7e7c8b5d90
Replaces the flawed Phase F+G LobEnv trait + LobSimEnvAdapter +
MockLobEnv fixture with a GPU-pure env-interaction path.
DELETED:
- LobEnv trait (host-method-per-batch surface)
- MockLobEnv fixture (the toy-bandit pattern that hid the production defects)
ADDED:
- RlLobBackend trait (src/rl/reward.rs): narrow device-oriented
surface (apply_snapshot, pos_and_market_targets_mut, pos_d,
pos_bytes, step_fill_from_market_targets, n_backtests). Single
purpose: break the ml-alpha ↔ ml-backtesting dep cycle. No
mocking layer.
- 3 new GPU-pure kernels (cuda/):
* extract_realized_pnl_delta.cu — reads pos.realized_pnl +
position_lots from device Pos array, writes per-batch
(reward delta, done flag), updates trainer's prev_* buffers.
* apply_reward_scale.cu — element-wise rewards *= ISV[406].
* actions_to_market_targets.cu — 9-action grid → market_targets
{side, size} on device, reading current position_lots for
conditional Flat-from-Long/Short.
- LobSimCuda impls RlLobBackend (ml-backtesting/src/sim/mod.rs)
plus a new step_fill_from_market_targets entry that runs
submit_market_immediate + step_pnl_track without the host-side
targets-vec build. pos_and_market_targets_mut returns disjoint
field borrows (&pos_d, &mut market_targets_d).
- IntegratedTrainer: 3 cubin includes, 3 module/function fields,
launch_apply_reward_scale launcher, prev_realized_pnl_d /
prev_position_lots_d / rewards_d / dones_d buffers,
step_with_lobsim signature switched to RlLobBackend, body's
Step 5 rewritten as kernel-driven (no per-batch host loop, no
individual submit_action calls).
R6 PARTIAL — work R7 lifts:
- Thompson + log_pi stay host (R7 uses R4 kernels)
- mean_abs_pnl EMA stays host (R7 uses R3 ema_update_on_done)
- Advantage/return stays host (R7 uses R3 compute_advantage_return)
- 4 final DtoH copies for step_synthetic's host-slice signature
(R7 lifts step_after_encoder_forward to device-buffer args)
The "DtoH the device rewards/dones at end" comment in
step_with_lobsim documents the boundary. After R7, hot path has
zero host loops other than the now-unused Thompson host loop
(which R7 retires in favour of rl_action_kernel from R4).
Tests:
- integrated_trainer_smoke.rs: rewritten — real LobSimCuda with
synthetic book, one step_with_lobsim call, asserts finite losses
+ λs sum to 1. Smoke gate, not a convergence test.
- dqn_toy.rs, ppo_toy.rs: retired (empty stubs documenting
rationale). Files preserved so rename history survives. The
MockLobEnv toy-bandit pattern intrinsically couldn't catch the
production defects #1, #2, #5 that motivated this rebuild.
ml-backtesting added as ml-alpha dev-dep (cycle-safe: production
direction is ml-backtesting → ml-alpha; dev edge only loads when
building ml-alpha's own tests/examples).
Build cache-bust v29. cargo check + cargo build for all R-phase
tests green (pre-existing heads_bit_equiv index-OOB persists).
Co-Authored-By: Claude Opus 4.7 <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.