Knowledge library

This notebook explains PCA as a feature-free latent-factor baseline for forecasting ETF returns. It decomposes each label’s training-window return matrix, using a loading for each fund and estimated factor premia to assemble forecasts. Because the same fund…

This notebook walks through a deployment cycle for a daily FX pairs model using Interactive Brokers as both the live data source and execution venue. It trains a Ridge regression model on a historical panel, computes a compact set of daily return, momentum,…

This notebook presents a neural stochastic discount factor model for a panel of CME futures. The model uses both product returns and observable characteristics, such as carry, momentum, and volatility, to construct latent factors and generate predictions for…

This study re-prices leading ETF allocation and risk-control combinations across two cost grids while holding each registered strategy configuration fixed. One grid uses basis points per trade; the other uses per-share commission and half-spread, the…

This notebook compares six long-only portfolio allocations for a multi-asset ETF universe. It uses Ledoit–Wolf covariance shrinkage to reduce the sampling variation that can destabilize optimization, and compares maximum Sharpe, minimum variance, risk…

This notebook shows how to use PCMCI to search for delayed relationships among ETF and volatility returns without specifying a causal graph in advance. It contrasts panel-based conditional-independence testing with pairwise Granger tests, which account for a…

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…

This document explains a CME futures experiment using TabM, a parameter-efficient neural ensemble, on the same engineered feature rows and walk-forward folds used by linear and gradient-boosting models. TabM shares most weights across ensemble members to…

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 module describes migrating a registry after a library changed the numbering of walk-forward folds while leaving their date windows and fitted values unchanged. Since fold identifiers appear throughout training specifications, prediction keys, input…

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…

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…

The notebook explains how to compare prediction models for S&P 500 options without allowing incomplete or mismatched evaluation samples to distort the results. It identifies each model by family, configuration, and checkpoint, checks that predictions meet…

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 dataset guide describes a collection of continuous CME futures contracts spanning equity indexes, rates, energy, metals, currencies, agriculture, and livestock. It explains the hourly source data and derived daily frequency, multiple contract tenors,…

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 compares linear models on ETF features built from overlapping return windows and related momentum, volatility, and oscillator measures. Such predictors are collinear, making ordinary least squares coefficients unstable. The experiment fits…

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…

This notebook evaluates how registered ETF prediction sets perform when converted into portfolios. It crosses each validation prediction set with declared entry schemes, such as different top-k holdings, while keeping the trading calendar, rebalance cadence,…

This chapter describes a path from research to live execution built around strategy logic that can run in backtest, paper, and live settings. It covers broker and platform integrations, deployment loops, data and feature parity checks, order lifecycle…