2d498bec3a
rdgzl follow-up — chain hypothesis layer 2:
reward clamp lift unlocked V regression + PPO advantage (R/done
-$1.39 → -$0.48), but Q's distributional learning was structurally
capped at hardcoded V_MAX=1.0 in bellman_target_projection.cu —
any Bellman target > 1.0 categorically projected to atom 20 (top)
regardless of clamp. Even with WIN=3.8 clamp, Q never saw a +3.8
reward signal as distinct from a +1.0 reward signal.
This commit makes V_MIN/V_MAX ISV-driven with monotone-grow ratchet
coupled to the reward clamp. The C51 distribution support adapts
WITHOUT destabilising Q's learned values — atom 20 always represents
at least the widest WIN we've ever admitted (only grows, never shrinks).
Implementation:
- 2 new ISV slots (484 V_MAX, 485 V_MIN) with [-1, +1] floors
seeded by rl_isv_write
- rl_reward_clamp_controller.cu also ratchets these slots:
V_MAX_new = max(V_MAX_prev, max(1.0, WIN_clamp))
V_MIN_new = min(V_MIN_prev, min(-1.0, -LOSS_clamp))
- bellman_target_projection.cu reads V_MIN/V_MAX from ISV, derives
DELTA_Z inline (was #define)
- New rl_atom_support_update.cu (21-thread block) refreshes
atom_supports_d = linspace(V_MIN, V_MAX, 21) per step so
downstream C51 kernels (argmax_expected_q, rl_action_kernel,
dqn_distributional_q) see the current span
- Trainer launches atom-support updater after each reward-clamp
controller launch (both helper + step_with_lobsim inline paths)
- Diag exposes c51_v_max + c51_v_min for adaptation visibility
Floors at [-1, +1] preserve original C51 design as hard minimum —
the atom support can only become wider, never narrower than the
baseline.
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.