d6eca73e52
Adds intent_dist_q/h/f alongside eval_dist_q/h/f to separate policy-
learning quality from Kelly-enforcement reality. Per memory pearl
project_magnitude_eval_collapse_kelly_capped.md: EVAL_DIST Quarter
dominance is a downstream artefact of Kelly cap × warmup_floor ×
safety_multiplier math, NOT a policy-learning failure. The intent
metric exposes the policy's pre-Kelly-cap chosen mag bucket so
operators can distinguish "policy isn't learning Full" from "Kelly
cap is suppressing Full" — the latter being an operationally-correct
state during cold-start positions.
Pure observability: no Kelly-math tweaks, no new tuned constants, no
reward-bias mechanisms (all explicitly forbidden by the memory pearl).
The intent_mag bucket comes straight from the factored action's
mag_idx (0/1/2) which already maps 1:1 to Quarter/Half/Full buckets.
The pre-cap mag_idx is already captured by the action-select kernel
into intent_mag_buf and exposed via the trainer's pre-existing
last_eval_intent_magnitude_dist host field — this commit only wires
that signal into HEALTH_DIAG.
Wired through (one coordinated commit per feedback_no_partial_refactor):
- health_diag.rs: HealthDiagSnapshot gains intent_dist_q/h/f fields
adjacent to eval_dist_*; size assertion bumped 147 → 150 fields.
- health_diag_kernel.cu: 3 new WORD_INTENT_DIST_* slots at [77..80);
every downstream WORD_* shifted by +3 in lockstep; WORD_TOTAL +
static_assert bumped 147 → 150. (The slots are reserved identically
to WORD_EVAL_DIST_* — both currently inert; HEALTH_DIAG GPU port
Phases 2/3 will populate them in lockstep with their sibling slots.)
- training_loop.rs: new adjacent tracing::info!() emitting
"intent_dist [iq=... ih=... if=...]" from the existing host-side
last_eval_intent_magnitude_dist field, immediately after the big
HEALTH_DIAG line containing eval_dist [eq=... eh=... ef=...].
- docs/dqn-wire-up-audit.md: new top-of-file entry per Invariant 7.
Behavior: zero change. Only adds observability.
Verification:
- cargo check -p ml --offline: clean.
- cargo test -p ml --lib --offline -- health_diag: 3/3 pass.
- cargo test -p ml --test sp4_producer_unit_tests --release
--ignored: 16/16 GPU tests pass.
Closes #212.
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.