119c3a15f4
IntegratedTrainer now owns an FrdHead instance and per-step buffers
(frd_hidden_d [B × FRD_HIDDEN_DIM=64], frd_logits_d [B × FRD_OUT_DIM=63]).
The forward kernel runs in step_with_lobsim immediately after the
current-snapshot encoder forward, reading h_t_borrow and producing the
3-horizon × 21-atom return-bucket logits.
step_with_lobsim FRD forward placement rationale: it has to read
self.perception.h_t_view() AFTER the second forward_encoder(snapshots)
call (which lands h_t at slot K-1), but BEFORE any downstream
consumer of the encoder state — so right between Step 1b and Step 2.
This keeps the FRD output aligned with the same h_t that the Q / π /
V heads see for action sampling.
alpha_rl_train diag emits a new "frd" block per step:
"frd": { "h1": {"entropy_mean", "argmax_mean"}, "h2": ..., "h3": ... }
At init (Xavier × 0.1, b1=b2=0) the per-horizon softmax is near-
uniform → entropy_mean ≈ ln(21) = 3.044 and argmax_mean drifts around
the uniform expectation of 10. As supervised training kicks in (F.3),
entropy drops and argmax tracks the realized forward-return mode per
horizon — this is the observable signal that lets us catch a broken
backward kernel before cluster smoke.
Verification:
* cargo check -p ml-alpha --examples → clean
* integrated_trainer_step_with_lobsim_runs_without_panic → ok
(1.66s, b_size=1, full step path through encoder + FRD + Q/π/V)
* audit-rust-consts → 0 flags
* trade_management_kernels (5/5) + frd_head (3/3) → still pass
F.3 (backward kernel + finite-diff tests + label generation in loader
+ λ_frd-weighted loss accumulation into stats.l_total) is the next
chunk. FRD-gate (P9) and FRD label-cache wiring are separate scope.
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.