7e9a8f6ef1
Per Class A audit-fix Batch 4-A (deferred from P1-wiring/P1-producer due to
audit-doc errors). Fixes 2 of 4 deferred items; Batch 4-B handles plan_threshold
floor + MIN_HOLD_TEMPERATURE in a separate commit.
Item 1: DD saturation floor (the upper end of the DD ramp at trade_physics.cuh:154
in apply_margin_cap, NOT line 548 as the audit doc claimed — that line is a
magnitude action constant; the actual saturation floor lives in apply_margin_cap)
- NEW slot DD_SATURATION_FLOOR_ADAPTIVE_INDEX=458
- Producer dd_saturation_floor_update_kernel.cu — p75(per-env DD_MAX) × 1.5
via Welford `mean + Z_75 × sigma` estimator with `max(p75, mean)` robustness
guard, mirrors P0-A REWARD_POS_CAP producer pattern (Pearl-A bootstrap +
Welford α=0.01)
- Cold-start fallback: 0.25f (DD_SATURATION_FLOOR_DEFAULT in state_layout.cuh)
- Bounds: [0.10, 0.50] (Category-1 dimensional safety)
- Distinct from SP15_DD_THRESHOLD_INDEX=421 (the SP15 quadratic DD-penalty
*trigger* threshold, a *lower* bound; this slot is the *upper* end of the
linear position-size scaling ramp dd_scale = max(0.05, 1.0 − dd_frac/floor))
- Threaded `isv_signals_ptr` into `apply_margin_cap` with NULL-tolerant
cold-start fallback to DD_SATURATION_FLOOR_DEFAULT
- 4 oracle tests (Pearl-A bootstrap, no-DD guard, bounds clamp, Welford EMA)
Item 2: Legacy compute_drawdown_penalty path → Case A (DELETED)
- Decision rationale: SP15's quadratic asymmetric DD penalty
(compute_sp15_final_reward_kernel.cu:154 via sp15_dd_penalty helper) runs
unconditionally as a post-modifier on the SP11-composed reward with
ISV-driven λ_dd (slot 420) and DD threshold (slot 421). Layering the legacy
linear-ramp penalty inside the SP11 composer on top of the SP15 quadratic
creates double-counting of DD shaping — exactly the code-smell the Class A
audit was designed to eliminate. Per `feedback_no_legacy_aliases.md` and
`feedback_no_partial_refactor.md`.
- Atomic deletion across:
- `compute_drawdown_penalty` device function (trade_physics.cuh)
- Single call site at experience_kernels.cu:3822
- `dd_threshold` and `w_dd` kernel arguments
- `w_dd` Rust config field (gpu_experience_collector.rs +
trainers/dqn/config.rs DQNHyperparameters)
- `w_dd` profile section + dispatch (training_profile.rs RewardSection,
OptimizableParameterRanges, FixedRewardParameters, ParamLookup
dispatch, profile→hyperparam mapping, test assertion)
- `w_dd *= rki` risk-intensity multiplier (config.rs)
- `w_dd` TOML keys (dqn-hyperopt.toml × 2, dqn-localdev.toml,
dqn-production.toml, dqn-smoketest.toml)
- Stale doc comments on hyperopt/adapters/dqn.rs + config.rs
risk_intensity field
- `config.dd_threshold` SURVIVES (still consumed by `launch_sp15_dd_state`
as the dd_budget for DD_PCT scaling). Documented in field comment.
ISV_TOTAL_DIM: 458 → 459 (Item 1 adds 1 slot; Item 2 is pure deletion)
Cumulative WR-plateau fix series (this is commit 7):
- Class C bug 1 + P0-B (8f218cab2)
- P0-C (316db416b)
- P0-A (394de7d43)
- P1 wiring (c4b6d6ef2) — 1 of 4 wireable
- P0-A downstream (657972a4b)
- P1 producer (87d597d5d)
- audit-fix 4-A (this commit)
Verification: 16 sp14_oracle_tests pass (incl. 4 new), 36 sp15_phase1_oracle_tests
pass, 12 sp14_isv_slots layout tests pass, 4 state_reset_registry tests pass
(every-FoldReset-arm-has-dispatch contract holds), workspace cargo check clean.
Per feedback_isv_for_adaptive_bounds + feedback_no_partial_refactor +
feedback_no_legacy_aliases.
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.