f9192f70a5
Phase 1 post-mortem traced an actual `pearl_first_observation_bootstrap` violation in my own H6 implementation: state slot 121 wrote `aux_softmax[env, 1] = p_up ∈ [0, 1]` with sentinel 0.5, but every OTHER state slot uses 0 as the "no signal" baseline (zero-padding, feature_mask, ofi-missing, mtf-missing). The encoder had to learn TWO things about slot 121 (directional mapping + non-zero bias offset) instead of one. Phase 2 fixes the encoding to match the project convention BEFORE declaring H6 fully falsified. Mechanism change ──────────────── - `aux_softmax_to_per_env_kernel.cu` writes `2*p_up - 1 ∈ [-1, +1]` instead of `p_up`. Still structurally bounded (softmax components in [0, 1] sum to 1). - Cold-start + FoldReset sentinel: 0.5 → 0.0 via the same pure-GPU `fill_f32` path. No HtoD per `feedback_no_htod_htoh_only_mapped_pinned`. - NULL-fallback in 3 state-gather kernels (training + backtest-per-step + backtest-chunk): 0.5f → 0.0f. - Constant + device-function comment updates to document the recentered encoding. Atomic per `feedback_no_partial_refactor`: the encoding contract spans 5 source files; partial migration produces inconsistent slot semantics between training and eval. Verification gates (all clean) ────────────────────────────── - cargo check -p ml --features cuda: 0 errors, 21 pre-existing warnings (parity with Phase 1 baseline) - gpu_backtest_validation: 4/4 expected-passing tests still pass; 2 pre-existing PnL-assertion failures bit-identical to Phase 1 (confirms recentering does not perturb scripted-policy paths) - compute-sanitizer --tool=memcheck: ERROR SUMMARY: 0 errors Smoke dispatch deferred pending an orthogonal investigation into the 2 pre-existing gpu_backtest_validation failures (stale action constants in the tests; addressed in a follow-up commit, NOT a Phase 2 regression). Verdict criteria (per spec, evaluated after smoke) ────────────────────────────────────────────────── - WR > 50.5% within 3 epochs → recentering binding, H6 + Phase 2 sufficient → justify A2. - a_var for mag/ord/urg > 1e-3 → sub-branches gradient-coupled under recentered signal. - WR pinned at 50.1–50.2% → Phase 2 falsified, pivot to amplitude scaling or deeper hypothesis. Refs ──── - docs/plans/2026-05-12-sp22-h6-phase2-recenter.md (spec) - docs/plans/2026-05-12-sp22-h6-phase2-recenter-runbook.md (this plan) - pearl_first_observation_bootstrap (sentinel = 0) - feedback_no_partial_refactor (5-file atomic) - feedback_no_htod_htoh_only_mapped_pinned (fill_f32, not HtoD) 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.