Machine learning.

Time-series foundation models meet nearest-neighbor retrieval, causal evaluation, and ablations that expose prediction-to-decision failures.

START HEREThe deepest experimental thread: model adaptation, retrieval, ablations, and useful negative results.

An extensive adaptation of the upstream Momentum Transformer research, including a PyTorch implementation and portfolio/execution reinforcement-learning layers.

A NumPy-only neural-network training library with dynamic computation graphs, reverse-mode differentiation and a small MNIST classifier.

Recurrent PPO, portfolio environments, and related regime and fundamental-overlay studies. Each experiment retains its own evaluation boundary.

Two iterations of a few-shot forecasting reproduction, including a tensor-level correction to what attention values remember.

A sequence of course studies in latent-variable generation, DDPM training and classifier-free guidance, then diffusion-based image deblurring.

Related DoubleAdapt/StockMixer, DeePM, and imitation-learning experiments in adapting models to A-share portfolio decisions.

An industry-aware equity research framework whose concrete implementation centers on ingestion, feature/label contracts, purged cross-validation and supervised model training.

An adapted AlphaPROBE research workspace with local point-in-time feature infrastructure and alpha-allocation additions.

Chart-image CNN/ViT, market-guided attention, and cross-sectional representation studies in one model-research family.

Graph, GNN, and HMM experiments investigate which stocks recover after a market leader’s limit-up streak ends.

A modular reproduction of the PyTorch CIFAR-10 tutorial that turns seed, augmentation, epoch and channel-width changes into selectable experiment configurations.

CAMEF and the Kalshi AI bot: upstream codebases retained as reference foundations, with original authorship clearly distinguished.