9114374d25
Closes Phase E of the integrated RL trainer plan (docs/superpowers/plans/2026-05-22-integrated-rl-trainer.md). The integrated trainer can now be exercised end-to-end on a real (or mock) LobSim environment. What this commit lands: - LobEnv trait in src/rl/reward.rs — narrow contract over apply_snapshot + submit_action + step_event. Chosen over a direct ml-backtesting → ml-alpha dep because ml-backtesting already depends on ml-alpha (loader + trunk reuse); a reverse direct dep would cycle. The simulator-side `impl LobEnv for LobSimCuda` completes the wire from ml-backtesting in a follow-up (the trait surface is intentionally small and lobsim-agnostic so other env implementations can plug in). - MockLobEnv in the same module — deterministic toy bandit (action 5 → +1, else → -1, done = true every step). Powers the dqn_toy / ppo_toy / integrated_trainer_smoke gate tests. - IntegratedTrainer::step_with_lobsim — one training step driven by a real LobEnv. Forwards encoder, forwards Q + V + π, reads logits to host, Thompson-samples action per batch (pearl_thompson_for_distributional_action_selection), drives the env, derives real reward / advantages / returns / log_pi_old, and delegates to step_synthetic for the per-head backward + Adam + encoder backward (single source of truth per feedback_single_source_of_truth_no_duplicates). - IntegratedTrainer::eval_expected_q_per_action + IntegratedTrainer::eval_policy_probs_per_action — public eval helpers the gate tests use to inspect the trained Q-distribution / policy without re-implementing device readback in test crates. - dqn_toy / ppo_toy / integrated_trainer_smoke — bodies filled with the real training loop driven by MockLobEnv::toy_bandit. The convergence gates assert argmax_a E[Q(s, a)] == LongSmall and mode π(s) == LongSmall after 300 step_with_lobsim calls. Tests are #[ignore]-gated for CUDA availability per the project's test discipline; activate via `cargo test -p ml-alpha --test dqn_toy -- --ignored` on a CUDA host. - rl_rollout_steps_controller.cu — ISV[404] producer. Rollout length adapts to var(advantage)/|mean A|: noisy advantages → grow rollout (more samples per PPO update), stable → shrink (fresher data). Bootstrap 2048 (PPO default), bounds [256, 8192], Wiener-α blend with floor 0.4 per pearl_wiener_alpha_floor_for_nonstationary. - rl_per_alpha_controller.cu — ISV[405] producer. PER priority exponent α adapts to TD-error kurtosis EMA: heavy tails → raise α to concentrate on the informative tails, light tails → keep α near the canonical 0.6. Bootstrap 0.6 (Schaul 2016), bounds [0.3, 1.0]. Both controllers honour pearl_first_observation_bootstrap (sentinel 0.0 → first-emit writes bootstrap, subsequent emits Wiener-α blend) and pearl_controller_anchors_isv_driven (no hardcoded constants — every adaptive hyperparameter sourced from ISV). build.rs registers both kernels and bumps cache-bust v23 → v24. Phase F follows with the reward-shaping ISV controller (RL_REWARD_SCALE_INDEX=406) + per-trade PnL extraction calibration. Phase G adds the Argo workflow + dispatcher. Phase H runs the actual training + backtest smoke and tests the G8 gate (profit_factor > 1.0). Verification: - cargo check --workspace --lib clean (no warnings) - cargo test -p ml-alpha --lib: 66 passed (was 63; +3 mock_bandit_* tests in rl::reward), 0 failed, 6 ignored - cargo build -p ml-alpha --test dqn_toy --test ppo_toy --test integrated_trainer_smoke clean - integrated_trainer_loss_lambdas_default_equal_weight (non-ignored) still passes
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.