39efacf77d
Each batch element is an independent backtest session with its own
$35k starting capital. The CMDP kernel had been summing per-batch
rewards into a single ISV slot — at b=1024 the accumulator grew
b_size× faster than the -$3500 single-account DD limit, tripping
session_dd_triggered globally in ~250 steps (cluster alpha-rl-2j9k9
step 320: session_pnl_usd=-$5775, all 1024 sessions locked out).
Same flaw on consec_loss_count: any 10 losses across the batch in a
single step triggered cooldown for the entire fleet.
Refactor — every Layer-1 state shard is per-batch:
session_pnl_per_batch_d [b_size] f32
session_dd_triggered_per_batch_d [b_size] f32
consec_loss_per_batch_d [b_size] f32
cooldown_remaining_per_batch_d [b_size] f32
`actions_to_market_targets` now reads per-batch DD-triggered + cooldown,
so one account hitting its limit does not lock out the other 1023.
Summary ISV slots (RL_SESSION_PNL_USD, RL_CONSEC_LOSS_COUNT,
RL_SESSION_DD_TRIGGERED, RL_COOLDOWN_REMAINING_STEPS) are now
kernel-OUT only — they expose worst-account / any-triggered / max
aggregates for diag + the single IQN-τ consumer ("be pessimistic
when ANY session is in trouble").
3 raw `actions_to_market_targets_fn.cu_function()` launch sites in
integrated.rs were updated atomically per `feedback_no_partial_refactor`
— public wrapper + 2 internal step_with_lobsim sites. The first
attempt hit CUDA_ERROR_INVALID_VALUE in integrated_trainer_smoke
because only the public wrapper had been updated.
Validation:
- 12/12 risk_stack_invariants pass on per-batch semantics (G1-G4
rewritten to seed per-batch buffers via write_slice_f32_d_pub).
- 20/20 trade_management_kernels still pass (actions gating intact).
- integrated_trainer_smoke passes end-to-end.
- Local b=128 1k smoke: 1000/1000 steps clean, no NaN. CMDP behavior
matches design: worst-of-128 hit DD (-$34k > $3500 limit), other 127
unaffected. IQN τ floored at 0.1 (worst-account drawdown >>10%),
Kelly = 0.0 (observed edge negative: wr=0.21, R=1.57).
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.