27f8e332da
Adds the SP18 v2 Phase 0 B-leg observability scaffold per the plan's
Task 0.2 spec:
- New kernel `td_error_mag_ema_kernel.cu` (single-block × 256 threads):
reads `td_errors_buf [B]` (already populated by C51/MSE loss for PER
priority recomputation, post-train-step), block tree-reduces
`mean(|td_errors[b]|)` (no atomicAdd), and EMA-blends into
`ISV[TD_ERROR_MAG_EMA_INDEX=493]` via Pearl-A first-observation
bootstrap (sentinel 0.0 → REPLACE) + fixed α=`WELFORD_ALPHA_MIN=0.4`
per `pearl_wiener_alpha_floor_for_nonstationary`. The TDB_* Welford
accumulators in slots [498..504) are RESERVED for the Phase 4
q_next_target Wiener-α chain — not used in Phase 0.
- Cubin manifest entry in `crates/ml/build.rs` + `TD_ERROR_MAG_EMA_
CUBIN` re-export in `gpu_dqn_trainer.rs`.
- `sp18_td_error_mag_ema_kernel` field on `GpuDqnTrainer` + cubin load
on the trainer's stream + `launch_sp18_td_error_mag_ema_update()`
cold-path launcher + `read_sp18_td_error_mag_ema()` convenience
wrapper.
- New `sp18_v_share_history: [[f32; 4]; 5]` field on `DQNTrainer` —
fixed-size ring buffer of the last 5 epochs of per-branch V_SHARE
EMA readings (slots [478..482) per SP17 Phase 3.2). Initialised to
`[[NaN; 4]; 5]`; epochs 0–3 emit `nan` as the slope and skip the
ISV write; epoch 4 onward computes `(EMA[now] - EMA[now-4]) / 4`
per branch and writes the dir-branch slope to
`ISV[V_SHARE_TREND_DIAG_INDEX=496]`.
- Two new HEALTH_DIAG lines in `training_loop.rs` at the per-epoch
boundary (right after the SP18 reward_decomp line):
HEALTH_DIAG[N]: v_share_traj [dir_slope=X mag_slope=Y ord_slope=Z urg_slope=W]
HEALTH_DIAG[N]: td_error_pre [magnitude_ema=X]
V_SHARE slope is host-side computation against the ring buffer
(`(now - now_m4) / 4` per branch). TD-error magnitude is post-blend
ISV slot 493 read (producer fires inside `read_sp18_td_error_mag_
ema()`). Pre-fix baseline for the B-DD9 ratio gate
(`avg(|TD-error|) ratio post-fix / pre-fix ∈ [0.5, 5.0]`).
- New GPU oracle tests in `crates/ml/tests/sp18_hold_reward_oracle_
tests.rs`:
* `td_error_mag_ema_pearl_a_bootstrap` — synthetic td_errors with
closed-form mean(|td|)=1.125; pre-populate slot at sentinel;
assert post-launch slot equals the mean (Pearl-A direct-replace).
* `td_error_mag_ema_blend_post_bootstrap` — synthetic td_errors
with mean(|td|)=0.5; pre-populate slot at non-sentinel 1.0;
assert blend equals `(1 - 0.4) × 1.0 + 0.4 × 0.5 = 0.8`.
Pure observability — no production-path consumer in this commit. No
reward changes, no Bellman target changes, no kernel modifications to
the action-selection or training paths. Per `feedback_no_partial_
refactor` the kernel + cubin manifest + buffer + launcher + ring
buffer + HEALTH_DIAG emit + GPU oracle tests all land atomically.
Verification:
SQLX_OFFLINE=true CUDA_COMPUTE_CAP=86 cargo check --workspace clean.
All 4 GPU oracle tests pass on RTX 3050 Ti (2.09s):
reward_decomp_per_action_gpu_oracle, reward_decomp_empty_bin,
td_error_mag_ema_pearl_a_bootstrap, td_error_mag_ema_blend_post_bootstrap.
Existing slot lock + state_reset_registry tests still pass.
Plan: docs/superpowers/plans/2026-05-08-sp18-reward-shape-hold-attractor.md
§ Phase 0 Task 0.2.
Audit: docs/dqn-wire-up-audit.md § "SP18 v2 Phase 0 Task 0.2".
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.