Knowledge library

This notebook compares equal-weight, positive-score-weighted, and conformal-width-weighted allocations for top-ranked ETFs and CME futures. It estimates entity-specific split-conformal interval widths from residuals in earlier validation folds, excluding…

This document presents a supervised autoencoder as a control for conditional latent-factor models. It maps firm characteristics directly to forward returns while compressing 57 inputs through a narrow bottleneck. Comparing it with a conditional autoencoder…

This notebook explains how stock splits and cash dividends disrupt raw historical price series and distort calculated returns. It demonstrates backward adjustment, in which historical prices are rescaled for subsequent corporate actions, and applies the…

This NASDAQ-100 study examines how transaction costs affect a frequently rebalanced equity strategy. It separates two possible responses to costs: restricting the traded universe to more liquid, lower-cost names, and reducing rebalance frequency. Keeping…

This experiment combines validation predictions from a gradient-boosted tree model, a tabular neural network, and a latent-factor model for monthly ETF ranking. It first standardizes predictions cross-sectionally with z-scores, then averages them. The…

This document presents shared methods for generating fitted-model features without using future observations. For hidden Markov models, it distinguishes filtered state probabilities, based on observations up to the current time, from smoothed probabilities…

This document describes a walk-forward method for turning prediction residuals into per-entity uncertainty widths used to size positions. At each decision time, calibration uses residuals that have had time to resolve, with a lag based on the prediction…

This case study selects one configuration from a frozen pool of crypto perpetual futures strategies spanning multiple return and direction labels, portfolio sizing methods, and risk overlays. It chooses the highest validation Sharpe, then examines…

This notebook explains how to evaluate probability forecasts and govern a forecasting pipeline. It compares Brier and log scores, expected calibration error, and sharpness; shows how reliability diagrams reveal where forecasts are over- or under-confident;…

This tutorial turns ETF cross-sectional signals into long-only, equal-weight top-ten portfolios and compares Ridge regression and logistic classification with momentum and equal-weight baselines. Models are fitted on walk-forward training windows, with a…

This study tests whether the poor fit of a linear model to monthly US stock returns comes from its squared-error objective. It holds the unwinsorized forward-return target, features, and walk-forward folds fixed while comparing boosted tree models trained…

This notebook trains a Proximal Policy Optimization agent to liquidate a fixed order over a set horizon. It compares the learned pacing policy with TWAP and an Almgren–Chriss schedule, measuring implementation shortfall and examining when each strategy…

This notebook presents the Rademacher Anti-Serum (RAS) protocol as a way to adjust performance estimates for selecting from a finite class of strategies or signals. Empirical Rademacher complexity estimates how well the candidate performance matrix can align…

This notebook introduces NLinear as a baseline for predicting stock returns from ordered feature windows. Each example contains 60 consecutive sessions for one stock. For every feature, the model subtracts its latest value, maps the resulting history to a…

This notebook fits TabM neural models to an S&P 500 options short-straddle return target and compares three model capacities on the same feature panel used by linear and boosting approaches. TabM shares a neural backbone across ensemble members, while…

This notebook evaluates position-level controls for an S&P 500 equity-and-options allocation, comparing fixed stop losses, trailing stops, and time exits with each strategy's own no-overlay baseline. It selects an eligible strategy lineage using validation…

This document explains how TSMixer processes a stock’s recent feature history as a sessions-by-features matrix. Its blocks mix information down feature columns across time, then across features within each session. Stacking blocks lets those operations…

This notebook uses registered walk-forward GBM predictions for ETFs and CME futures to compare equal weighting, inverse conformal-width weighting, and weighting proportional to positive prediction scores. It calibrates entity-specific Mondrian…

This notebook compares zero-shot Chronos and TinyTimeMixer forecasts with an LSTM and a penalized linear model on an ETF panel. Every model receives the same univariate historical context, but the pretrained models forecast that context series rather than…

This notebook evaluates whether option-market measures can rank future returns across stocks. It fits declared linear models on option-derived features, using walk-forward validation, and compares penalty strengths for ridge, lasso, and elastic net. The…

This study asks whether option-market quantities can rank future stock returns. Its features include implied volatility across maturities, put-call skew, term-structure slope, and the variance risk premium. Because many measures are represented in several…

This document develops a daily feature panel for ranking twenty currency pairs at the New York 5 PM close. It distinguishes rankable signals, such as standardized multi-horizon returns and channel position, from market-state measures such as volatility,…

This tutorial constructs a continuous futures series from individual ES contracts. It identifies the front month using trading volume, with a monotonic roll constraint, and compares volume-based switching with calendar-based timing. It explains why outright…

This tutorial builds a monthly ETF rotation simulator using a trailing risk-adjusted momentum ranking and a Treasury yield-curve regime filter. At each month-end, the rule ranks ten funds using their recent price history; risk-on months hold the top three…