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foxhunt/crates/ml
jgrusewski 91383507fc feat(alpha): wire stacker-threshold controller into smoke rollout-end
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.
2026-05-15 17:35:35 +02:00
..

ml

10-model ML ensemble for the Foxhunt HFT system, built on Candle v0.9.1.

Models

  • DQN (Rainbow) — deep Q-network with prioritized replay, dueling heads, noisy nets
  • PPO — proximal policy optimization with GAE, LSTM policies, clip-higher
  • TFT — temporal fusion transformer for multi-horizon forecasting
  • Mamba2 — state space model for sequence prediction
  • Liquid Networks — biologically inspired networks for non-stationary data
  • TLOB — transformer-based limit order book analysis
  • KAN — Kolmogorov-Arnold networks
  • xLSTM — extended LSTM architecture
  • TGGN — temporal graph neural network
  • Diffusion — diffusion-based generative model

Key Modules

  • ensemble — model ensemble coordination and confidence aggregation
  • hyperopt — PSO-based hyperparameter optimization with per-model adapters
  • trainers — unified training loops (DQN, PPO, supervised)
  • inferenceInferenceAdapter trait for prediction
  • checkpoint — model checkpointing and restoration
  • evaluation — walk-forward evaluation pipeline

Usage

use ml::dqn::DQN;
use ml::ppo::PpoTrainer;