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Phase E.1 Task 12. Stabilized H=600 DQN smoke ran end-to-end on full 500K-snapshot data. All three preconditions PASS but the rvr gate FAILS: Q_SPREAD_EMA = 35.54 ≥ 0.05 PASS ACTION_ENTROPY_EMA = 1.91 ≥ 1.099 PASS RETURN_VS_RANDOM_EMA = -2.58 ≥ 0.0 FAIL ← policy WORSE than random EARLY_Q_MOVEMENT_EMA = 0.096 ≥ 0.01 PASS rvr trajectory across 1000 episodes: ep 50 | rollout_R= -11049 | rvr = -1.18 (near random) ep 200 | rollout_R= -9620 | rvr = -0.97 (briefly improving) ep 600 | rollout_R= -18140 | rvr = -2.03 (degrading) ep 1000 | rollout_R= -18272 | rvr = -2.58 (deterministic-bad) Random baseline at H=600 = -5185 mean, std=4953. Trained policy loses 3.5× worse than random. Diagnostics performed: reward_scale=10000 → rvr=-2.37 (no help) alpha_m=0 (vanilla DQN, no Munchausen) → rvr=-2.44 (no help) Root cause: "first-best-action lock-in" of linear Q + ε-greedy. DQN's TD update only modifies Q[a] for the TAKEN action; with ε-decay, the argmax action self-reinforces while other actions' Q stays frozen at random Xavier init. Random policy samples all 9 uniformly → 11% chance of "lucky" close-position at any step → exits bad trades. Trained policy converges deterministic on one bad action → never exits. Per plan: pivot to NoisyNet (Task 19) — parameter-space noise breaks the lock-in. Alternative: wire alpha_logit from Phase 1d.3 stacker (currently hardcoded to 0.0 placeholder) so the policy has actual directional signal to work with. Kill-criteria gate worked as designed — correctly flagged that linear Q + ε-greedy on this env is insufficient without further intervention. Memory note: project_phase_e1_h600_smoke_verdict.md (full analysis + hypothesis tree + recommended next steps).
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.