Knowledge library

This notebook examines whether financial features help explain a 10-session delta-hedged options return label. It compares Ridge models using one implied-volatility feature, groups of implied-volatility-dependent and independent features, and the full…

This implementation describes how to construct batched sequence windows for panel data used in deep-learning studies. Windows follow the panel’s expected calendar rather than compressing each asset’s observations into consecutive rows. Missing sessions…

This notebook presents practical cleaning methods for market and firm data, including duplicate handling, gap flags, domain filters, reversal-based spike detection, outlier clipping, categorical encoding, and alignment across datasets with different…

This notebook explains how to assess whether strategies built on a US equities panel can withstand trading friction. It fixes one configuration per label using validation Sharpe, then reprices that same strategy across declared cost levels. This prevents the…

This notebook examines whether SHAP attributions remain stable when predictions are similar or when models are refitted with different random seeds and architectures. Using ETF forward returns, it searches for sample pairs whose predictions are close but…

This notebook compares predictive, structural, and causal model evidence for a cross-sectional S&P 500 study combining equity features with options-derived measures such as implied volatility, skew, and term structure. It evaluates weekly forward-return…

This notebook shows how to translate gross returns into net performance under explicit trading, financing, and fund-expense assumptions. Its cost stack includes spread and market impact, commissions and exchange fees, margin interest, stock borrow,…

This notebook demonstrates a trade-level diagnostic workflow that links realized failures to a model’s SHAP explanations. It trains a LightGBM model on lagged price and volatility features plus macro inputs to forecast next-session SPY returns. A fixed…

This notebook uses double machine learning to test whether the association between the implied-minus-realized volatility spread and future equity returns persists after adjustment for observed confounders. It compares the adjusted estimate with naive…

This utility describes a workflow for collecting public SEC 10-Q, 10-K, and 8-K filings and converting them into a common tabular dataset for downstream research. It applies form-specific text extraction: quarterly filings yield the management discussion…

This experiment examines how much a selected monthly US equity long-short signal depends on small-cap stocks. It applies a lagged market-equity filter that removes the smallest quartile from validation predictions, then reruns the same portfolio construction…

This notebook explains how PatchTST uses fixed-length patches of sequential market features to forecast NASDAQ-100 labels. Attention relates patches across a trailing hour while reducing the sequence length compared with attention over every minute. The…

The document presents NLinear, a minimal sequence model for forecasting NASDAQ-100 stock returns from intraday features. It uses a fixed trailing window, subtracts the latest observation so the model works with changes rather than levels, and maps the…

This material explains how to evaluate a strategy chosen on validation data using predictions from a separate 2021 holdout. It keeps the selected allocation, concentration, risk controls, trading schedule, and costs fixed, changing only the predictions…

This notebook examines retained results from a real-strategy audit comparing the LEAN engine with matching ML4T Backtest profiles. It identifies the asset-class workloads supported by the frozen inputs and reports parity evidence across fills, valuations,…

This notebook describes producing predictions for a US equities case study’s holdout window after selecting a configuration on validation folds. It resolves the selected configuration, preserves its checkpoint choice, and constructs a new training…

The notebook assembles end-to-end pipelines for daily equities and hourly crypto perpetual futures, covering acquisition, validation, source labeling, and storage. Its equity example joins historical WikiPrices data with a more recent Yahoo feed. Since the…

This notebook presents a framework for checking whether a momentum signal’s apparent quality survives reasonable research choices. Using real ETF data before the designated holdout period, it varies one lookback parameter at a time and measures…

The notebook contrasts two ways to divide a large equity order into child orders over a trading session. TWAP sends roughly equal quantities at each interval and needs no market forecast. VWAP allocates shares according to an estimated intraday volume…

This notebook builds uncertainty intervals for ETF forward returns using Ridge regression and three conformal methods: split conformal prediction, conformalized quantile regression, and adaptive conformal inference. It evaluates them with walk-forward folds…

This notebook explains a conditional autoencoder that models stock returns as characteristic-dependent factor loadings multiplied by latent factors. It first estimates characteristic-managed portfolio returns with a joint cross-sectional least-squares fit,…

This notebook studies how allocation rules affect ETF strategies after a model has already ranked assets. It holds the prediction sets and rebalance calendar fixed, selects the same top-k basket for each comparison, and varies the weights assigned within…

This notebook tests whether portfolio construction can rescue a cost-defeated, every-bar NASDAQ-100 strategy. It selects leading baseline predictions using the declared selection universe, then runs the allocation comparisons on the full universe. The sweep…

This notebook takes leading validation prediction configurations from an equal-weight signal-stage baseline and tests alternative allocation methods on the same predictions. It compares score-based and conformal weighting with inverse volatility, risk…