e96f7fcdd1
Parallel to the 2026-05-08 Class C direction fix but on the **magnitude axis**.
Class C correctly chose REALIZED for direction because env-gated trades (capital
floor / trail / broker cap → Flat) mean no trade happens — recording as Flat is
honest. For magnitude, Kelly cap clamping does NOT gate the trade — the trade
still happens, just at a smaller size. `actual_mag_core` is the bucketed
magnitude derived in trade_physics.cuh:1099-1117 from |position|/max_position;
when the policy intends Full and Kelly clamps to 0.35× max (bucket → Quarter),
recording Quarter loses the policy's intent. Q(s, mag=Full) never receives the
reward gradient from Full-intent trades that actually executed.
One-line swap inside the Class C encoding block at experience_kernels.cu:2689-
2697: `actual_mag_core * b2_size * b3_size` → `mag_idx * b2_size * b3_size`.
`mag_idx` is the local intent magnitude already decoded at line ~2460 from the
original action_idx BEFORE any Kelly/CVaR/Q-gap clamping. Direction axis keeps
Class C semantics (`actual_dir_core` → gated trades record as Flat). Order/
urgency remain intent passthrough.
`actual_mag_core` remains consumed downstream by Task 2.X per-magnitude trade-
lifecycle instrumentation at the seg_mag_bin site below — direction-branch Q
learns from realized; magnitude-branch Q learns from intent. The two axes have
different env-enforcement semantics so they take different actions at the
replay-write site.
Verification: new CPU oracle test crates/ml/tests/sp20_magnitude_intent_record_
test.rs pins the encoding contract with 4 invariants (Full→Quarter scenario,
all-pairs sweep, order/urgency passthrough, direction Class C unchanged). Per
pearl_tests_must_prove_not_lock_observations the test asserts invariants
("recorded mag == intent mag, regardless of realized") not observed values, so
it cannot become a bug-lock if the kernel's compute path changes — only if the
encoding contract itself is broken. All 4 tests pass; cargo check clean.
Audit-doc entry added to docs/dqn-wire-up-audit.md as the SP20 magnitude intent
fix, parallel to the Class C direction fix above with the asymmetry between
the two axes spelled out.
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.