f9a8a5aa9a
Adds a parallel read path that reports the policy's intended mag_idx BEFORE Kelly/margin caps and before the Hold/Flat dir_idx forces mag=0. This exposes whether the magnitude Q-head is learning state- dependent preferences, independent of the Kelly cold-start cap that was masking it via actual_mag decoding (kelly_position_cap warmup_floor=0.5 + safety=0.5+0.5*health pinning abs_pos <= 0.375). Kernel changes (experience_kernels.cu): - experience_action_select: new trailing optional arg `out_intent_mag` (int*, NULL=skip). Populated AFTER the existing mag_idx selection via a strict argmax over q_b1, ignoring the Hold/Flat mag=0 forcing, with the same higher-bin-wins tie-break used in the b2/b3 paths. Uses q_sign so the intent stays consistent with contrarian mode. - New scatter_intent_chunk kernel: copies step-major chunked intent [chunk_len, n_windows] into window-major intent_history [n_windows, max_len], mirroring the actions_history layout. Rust wiring (gpu_backtest_evaluator.rs): - New fields intent_mag_buf, chunked_intent_mag_buf, scatter_intent_kernel. Buffers allocated alongside existing chunked buffers in ensure_action_select_ready. intent_mag_buf is zeroed by reset_evaluation_state so short rollouts don't read stale data. - submit_dqn_step_loop_cublas appends the new arg to the action_select launch and launches scatter_intent_chunk immediately after, before the env_batch_kernel (which never touches intent_mag_buf). - read_eval_intent_magnitude_distribution mirrors read_eval_action_distribution_per_magnitude but decodes raw mag_idx (a as usize) rather than the factored action encoding. Training-path call site (gpu_experience_collector.rs): passes NULL (0u64) for the new arg — training does not collect intent history. Trainer wiring: - new last_eval_intent_magnitude_dist field + accessor; populated in metrics.rs::evaluate_on_gpu next to last_eval_magnitude_dist. Smoke test (magnitude_distribution.rs): adds [EVAL_INTENT_MAG_DIST] println line; no new assertions. Diagnostic-only, no new feature flag — production behaviour unchanged. All 7 files build cleanly with no new warnings. [testing: smoke compiles + runs, still fails on the existing H10 assertion (EVAL_DIST Quarter=1.000 driven by Kelly cold-start cap), EVAL_INTENT_MAG_DIST shows Quarter=0.357 Half=0.045 Full=0.599 at 20-epoch smoke on local RTX 3050 Ti — confirming the magnitude head prefers Full ~60% of the time while the Kelly-capped realised distribution pins to Quarter.] 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.