705d6c156b
Per `feedback_isv_for_adaptive_bounds` + user "do all except floors
and clamp bounds": 10 more constants moved from kernel-side `#define`s
into ISV slots (78 slots total now).
## Slot additions (468-477)
RL_SCHULMAN_TOLERANCE_INDEX (468, =1.5) — shared by 4 controllers
RL_SCHULMAN_ADJUST_RATE_INDEX (469, =1.5) — shared by 4 controllers
RL_STREAM_ALPHA_INDEX (470, =0.05) — shared by var + kurt streaming
RL_KURT_GAUSSIAN_INDEX (471, =3.0)
RL_KURT_NOISE_FLOOR_INDEX (472, =1.0)
RL_TAU_BOOTSTRAP_INDEX (473, =0.005)
RL_EPS_BOOTSTRAP_INDEX (474, =0.2)
RL_ROLLOUT_BOOTSTRAP_INDEX (475, =2048)
RL_REWARD_SCALE_BOOTSTRAP_INDEX (476, =1.0)
RL_PPO_RATIO_CLAMP_BOOTSTRAP_INDEX (477, =10.0)
## Skipped (per user "do all except floors and clamp bounds")
* `*_MIN`/`*_MAX` clamp bounds (algebraic domain — risk γ=1.5 nonsense)
* Numerical floors: ABS_MEAN_FLOOR=1e-6, M2_SQ_FLOOR=1e-12, EPS_PNL=1e-3
(risk div-by-zero if mis-tuned)
* C51 atom layout (V_MIN/V_MAX) — architecture, not config
## Wiring
* Shared Schulman pattern: 4 controllers (ppo_clip, target_tau,
rollout_steps, plus per_α independent KURT slots) now read TOLERANCE
+ ADJUST_RATE from the same 2 ISV slots. Single source of truth.
* Each controller's bootstrap (1st-emit on sentinel-zero) reads
isv[*_BOOTSTRAP_INDEX] instead of #define value. The `prev ==
BOOTSTRAP` first-observation replace-direct check also reads from
ISV.
* 2 streaming kernels (var + kurt) share RL_STREAM_ALPHA_INDEX.
## Diag bake-in
JSONL `isv_config` block grows by 10 new fields: schulman_tolerance,
schulman_adjust_rate, stream_alpha, kurt_gaussian, kurt_noise_floor,
tau_bootstrap, eps_bootstrap, rollout_bootstrap,
reward_scale_bootstrap, ppo_ratio_clamp_bootstrap. Total isv_config
fields: 26.
Also includes windowed action_entropy fix (was structurally 0 at
b_size=1) — accumulates EMA-smoothed action distribution over
~1k-step window, computes entropy on the windowed dist. Makes the
exploration metric meaningful at b_size=1.
## Slot total
RL_SLOTS_END: 468 → 478. **78 total ISV slots.**
## Verified gates (local sm_86)
G1 isv_bootstrap ✅ (with 10 new assertions)
G3 controllers ✅
G4 target_update ✅
integrated_smoke ✅
Co-Authored-By: Claude Opus 4.7 <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.