56efd96cb2
Adds the DQN component of the integrated RL trainer per the plan at docs/superpowers/plans/2026-05-22-integrated-rl-trainer.md. What this commit lands: - DqnHead: linear projection h_t [B, HIDDEN_DIM] -> atom logits [B, N_ACTIONS=9, Q_N_ATOMS=21] with parallel target-network weights. Xavier x 0.01 init (initial softmax-over-atoms approx uniform), scoped_init_seed-guarded per pearl_scoped_init_seed_for_reproducibility. - dqn_distributional_q.cu: forward (one block per (batch, action), one thread per atom) + Bellman categorical-CE backward against a pre-projected target distribution. Atom-softmax fused into backward. - ReplayBuffer (rl/replay.rs): capacity-bounded PER with priority^alpha sampling, random replacement, and TD-error priority update. O(N) cumulative-sum sampling; Phase E may upgrade to a GPU sum-tree once capacity profiling demands it. - rl_gamma_controller.cu: ISV[RL_GAMMA_INDEX=400] producer; gamma adapts toward 0.5^(1/mean_trade_duration) via Wiener-alpha blend (floor 0.4 per pearl_wiener_alpha_floor_for_nonstationary), clamped to [0.90, 0.999]. Bootstrap gamma = 0.99 on sentinel. - rl_target_tau_controller.cu: ISV[RL_TARGET_TAU_INDEX=401] producer; tau adapts multiplicatively from Q-divergence ratio vs anchor 0.01, Wiener-alpha blend with floor 0.4, clamped to [0.001, 0.05]. Bootstrap tau = 0.005 on sentinel. - Action enum + try_from_u32 in rl/common.rs (matches existing ml DQN action grid for cross-system policy comparability). - C51 atom support constants Q_V_MIN / Q_V_MAX in rl/common.rs (kept for Phase E's projection kernel; backward in this commit operates in categorical domain on a pre-projected target). What this commit DEFERS to Phase E: - soft_update_target kernel (struct fields w_target_d / b_target_d are wired and read by the Bellman backward in this commit; the writer lives in Phase E alongside the training-loop tau driver). - Categorical projection kernel that reads gamma from ISV[400] and produces the target_dist input to the backward kernel. - Toy bandit test activation (tests/dqn_toy.rs is #[ignore]-gated; the type contract is locked here, the training loop wires in Phase E). - atomicAdd in the per-batch CE accumulator (Phase E replaces with the warp-shuffle + shared reduce pattern from aux_loss.cu when batches reach production sizes; B <= 32 toy contention is negligible). Per pearl_controller_anchors_isv_driven and feedback_isv_for_adaptive_bounds: gamma and tau are NOT hardcoded constants. They live in ISV[400] / ISV[401], emitted by the controller kernels above, and Phase E consumers read via __ldg(isv + INDEX). Bootstrap values (0.99, 0.005) appear only in the controller kernel as first-observation defaults, NOT baked into the loss kernel. Validation: - SQLX_OFFLINE=true cargo check -p ml-alpha --lib -> clean (1m 03s) - SQLX_OFFLINE=true cargo check --workspace --lib -> clean (42s) - SQLX_OFFLINE=true cargo test -p ml-alpha --lib rl::replay -> 3 pass - Cubins built for all 3 new kernels (sm_80 default). 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.