c55c24699a
Prerequisite for Plan 4 Task 2c (GRN ADOPT). The current layout_fingerprint_seed() only covers ISV slot names + indices; param-tensor layout shifts (which GRN insertion will cause) pass silently through checkpoint load. Extends the seed string to include all 86 param-tensor positions by canonical name. Any structural reshuffle (insert/delete/rename a tensor) now triggers a different fingerprint and fail-fast at checkpoint load. Pragmatic scope (Option A): tensor names + positions, not sizes. Sizes depend on runtime config (shared_h1, shared_h2, etc.) and can't be embedded in a const fn. Size mismatches between checkpoint and current binary are caught by safetensors deserialization separately. Task 2c's GRN insertion is structural (new tensor names at new positions) — Option A suffices. This commit IS a checkpoint break: every existing checkpoint's fingerprint matches the old seed and fails load. Behavior change zero; cold-start smoke passes at baseline Sharpe range (fold-2 best Sharpe = 96.65). New fingerprint value: 0xa504d3c2f275b8af. Pearl-aligned: complete fingerprint coverage for what it claims to protect. Partial coverage was worse than no coverage because it implied safety where there wasn't. 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;