034ba16801
Per spec §3.5.3 amended at 7ddaf9c51 on main: experience_env_step
reward composition decomposed from 8+ inline accumulation sites into
explicit per-component locals (r_popart, r_cf, r_trail, r_micro,
r_opp_cost, r_bonus), then composed as Σ w_i × r_i with controller
weights from ISV[340..346).
Trail reward extraction (§3.5.4): trail-fire P&L now flows through
r_trail (forced-exit signal) instead of r_popart (voluntary-exit
signal). REWARD_TRAIL_WEIGHT_INDEX has real signal — controller can
weight forced-exit vs voluntary-exit P&L differently. rc[2] (the
prior structural-placeholder slot) now carries trail magnitude.
Universal post-composition modifiers (§3.4.4): drawdown / capital-
floor / inventory / churn / conviction-scale / cf-flip apply AFTER
the weighted Σ, unweighted. They are risk constraints and structural
operators, NOT learning components — agent cannot weigh them away.
Mean=1 normalization (B0, §3.4.3): weights normalize to mean=1 so per-
bar `w_active × r_active` ≈ pre-SP11 absolute scale on average.
Sentinel-defense: experience_env_step runs at start of epoch, SP11
controller runs at end (training_loop.rs ~3475). At fold 0 epoch 0
step 0 the controller has not yet emitted, so ISV[340..346) hold
sentinel 0. Defense: fmaxf(w_raw, 0.01) — same Invariant-1 hard floor
the controller enforces post-renorm. Cold-start scale = 1% of
pre-SP11; Pearl A bootstrap on first emit replaces sentinel.
cf_reward path: out_rewards[cf_off] now writes controller-weighted
cf reward (w_cf × r_cf with sentinel-defense). Loss-kernel
cf_weight=0.3f at mse:318/c51:789 (structural Q-blend, NOT reward
weight) UNTOUCHED per §3.5 amendment.
Mutual exclusivity preserved (popart / trail / micro / opp_cost):
exactly one path fires per bar; others stay 0. The cascade scalar
`reward` mirrors per-component locals so C.4/D.4b bonus blocks that
read in-progress trade reward (Q-cap pattern from
pearl_one_unbounded_signal_per_reward) keep bit-identical compounding.
After cascade, `reward` is overwritten with r_weighted; post-
composition modifiers operate on r_weighted as before.
This is the production-flip commit. Trainer is now on the SP11
controller end-to-end (modulo replay-time curiosity which lands in
B1c after Layer C audit per §3.5.5).
Verification:
- cargo check + build clean (1m32s release).
- 6/6 SP11 GPU oracle tests pass (none exercise env_step directly).
- 14/14 contract tests pass (sp5_isv_slots=10, state_reset_registry=4).
- Local smoke (RTX 3050 Ti, 20-epoch magnitude_distribution) verifies
HEALTH_DIAG sp11_reward weights drift epoch-over-epoch:
epoch 0: w_pop=1.000 w_cf=1.000 w_tr=1.000 ... (uniform sentinel-defense floor → mean=1)
epoch 3: w_pop=1.991 w_cf=2.036 w_tr=0.493 ... (controller redistributes)
epoch 9: w_pop=1.823 w_cf=1.887 w_tr=0.572 ... (mean ≈ 1.0 preserved, Σ ≈ 6)
EVAL_DIST bit-identical to B1a baseline (eq=0.803 eh=0.197 ef=0.000)
— pre-existing magnitude eval-collapse pathology
(project_magnitude_eval_collapse_kelly_capped) unchanged by B1b.
Audit doc updated (Invariant 7): docs/isv-slots.md SP11 section now
reflects Layer B status with B0/B1a/B1b/B1c rollout timeline.
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.