9732a667cc
Partner to Phase C (DQN/C51). Adds the PPO component of the integrated
RL trainer per docs/superpowers/plans/2026-05-22-integrated-rl-trainer.md.
What this commit lands:
- PolicyHead: linear h_t [B, HIDDEN_DIM] -> logits [B, N_ACTIONS=9].
Softmax fused into surrogate kernel.
- ValueHead: linear h_t [B, HIDDEN_DIM] -> scalar V(s) [B].
- ppo_clipped_surrogate.cu: fwd kernel computes pi_new probabilities,
the clipped surrogate L_pi = -min(ratio*A, clip(ratio,1+-eps)*A),
value MSE, entropy bonus. Bwd kernel computes per-logit grad with
clip-mask. eps and entropy coef are read from ISV[402] / ISV[403],
NOT hardcoded.
- RolloutBuffer: capacity-bounded on-policy buffer with Q-bootstrapped
advantage A_t = Q(s_t,a_t) - V(s_t) and done-aware backward-returns.
- rl_ppo_clip_controller.cu: ISV[402] producer; eps adapts to keep
KL ~ 0.01 target; bootstrap eps=0.2; clamp [0.05, 0.5].
- rl_entropy_coef_controller.cu: ISV[403] producer; coef adapts to keep
entropy >= 0.7*ln(9); bootstrap 0.01; clamp [0.0, 0.05].
What this commit DEFERS to Phase E:
- Toy bandit test activation (test stub is #[ignore])
- atomicAdd in surrogate loss accumulator (replaces with warp-shuffle
reduce when integrated with the full training loop, same plan as
dqn_distributional_q_bwd)
- V-head gradient kernel (single MSE backward - trivial; Phase E's
loss-combine path will handle it inline)
- Boundary case in clipped surrogate bwd (Phase D uses 'zero outside
clip; standard PG inside'; Phase E may refine the sign-of-A edges)
Per pearl_controller_anchors_isv_driven and feedback_isv_for_adaptive_bounds:
eps and entropy coef are read from ISV at consumer site, not hardcoded.
Bootstrap values shown in the controllers (eps=0.2, coef=0.01) are what
first-observation emits produce, not const defaults baked into the loss
kernel.
Validation:
- SQLX_OFFLINE=true cargo check -p ml-alpha --lib: clean (54.96s)
- cargo test -p ml-alpha --lib rl::rollout: 1 passed
- cargo test -p ml-alpha --test ppo_toy --no-run: compiles
- All 3 new cubins built into target/debug/.../out/
Companion to Phase C (commit 56efd96cb). Phase E wires both DQN and PPO
heads into the IntegratedTrainer with LobSim and reward shaping.
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.