491bf7d3e6
First half of Phase B4b (replay-buffer label scatter chain). Amends the Phase A2 trade_outcome_label_kernel to emit a per-(env, t) output column alongside the existing per-env tile, and adds the collector-side buffer + per-step launch arg. Kernel amendment (trade_outcome_label_kernel.cu): - Added NULL-tolerant `out_labels_per_sample` arg after existing `out_labels`. When non-NULL, writes `out_labels_per_sample[env*L + t] = label` at the same offset as the `trade_close_per_sample[env*L + t]` read. NULL = no-op (preserves Phase A2/A3 contract for callers passing old signature). - Pattern mirrors the K=2 head's `aux_sign_labels` per-(i, t) ring column that threads through the replay buffer. Collector field + alloc + launch: - New struct field `exp_aux_to_label_per_sample: CudaSlice<i32>` sized `[alloc_episodes × alloc_timesteps]`. Sentinel -1 (mask) populated by alloc_zeros + per-step kernel writes — survives until a trade-close event overwrites the env's slot at that t. - Updated Phase B3 launcher in collect_experiences_gpu to pass `self.exp_aux_to_label_per_sample.raw_ptr()` as the new arg. Why split B4b into B4b-1 + B4b-2: the full replay-buffer wireup mirrors the K=2 head's aux_sign_labels pattern across ~8 distinct code sites (replay-buffer struct field, sample destination buffer, direct-to-trainer pointer, setter method, scatter on insert, gather direct, gather fallback, GpuBatchPtrs field). Splitting lets us validate the per-(i, t) producer in isolation before touching the consumer pipeline. B4b-1 (this commit) = producer chain complete. Per-(env, t) column populated correctly every rollout step. Consumer wiring (replay- buffer scatter + trainer setter) is B4b-2's scope. Verification: - cargo check -p ml clean (21 warnings, none new). - cargo test -p ml --lib → 1016/0 on clean runs; pre-existing test_dqn_checkpoint_round_trip NoisyLinear flake still surfaces ~50-70% of full-suite runs (flake predates Phase B4b, unrelated to trade-outcome head — disable_noise() zeros ε but leaves some other randomness source intact). Audit: docs/dqn-wire-up-audit.md Phase B4b-1 section. 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.