39f90f3723
mjzfk + pdgxn diags showed `advantage_var_ratio` and `td_kurtosis`
identically 0 for 100% of every 50k-step smoke. Root cause: the
per-batch `rl_var_over_abs_mean_b` and `rl_kurtosis_b` kernels are
mathematically undefined at b_size=1 (variance of a single sample is
zero; kurtosis of a single sample is 0/0). The kernels correctly
returned 0 in that case but the downstream `rl_rollout_steps` and
`rl_per_alpha` controllers then never saw signal and pegged at MIN
(2048 / 0.4) for the entire run.
## Fix: time-axis Welford-EMA streaming
Replace per-batch reduction with per-step EMA-streaming moments
maintained in ISV slots:
rl_var_over_abs_mean_streaming.cu — maintains streaming mean + M2,
emits var/|mean| each step. Welford-EMA on the batch-mean of
advantages_d (one value at b_size=1, or a single batch reduction
at b_size>1) folded into the time-axis estimator.
rl_kurtosis_streaming.cu — maintains streaming mean + M2 + M4,
emits M4/M2² (Pearson kurtosis) each step. Same Welford-EMA shape
applied to td_per_sample_d batch mean.
Both kernels use STREAM_ALPHA = 0.05 (matches LR_LOSS_EMA_ALPHA —
half-life ≈ 14 steps) so the time estimator smooths over noisy
per-step batch-mean observations. The kernel writes the smoothed
estimate DIRECTLY to the controller-input ISV slot
(RL_ADVANTAGE_VAR_RATIO_EMA_INDEX = 421,
RL_TD_KURTOSIS_EMA_INDEX = 422); the prior downstream
ema_update_per_step calls for these two signals are REMOVED — the
streaming kernel IS the EMA.
## ISV slot allocation
5 new state slots holding the streaming-mean / M2 / M4 per-stream
state. RL_SLOTS_END: 442 → 447.
RL_ADV_VAR_STREAM_MEAN_INDEX = 442 (streaming mean of advantages)
RL_ADV_VAR_STREAM_M2_INDEX = 443 (streaming M2 of advantages)
RL_TD_KURT_STREAM_MEAN_INDEX = 444 (streaming mean of TD-CE)
RL_TD_KURT_STREAM_M2_INDEX = 445
RL_TD_KURT_STREAM_M4_INDEX = 446
Per `pearl_first_observation_bootstrap`: sentinel-zero state
triggers replace-direct first-observation bootstrap (the first
step seeds μ = batch_mean, M2 = 0, M4 = 0 — subsequent steps blend).
Per `pearl_blend_formulas_must_have_permanent_floor`: var/|mean|
denominator floored at 1e-6, M2² denominator floored at 1e-12 —
prevents div-by-zero blow-up when streaming mean / variance is
genuinely zero (cold-start or quiet regime).
## Files
* crates/ml-alpha/cuda/rl_var_over_abs_mean_streaming.cu — new
* crates/ml-alpha/cuda/rl_kurtosis_streaming.cu — new
* crates/ml-alpha/cuda/rl_var_over_abs_mean_b.cu — deleted
* crates/ml-alpha/cuda/rl_kurtosis_b.cu — deleted
* crates/ml-alpha/src/rl/isv_slots.rs — +5 slots
* crates/ml-alpha/src/trainer/integrated.rs — rewired
launchers,
dropped
redundant
ema_update
calls
* crates/ml-alpha/build.rs — swapped
cubin
entries
## Verified gates (local sm_86)
G1 isv_bootstrap ✅
G3 controllers ✅
G4 target_update ✅
integrated_smoke ✅
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.