8b2ac1e577
URGENT correctness fix surfaced by checkpoint deep-dive after Smoke 2 failed
the WIN gate (mean_run_len ratio = 1.0× vs target ≥10×; all 5 horizons
uniformly ~2.4 events).
THREE INDEXING BUGS identified:
1. tau_reorder produces bucket-grouped tau_all_d, but Controller B's
tau_clamp_kernel reads bucket_id_per_channel[c] where c is the
POSITION in the reordered buffer (not the original channel index)
→ wrong bucket-IQR lookup → τ never constrained → buckets collapse
(deep-dive showed all 5 buckets had nearly identical τ ranges
[0.07, 74] except bucket 4 reaching 878).
2. heads_w_skip grad mask ran but Adam (m, v) momentum from BEFORE the
transition re-introduced gradient signal across the transition →
off-bucket positions stayed nonzero throughout training
(deep-dive: 512/512 = 100% of off-bucket positions nonzero in
trunk_best_h6000.bin).
3. per-branch CfC kernel read w_in[c * HIDDEN_DIM + k] with c =
bucket-grouped position, but W_in rows are indexed by ORIGINAL
channel → kernel read wrong rows for each output → outputs were
essentially random per-channel.
ALPHA FIX: skip the reorder entirely, use bucket-filter throughout:
- Removed tau_reorder_kernel; cfc.tau_d stays in original-channel layout.
- Added channels_in_bucket_kernel that populates a
[N_HORIZONS × MAX_BUCKET_DIM] lookup (original channel index per
(bucket, within-bucket-position)).
- Per-branch CfC fwd+bwd now reads channels_in_bucket[branch][tid]
→ original_c, then uses original_c for w_in/w_rec indexing. All
weights stay in original layout consistently.
- Controller B's tau_clamp_kernel now correctly operates on original-
channel cfc.tau_d with bucket_id_per_channel[c] lookup (no position-
vs-channel confusion).
- Added zero_off_bucket_kernel + three-layer defense for heads_w_skip
block-diagonal invariant:
(a) At transition: zero off-bucket params + zero Adam (m, v)
moments via opt_heads_w_skip.m_mut() / v_mut() accessors.
(b) Per-step: heads_w_skip_grad_mask_apply_kernel zeros off-bucket
gradients before Adam step (unchanged from prior follow-up).
(c) Per-step: zero_off_bucket_kernel zeros off-bucket params after
Adam step, catching any drift from Adam's ε denominator or
weight decay.
New AdamW::m_mut()/v_mut() accessors enable the projection at transition.
GPU oracle tests: 19 total (16 in bucket_transition + 3 in cfc_step_per_branch).
New tests verify:
- channels_in_bucket_kernel correctness under non-contiguous bucket assignment
- zero_off_bucket maintains invariant after many mock Adam steps
- fwd kernel writes only to bucket-assigned channels under arbitrary mapping
Per `feedback_no_partial_refactor`: all 3 indexing bugs + Adam momentum
defense land in one commit.
ml-alpha lib: 33 passed.
GPU oracle tests on RTX 3050 sm_86: 19 passed.
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.