657972a4b5
Per Class A audit P0-A downstream batch — both constants were tuned for fixed REWARD_POS_CAP=5.0f. P0-A made POS_CAP adaptive via isv[452]; this commit propagates the ratio to keep the Kahneman 2:1 asymmetry coherent across the reward shaping chain. Items: 1. DD penalty -5.0f → -1.0f * isv[REWARD_POS_CAP_ADAPTIVE_INDEX] (helper signature change in trade_physics.cuh::compute_drawdown_penalty adds dd_penalty_scale parameter; sole call site in experience_kernels.cu:3775 resolves the ISV value with the same defensive guard as sp15_apply_sp12_cap) 2. MIN_HOLD_PENALTY_MAX 3.0f → 0.6f * isv[REWARD_POS_CAP_ADAPTIVE_INDEX] (existing 60% ratio from state_layout.cuh comment line 252-253 preserved; resolved at the call site mirroring the effective_min_hold_target precedent for slot 451) Cold-start fallbacks preserved: - DD penalty: REWARD_POS_CAP=5.0f when ISV at sentinel/out-of-range - MIN_HOLD_PENALTY_MAX: kernel-passed 3.0f from gpu_experience_collector.rs:399 (bit-identical pre-P0-A behavior) Defensive guard at both consumer sites: ISV must be in [REWARD_POS_CAP_MIN_BOUND=1.0, REWARD_POS_CAP_MAX_BOUND=50.0] AND not within 1e-6f of SENTINEL_REWARD_POS_CAP=5.0f. Mirrors the existing sp15_apply_sp12_cap and segment-complete cap fallback patterns. Note: the SP15 quadratic DD penalty path (compute_sp15_final_reward_ kernel.cu::sp15_dd_penalty) is already fully ISV-driven via slots 420 (λ_dd) and 421 (dd_threshold) — only the legacy compute_drawdown_ penalty (linear ramp, slot-free) had the hardcoded -5.0f. The audit recommendation suggested ratio = 1.0 for MIN_HOLD_PENALTY_MAX assuming the value was 5.0f; actual is 3.0f and the existing tuning comment locks the ratio at 60% — pure wiring uses 0.6. Cumulative WR-plateau fix series: - Class C bug 1 + P0-B (8f218cab2) - P0-C (316db416b) - P0-A (394de7d43) — adaptive POS_CAP/NEG_CAP producer - P1 wiring (c4b6d6ef2) — var_floor only - P0-A-downstream (this commit) Per feedback_isv_for_adaptive_bounds + feedback_no_partial_refactor. 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.