15818dce01
Root cause of Q-value saturation at +/-50 seen in train-6nbx5 after ISV v-range unification (9deda5f65,11df03785): cold-start path in `update_eval_v_range` latched `q_std_ema = q_std.max(0.01)` on the first epoch. But that first `q_std` is dominated by the +/-50 bootstrap atom-spread (scaffolding set in construct/reset), NOT by real Q-distribution spread. Result: `3*std_ema` exceeds `min_half_floor=10` and approaches `abs_half=50` immediately, atoms stay wide next epoch, next `q_std` confirms that width, EMA never escapes. Q saturated at +/-abs_half every run. Fix 1 (gpu_dqn_trainer.rs:3106-3120): seed `eval_q_std_ema = min_half_floor / 3.0` at cold start so initial `half = 3 * std_ema = min_half_floor` exactly. Adaptive-rate EMA (alpha clamped to [0.01, 0.3]) then relaxes upward only if genuine Q-spread warrants it. Breaks the self-confirming initialization. Fix 2 (training_loop.rs:479-494): remove leftover pre-clamp of reward quantiles to `config.v_{min,max}`. That was from the earlier quantile-clamp fix (d38a8cf99). Phase 2c (9deda5f65) moved the per-branch clamp inside `warm_start_atom_positions`, which reads each branch's [centre-half, centre+half] from the ISV pinned bus. An outer static clamp to the wider config bound is redundant double-clamping and hides which layer owns the support. Pass raw quantiles through to warm_start. Validated locally: SQLX_OFFLINE cargo check -p ml passes (only pre-existing warnings). Next: push + L40S validation run. Diagnostic instrumentation from423ac460bremains in place to confirm (center, half) trajectory on the next run — will be removed once validated. 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.