285d42aa7b
Layered risk stack per spec docs/superpowers/specs/2026-05-30-adaptive-risk-management-design.md:
Layer 1 (CMDP) hard session-DD, cooldown, max-open, inventory limits
Layer 2 (IQN τ) risk-averse action selection adapts to session drawdown
Layer 3 (Inventory) Avellaneda-Stoikov penalty β scales with reward magnitude
and inventory variance
Layer 4 (Kelly) half-Kelly fraction sizing from observed win-rate +
R-multiple, warmup-gated until cumulative_dones >= 1000
Layer D (Trail) wire dead a7/a8 (TrailTighten/Loosen) via independent
ISV factors, replacing the symmetric reciprocal
Architecture: every threshold ISV-driven (22 new slots, RL_SLOTS_END 662→684).
Every adaptive bound follows the canonical Wiener-α blend with floor 0.4,
sentinel-zero bootstrap, and asymmetric Schulman where applicable.
Kernels:
rl_cmdp_constraints_check session pnl + cooldown + consec-loss tracking
rl_iqn_action_tau_controller τ = clamp(0.5 - 5·dd_frac, τ_min, 1.0)
rl_inventory_beta_controller β_target = 0.01·E|reward| / (2·σ_inventory)
rl_kelly_fraction_controller f = clamp(safety · (p·b - q)/b, 0, 1)
rl_win_rate_ema_update closed-trade win-rate EMA from rewards + dones
rl_avg_win_loss_ema_update separate avg-win and avg-loss EMAs
rl_inventory_variance_update Welford variance of net-position-per-batch
Integration:
- All 7 new cubins loaded in IntegratedTrainer + launched per step in spec order
(CMDP after reward pipeline, before actions_to_market_targets reads override
flags; Layer 2/3/4 controllers in rl_fused_controllers.cu).
- actions_to_market_targets.cu: Layer 1 hard overrides (DD-triggered → 0 lots;
cooldown → 0 lots; max-open → block opening actions; inventory cap → block
one-sided expansion) and Layer 4 Kelly fraction scaling on target lots.
- rl_fused_reward_pipeline.cu: Layer 3 inventory penalty term in reward shaping.
- rl_trail_mutate.cu: a7 multiplies trail by RL_TRAIL_TIGHTEN_FACTOR_INDEX,
a8 by RL_TRAIL_LOOSEN_FACTOR_INDEX (was symmetric reciprocal — pearls
pearl_dead_trail_stop_actions_a7_a8).
Validation:
- 12 GPU-oracle invariants pass (tests/risk_stack_invariants.rs):
G1-G4 CMDP, G5-G7 IQN τ, G8-G9 inventory β, G10-G12 Kelly.
- 20/20 trade_management_kernels.rs tests pass (a7/a8 migrated).
- 5/5 controller_adaptive_floors.rs tests still pass.
- integrated_trainer_smoke passes (end-to-end pipeline launch).
- Local 1k smoke b=128: completes 1000/1000 steps, no NaN, controllers steady.
- compute-sanitizer memcheck (5 steps b=128): ERROR SUMMARY: 0 errors.
Plan: docs/superpowers/plans/2026-05-30-adaptive-risk-management-plan.md
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.