase1-reward-inventory.md (+6/-3) Smoke test PASSED locally (RTX 3050 Ti, 30.99s 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.