10f4bcc15b
Two complementary additions to validate the minute-horizon alpha hypothesis at IBKR-realistic costs: 1. `alpha_baseline --decision-stride N`: emits a new action every N steps; between decisions force action=0 (wait) so an open position is held rather than re-decided per bar. Cuts per-bar trade counts ~stride× and removes the coin-flip overtrading. Local 2Q sweep showed stride=200 + scaled training (8K episodes × 25 envs × H=1200) flipped Sharpe at ¼-tick from -4.29 (per-bar, 3-fold mean) to +1.78, with std collapsing from ±8.8 to ±1.15. Break-even cost moved from <¼-tick to ~1-tick — for the first time positive at IBKR-realistic passive-execution frictions. 2. `alpha_train_stacker --max-rows N`: optional cap on bars consumed from the fxcache. Used during local 2Q smoke (--max-rows 4M against the 17.8M-row 9Q fxcache) to fit Mamba2 training on a 4 GB consumer GPU; on the cluster (--no-cap) it sees all 9Q. 3. New Argo workflow `alpha-cv`: standalone template that compiles alpha_train_stacker + alpha_baseline + alpha_fill_coeffs.json, trains the stacker on the 9Q fxcache, then runs 9 sequential walk-forward folds of alpha_baseline on disjoint 1.9M-bar windows (one per quarter). Launcher script `scripts/argo-alpha-cv.sh` mirrors argo-train.sh conventions. The local 2Q test that motivated this commit is summarised inline in the alpha-cv template comments; the verdict was "framing was the bug — once decision cadence matches the multi-minute alpha horizon, the strategy is positive at IBKR commission". Co-Authored-By: Claude Opus 4.7 <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;