35a5b4f1dc
ROOT CAUSE 1: Stop-loss 0.3%→1%, take-profit 0.5%→2% (2:1 R:R). Old 0.3% = 8.3 ticks on ES. Normal 1-min noise is 4-6 ticks. 99.88% of trades were stopped out by NOISE, not by bad entries. ROOT CAUSE 2: Dense shaping 0.1x→0.01x. Over 50 bars, old dense signal = 5.0 vs completion ±2.0 — dense dominated. Now dense = 0.5 vs completion ±2.0 — trade completion is the primary signal. ROOT CAUSE 3: Action aliasing in 5-bar hold override. When model chose Flat but was forced to Hold, replay stored (state, Flat, Hold's reward) — corrupting Q-values for Flat. Now overwrites out_actions with the ACTUAL held exposure action. ROOT CAUSE 4: Hyperopt HFT activity weight 25%→5%. Old objective penalized selective trading. MIN_VIABLE_TRADES 100→20. Co-Authored-By: Claude Opus 4.6 (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;