39d4577b77
Closes the last hardcoded anchor in compute_agreement_threshold per
pearl_controller_anchors_isv_driven. Smoke-driven motivation: the
9-cycle smoke run of commit 1d2dd38a1 (Phases 1.5..8) produced
monotonic agree_thr loosening from 1.30 → 10.00, hitting the
hardcoded upper clamp on cycle 9.
Bound formula:
scale = (1.0 + val_sharpe_std × 2.0).clamp(1.0, 5.0)
lo = 0.01 / scale
hi = 10.0 × scale
Bound behaviour:
val_sharpe_std=0 (cold) → scale=1.00 → [0.01, 10.0] (= pre-8.1 baseline)
val_sharpe_std=0.05 (mild) → scale=1.10 → [0.009, 11.0]
val_sharpe_std=0.30 (noisy)→ scale=1.60 → [0.006, 16.0]
val_sharpe_std≥2.0 (extreme) → scale=5.00 → [0.002, 50.0] (Invariant 1 ceiling)
The 2.0× multiplier and [1.0, 5.0] scale clamp are themselves
hardcoded but explicitly Invariant 1 carve-outs (numerical-
stability bounds on the bound formula, NOT controller anchors).
The recursion terminates at structural floors/ceilings per
pearl_wiener_alpha_floor_for_nonstationary's canonical pattern —
making meta-meta-meta-bounds signal-driven gains nothing.
Cold-start preservation: prior special-case short-circuit
returned current.clamp(0.01, 10.0). New formula reduces to that
exact behaviour when std=0 (scale=1, lo=0.01, hi=10.0). The
short-circuit is retained for explicit "no update on cold start"
semantics. No behavioural regression at cold-start.
Files changed:
- crates/ml/src/trainers/dqn/trainer/enrichment.rs: compute_agreement_
threshold clamp refactor (single-function change, no ABI churn)
- docs/dqn-wire-up-audit.md: 2026-05-11 audit entry with full
smoke cycle table
Verification (passing):
- cargo check -p ml --tests --features cuda: 0 errors
- cargo test -p ml --lib sp21_isv_slots: 3/3
- sp20_aggregate_inputs_test: 12/12
- sp20_phase1_4_wireup_test: 2/2
- sp20_emas_compute_test: 4/4
- sp20_controllers_compute_test: 7/7
- sp21_per_trade_predicted_q_test: 3/3
Total: 34 tests, 0 failures. Behavioral gate: a repeat smoke
should show agree_thr breaking past 10.0 as val_sharpe_std
drives bounds outward.
SP21 T2.2 cascade — FULLY COMPLETE after this commit. 12 atomic
commits, no hardcoded anchors remaining in enrichment controllers
(only Invariant 1 stability carve-outs on bound-on-bound formulas,
which terminate the recursion).
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.