935433850c
Wires the F.3a/b/c backward kernels into IntegratedTrainer's per-step
flow so the FRD head trains end-to-end as a 6th loss-balanced head
alongside BCE/Q/π/V/aux. With labels currently sentinel-initialized to
-1 (F.5 loader will populate from forward-snapshot lookahead), the
chain produces zero gradients + zero loss — Adam steps are no-ops
modulo β decay, and the encoder receives no FRD-derived signal yet.
The wiring is complete and the path is exercised end-to-end; F.5 just
needs to swap the labels in for the head to start training.
IntegratedTrainer state additions:
* frd_w1_adam / frd_b1_adam / frd_w2_adam / frd_b2_adam — AdamW
instances for the 4 FRD weight tensors (LR mirrored per-step from
ISV[RL_FRD_LR_INDEX=499], seed 1e-3 per F.1).
* frd_labels_d — owned [B × FRD_N_HORIZONS] i32 buffer, sentinel-
initialized to -1 (every entry "missing horizon" → softmax_ce_grad
zeros loss + grad for every row). F.5 loader integration overwrites
pre-step from forward-return-bucketed labels.
LossLambdas extension:
* Added `frd: f32` field, default 1.0
* read_loss_lambdas_from_isv reads slot 498 (RL_FRD_LAMBDA_INDEX)
with the standard zero-sentinel bootstrap path
* Doc-comment updated: "5 heads / 5.0" → "6 heads / 6.0"
IntegratedStepStats extension:
* Added `l_frd: f32` — mean CE across (B × FRD_N_HORIZONS) rows
* step_synthetic returns the real l_frd from the bwd chain; the
new combined l_total formula includes `lambdas.frd × l_frd / 6`
step_synthetic bwd chain — inserted between Step 9 (Q/π/V Adam) and
Step 10 (grad_h_t_combined zero+accumulate):
1. softmax_ce_grad → frd_grad_logits_d + frd_loss_per_b_h_d
2. layer2_bwd → frd_grad_w2_pb_d, frd_grad_b2_pb_d, frd_grad_hidden_d
3. layer1_bwd → frd_grad_w1_pb_d, frd_grad_b1_pb_d, frd_grad_h_t_d
4. 4× reduce_axis0 to collapse per-batch scratch → final grads
5. 4× AdamW.step on w1/b1/w2/b2
6. read loss_per_b_h via mapped-pinned, average → l_frd_host
Step 10 grad_h_t_combined accumulation adds a third λ-weighted call:
accumulate_grad_h(frd_grad_h_t_d, lambdas.frd, &mut combined)
With sentinel labels (F.4 state) this contributes zero gradient to the
encoder backward — the wiring is exercised but silent. F.5 makes it
active by providing real labels.
alpha_rl_train diag JSON gains:
* "loss": { ..., "frd": stats.l_frd, ... }
* "lambdas": { ..., "frd": stats.lambdas.frd, ... }
Verification (RTX 3050 Ti):
* cargo check -p ml-alpha + --examples → clean
* integrated_trainer_step_with_lobsim_runs_without_panic → ok
(l_total 0.5073 vs prior 0.6087 — ÷6 instead of ÷5 expected;
l_frd=0 confirms sentinel labels are passing through cleanly)
* frd_head 10/10 tests still pass (no regression)
* trade_management_kernels 5/5 → no regression
* audit-rust-consts → 0 flags
F.5 (next, separate scope):
* Loader-side forward-return label generation (mid[i+h] - mid[i])/σ
bucketed into FRD_N_ATOMS=21 atoms over the ISV-driven ±range_σ
* Populate trainer.frd_labels_d before each step_with_lobsim call
* That unlocks the supervised learning signal; FRD entropy_mean
should start dropping below ln(21) in diag as the head trains.
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.