Knowledge library

This overview organizes a set of trading case studies by asset class, universe size, data frequency, decision cadence, cost regime, and evaluation protocol. It reads each study’s declared setup and presents comparative tables, including training and…

This document defines two diagnostics for labels whose forward return windows overlap: pooled panel autocorrelation and effective sample size based on label uniqueness. Autocorrelation pairs observations only within the same entity and at the requested…

This notebook shows how to use an estimation-window correlation network to describe an equity universe and build a network-diversified portfolio. It selects liquid symbols using only data available before evaluation, converts return correlations to…

This notebook builds an LSTM allocator that maps sequences of ETF returns and rolling volatility features directly to long-only portfolio weights. A softmax output keeps weights nonnegative and fully invested. Instead of forecasting returns and feeding those…

This exploratory analysis examines the legacy Quandl WIKI US equities panel, focusing on price conventions, data coverage, and what its history can support in a backtest. It distinguishes raw prices, which reflect traded price levels, from adjusted prices…

This notebook assesses how transaction costs affect a fixed CME futures configuration selected elsewhere in the research process. It applies an all-in cost grid, splitting each level evenly between commission and slippage, and carries the selected risk…

This notebook treats a Sharpe ratio as an estimate with sampling uncertainty. It develops confidence intervals and probabilistic Sharpe calculations that account for sample length, skewness, and kurtosis, then uses minimum track record and power planning to…

This notebook compares PyPortfolioOpt, Riskfolio-Lib, and skfolio by fitting portfolio allocators on a shared ETF return panel and evaluating their frozen weights on later observations. It covers mean-risk, tail-risk, hierarchical risk parity, and penalized…

This notebook constructs features from minute-level NASDAQ-100 data using three procedures: a rolling heterogeneous autoregressive regression for near-term variance forecasts, a Fourier transform to describe periodic structure in recent activity, and…

This notebook assesses whether a monthly long–short strategy based on firm characteristics can be studied with a released US firm panel. It checks the data encoding, how the available universe changes over time, whether each rebalance has enough firms to…

This notebook develops the Almgren–Chriss solution for liquidating a position while balancing market impact against the risk of remaining exposed to price moves. It uses implementation shortfall relative to the arrival price, models permanent and temporary…

This notebook shows how to analyze a financial event graph without confusing when an event happened, when it became public, and when the graph pipeline extracted it. It loads timestamped 8-K relationships, measures disclosure and extraction delays, and…

The document introduces latent factors as shared return drivers inferred from co-movement across futures rather than supplied as named predictors. It compares two approaches fitted within each training fold. Principal component analysis (PCA) finds linear…

The document describes how to compare validation predictions from linear, gradient-boosting, tabular deep-learning, and sequence models for S&P 500 options. Before reporting diagnostics, it checks that each registered prediction population is complete…

This notebook turns registered NASDAQ-100 microstructure backtests into a strategy assessment without training models or rerunning backtests. It reads metrics and lineage from the registry, builds derived cohort and paired-comparison tables when needed, and…

This notebook applies a previously selected CME futures configuration to holdout predictions, using the established allocator, rebalance schedule, concentration rules, and transaction cost assumptions. Its central methodological point is to freeze those…

This notebook explains how feature stores keep training data and live model inputs aligned. It describes feature views through their entity key, event timestamp, time to live, and feature columns, then contrasts point-in-time joins for historical training…

This notebook measures how a validation-selected FX strategy responds to changes in proportional transaction costs. For each prediction label, it selects a parent strategy from the signal, allocation, and risk-overlay results, then reruns that strategy…

This notebook describes how to select a CME futures case-study configuration from registered validation backtests. It pools candidates across signal, allocation, and risk-management stages, then chooses the configuration with the strongest validation Sharpe.…

This notebook applies one monthly ETF momentum and yield-curve regime strategy in two ways: vectorized return arithmetic and a sequential account simulation. Both use the same target weights, assets, dates, and total trading cost, allowing the comparison to…

This document explains a five-gate process for evaluating nine trading case studies, from signal quality through validation, transaction costs, holdout performance, and evidence quality. The gates are cumulative, so a strategy reaches the final stage only if…

This notebook describes fitting a long short-term memory network to one-minute NASDAQ-100 microstructure features to predict forward returns at several horizons. The model processes a trailing sequence step by step, learning which earlier observations to…

This notebook compares volatility measurements, fits a heterogeneous autoregressive (HAR) model, and estimates roughness using Hurst exponents. It first contrasts intraday realized variance with daily estimators, separating overnight returns from session…

The notebook introduces a conditional autoencoder that extends instrumented principal component analysis by replacing its linear mapping from stock characteristics to factor exposures with a neural network. It retains the exposure-times-factor-return…