Knowledge library

This notebook implements a daily educational adaptation of an adversarial stochastic discount factor model. A portfolio-weight network constructs a factor from next-day excess returns using characteristics and market state available at the prior close. An…

This analysis assesses whether selected trading signals appear reliable by comparing information coefficient (IC) evidence with the Sharpe ratios produced after predictions are converted into positions. It aggregates model-family results across asset…

This notebook aggregates previously run walk-forward results to compare LSTM, NLinear, TSMixer, TCN, and PatchTST across case studies, alongside linear, gradient-boosted, and TabM baselines. It reports average daily Spearman information coefficients with HAC…

The notebook previews S&P 100 10-K and 8-K filings intended as inputs to a financial knowledge graph. It describes schema checks for required fields, unique company and accession keys, consistent form labels, year alignment, and text-length metadata. Annual…

This document describes a reader-facing interface for examining backtest results stored in a registry. It supports summaries of run counts, inspection of specifications and individual runs, ranking results, comparing allocation methods, viewing concentration…

This notebook analyzes timestamped financial event edges from a knowledge graph, distinguishing when an event happened, when it became public, and when the extraction pipeline created the edge. It calculates disclosure and extraction lags, builds snapshots…

This chapter review describes a hypothesis-driven process for defining trading strategies before backtesting. It connects documented data assumptions and immutable configuration to exploratory analysis, event studies, and a structured strategy term sheet.…

This notebook trains a vanilla autoencoder on hourly returns for a basket of crypto perpetual markets. Returns are standardized using the training period, and a neural network compresses the multi-asset input into a two-dimensional latent representation…

The document describes a shared analysis workflow for comparing model configurations across case studies. It first selects one configuration per case study, then derives fold-level and cross-study summaries from that selection so later analyses use a…

This notebook turns stock-level predictions into validation portfolios. On each rebalance date, it sorts stocks by predicted return, buys the top group and shorts the bottom group, and assigns equal capital to every position. It applies this baseline across…

This capstone explains how to evaluate probability forecasts with Brier score, log score, expected calibration error, and sharpness, and how reliability diagrams reveal where forecasts are overconfident or underconfident. It describes fitting calibration…

This notebook evaluates LSTM and NLinear sequence configurations on CME futures products. Sequence models consume ordered windows of observations, testing whether the recent path contains information beyond engineered summary features. The LSTM learns a…

This notebook describes a shared workflow for fitting temporal convolutional networks to daily FX pair data. Instead of treating each date as an isolated feature row, the model uses a fixed lookback of consecutive observations. The runner determines eligible…

This notebook applies token-level SHAP to a pinned FinBERT model that classifies financial text as negative, neutral, or positive. It wraps model inference to preserve the checkpoint’s label order, then explains class probabilities by measuring how masked…

This notebook adapts TSMixer to predict forward ETF returns from historical momentum features. Its architecture alternates a shared linear transformation across the time axis with a feature MLP applied separately at each date. Pre-normalization and residual…

This notebook turns a supply chain knowledge graph and institutional ownership filings into features for downstream financial modeling. From the company network it derives topology measures, supplier concentration, competitive exposure, and bottleneck…

This notebook explains how to fine tune a transformer for financial named entity recognition, which identifies spans such as organizations, people, amounts, dates, and percentages in text. It focuses on BIO boundary labels and the alignment problem between…

This notebook adapts a supervised autoencoder to daily US equities. The model reconstructs ranked price and volume characteristics while auxiliary and main classification heads train a shared latent representation to predict whether returns are positive…

This dataset guide describes a monthly US equity panel of anonymized firms and characteristics used for machine-learning asset-pricing research. The data includes accounting and technical characteristics, returns, and pre-defined training and test periods…

This notebook presents a workflow for reducing a large set of ETF features to candidates for later modeling. It ranks features using cross-sectional Spearman information coefficients against forward returns, computed by date and then averaged. Newey–West…

This notebook explains how a stochastic discount factor (SDF) prices a cross-section of equity option signals. Unlike factor models that estimate exposures and factor returns, the SDF approach estimates one random variable that makes asset returns price…

This document introduces latent factors as common return patterns across stocks, with each stock's loading describing its exposure to a pattern. It contrasts principal component analysis, which extracts factors and stock loadings from the return panel alone,…

This notebook studies TabM, a neural network design for tabular data, on monthly firm characteristics. It compares three presets that increase hidden width and ensemble membership while keeping other training settings fixed. The shared runner fixes the…

This chapter outlines post-deployment governance for machine-learning trading systems. It separates technical failures, where identical inputs produce different outputs, from statistical decay, where outputs no longer predict returns. Its framework combines…