316db416bb
Per Class A audit: MIN_HOLD_TARGET=30.0f hardcoded was creating a
deterministic gradient pushing trades toward 30-bar holds regardless of
edge expiry. User's trading frequency is between HFT-MFT and varies by
regime; a 30-bar fixed target kills MFT-frequency alpha when the
optimal hold for current data is shorter (or longer).
The producer slot ISV[AVG_WIN_HOLD_TIME_BARS_INDEX=451] already exists
from SP14 Layer C Phase C.4b (commit 3b71d2183) — Pearl-A-bootstrapped
Welford EMA of observed winning trade hold times. Wiring fix only.
Cold-start fallback: when slot still at sentinel (no winning trades
observed yet), use MIN_HOLD_TARGET=30.0f as safety floor. Once a
winning trade closes and the EMA bootstraps, the adaptive value
takes over.
Validity window: isv_hold_target > 0.0f && < 240.0f; outside window
falls back to min_hold_target param (= MIN_HOLD_TARGET=30).
Added #define AVG_WIN_HOLD_TIME_BARS_INDEX 451 to state_layout.cuh
(C-side mirror of sp14_isv_slots.rs:97).
Per feedback_isv_for_adaptive_bounds: every adaptive bound in ISV.
Fixes the third Class A P0 hardcoded constant.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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 aggregationhyperopt— PSO-based hyperparameter optimization with per-model adapterstrainers— unified training loops (DQN, PPO, supervised)inference—InferenceAdaptertrait for predictioncheckpoint— model checkpointing and restorationevaluation— walk-forward evaluation pipeline
Usage
use ml::dqn::DQN;
use ml::ppo::PpoTrainer;