91c4e499d2
R9 cluster smoke alpha-rl-qzstj diag exposed that 6 of 7 controllers
held at bootstrap for the entire 1000-step run because their input
EMAs were never populated. Only `mean_abs_pnl_ema` was wired (via
ema_update_on_done on reward_abs_d). The other 6 EMA producers
existed as generic kernels (ema_update_per_step / ema_update_on_done)
but nothing computed the per-step input signals to feed them.
This commit wires the 3 EMAs whose source signals are ALREADY
computed and live in trainer per-step buffers (Phase A — cheapest
to wire):
* `entropy_observed_ema` (ISV[420] → rl_entropy_coef controller)
← per-batch entropy `entropy_d` from PPO surrogate forward.
`ema_update_per_step` does mean-reduce internally, so this is
a single launch with entropy_d as input (b_size native).
* `advantage_var_ratio_ema` (ISV[421] → rl_rollout_steps)
← `var(advantages) / max(|mean(advantages)|, 1e-6)` reduction
on advantages_d. New kernel `rl_var_over_abs_mean_b`
(two-pass shared-mem tree-reduce) writes scalar to trainer-
owned `ema_input_scratch_d[1]`, then `ema_update_per_step`
consumes with b_size=1.
* `td_kurtosis_ema` (ISV[422] → rl_per_alpha)
← `E[(x-μ)⁴] / σ⁴` kurtosis reduction on td_per_sample_d
(R7d's per-sample CE loss from dqn_distributional_q_bwd).
New kernel `rl_kurtosis_b` (three-pass shared-mem tree-reduce)
writes scalar to ema_input_scratch_d, then ema_update_per_step
with b_size=1.
## Wiring placement
* `advantage_var_ratio` update: in `step_with_lobsim` immediately
after `compute_advantage_return` populates `advantages_d`. Fires
BEFORE the next step's controllers, so the controller sees the
fresh signal one step later.
* `entropy_observed` + `td_kurtosis` updates: in `step_synthetic`
AFTER the encoder backward (deferred from their natural in-place
locations to avoid a borrow-checker conflict with `h_t_borrow`
which holds `&self.perception` through the entire forward chain).
One-step lag — same as advantage_var_ratio for the same reason
(controllers fire in the NEXT step_with_lobsim).
## Trainer-owned scratch
Single `ema_input_scratch_d: CudaSlice<f32>` of length 1. Reused
across the var-over-abs-mean and kurtosis launches in any given
step — they're stream-serialised, so the second reducer's write
to slot 0 strictly follows the first reducer's consumer (the
corresponding ema_update_per_step). Cheap (4 bytes); avoids
two separate scratches.
## Why a 1-float scratch + b_size=1 ema_update
`ema_update_per_step` expects `obs_d[b_size]` and computes per-step
mean as `Σobs / b_size`. Passing a 1-element buffer gives
mean = obs[0] = the reduce kernel's scalar output. The EMA then
blends `prev` toward that scalar via Wiener-α (or bootstraps on
first non-zero per `pearl_first_observation_bootstrap`).
This pattern lets the existing per-step EMA kernel handle scalar
inputs without modification — the alternative (a dedicated
"ema_scalar_per_step") would duplicate logic per
`feedback_single_source_of_truth_no_duplicates`.
## Verified gates (post-fix, local sm_86)
G1 isv_bootstrap ✅
G3 controllers_emit ✅ (test pre-seeds inputs, so
wiring path not exercised)
G4 target_soft_update ✅
G6 r7d_per_wiring ✅
R3, R4, smoke ✅
Local smoke at b_size=1 won't exercise kurtosis (kernel returns 0
at b_size<2 → cold-start gate holds per_α at bootstrap). var_over_
abs_mean does fire because b_size=1 has a well-defined (degenerate)
variance of 0. Cluster smoke at b_size=1 will mostly exercise
entropy_observed.
## What's NOT in this commit (Phase B — 3 EMAs left)
* `kl_pi_ema` (ISV[419] → rl_ppo_clip)
needs: D_KL approximation between log_pi_old and log_pi_new.
Both buffers exist in trainer; need a small subtract-and-mean
kernel OR extend PPO surrogate forward to emit kl_per_batch.
* `q_divergence_ema` (ISV[418] → rl_target_tau)
needs: `‖W_online − W_target‖₂`. Both DQN weight buffers
accessible via dqn_head fields; need a small L2-diff-norm
kernel called after soft_update_target.
* `trade_duration_ema` (ISV[417] → rl_gamma)
needs: per-batch step counter (i32, b_size, trainer-owned)
that increments each step and emits its value on done. Needs
a small `step_counter_update` kernel + the counter buffer.
These three need NEW signal-derivation kernels (not just reductions
over existing buffers). Separate commit.
## Cluster smoke expected diag change
Before this commit: 6 of 7 EMA input slots stuck at 0.0 for all
1000 steps. After: ISV[420], ISV[421], ISV[422] populated each
step. The corresponding controllers (coef, n_roll, per_α) should
visibly adapt after the first few non-zero observations.
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.