91383507fc
Phase E.2 Task 17. Loads stacker_threshold_controller.cubin at smoke
startup, initialises ISV[544] (TRADE_RATE_TARGET) to 0.08 (CLI flag
--trade-rate-target, never reset), allocates a 3-float Wiener state
buffer for slot 545's Pearl A+D state.
Invokes the controller at every episode end with:
rollout_trade_count = count of non-Wait actions in the episode
rollout_total_decisions = actions_host.len() (= ep_len)
rollout_realized_sharpe = ep_terminal_R / RANDOM_BASELINE_STD
(per-rollout analog of the rvr metric;
lets the Kelly-atten controller respond
to in-policy performance vs the baseline
noise floor)
CLI args added:
--trade-rate-target default 0.08 (8% per-step trade rate target)
--k-threshold default 0.01
--k-atten default 0.005
--target-sharpe default 0.5
--wiener-alpha-floor default 0.4
--ctl-alpha-meta default 0.1
Periodic log line extended:
ep ... | KC q/H/rvr/ΔQ ... | CTL thresh=... obs=... atten=...
Final JSON adds:
final_stacker_threshold
final_trade_rate_observed_ema
final_stacker_kelly_attenuation
trade_rate_target
Smoke run (H=600, 1000 episodes) verifies the controller is alive:
ISV[543] STACKER_THRESHOLD: 0.000 → 0.5000 (saturated at ceiling)
ISV[545] TRADE_RATE_OBSERVED_EMA: 0.000 → 0.712
ISV[546] STACKER_KELLY_ATTENUATION:0.000 → 0.100 (hit floor)
Verdict: PASS — rvr=+1.043σ (unchanged from Task 12b PASS, expected
since smoke doesn't yet CONSUME slots 543/546).
Tuning notes (calibration for production, not bugs):
• Threshold saturating at 0.5 → policy trades ~85% (target 8%, off by
10×). Either re-calibrate target_trade_rate from realistic backtest
behaviour, or raise the clamp ceiling. Current ε-greedy with low
threshold-consumption gate produces high trade rate.
• Kelly atten hit floor (0.1) because rollout_sharpe (~-0.004) is far
below target_sharpe=0.5. The target needs to match the rollout
metric's scale, OR the metric should be time-normalised. The
current ep_terminal_R / baseline_std proxy is meaningful but its
scale doesn't match a typical annualised Sharpe target.
These tuning items don't gate Milestone E.2 — the producer-side
controller is correctly driving the ISV slots; *consuming* those slots
(threshold gate on alpha signal, Kelly-cap multiplier) is Phase E.3
work (alpha + execution composition).
Phase E.2 Tasks 16 + 17 close-out: kernel + launcher + GPU smoke test
+ wired into smoke binary + initialisation + verified end-to-end. Tasks
19-22 (NoisyNet) are gated on Task 12 FAIL, which we passed — skipped.
Task 18 (alpha-trust ablation, ~9-18 hours compute) deferred to a
dedicated session if needed.
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.