Knowledge library

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 compares reference ETF portfolios under historical crises, hand-defined simultaneous asset shocks, and Monte Carlo loss scenarios. Historical windows include major equity and rate-driven selloffs, with portfolio returns compounded over explicit…

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 source module builds catalog tables from experiment registries for model predictions and backtests. It defines reserved fields for provenance, identity, completion status, artifact availability, and metrics. The catalog carries multiple measures that…

This case study evaluates daily momentum and carry signals across a small, correlated universe of G10 spot currency pairs. Its workflow moves from feasibility checks and forward-return labels through engineered and model-based features, cross-validation,…

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 brings an ETF case study’s registered backtests together into an uncertainty-aware strategy assessment. It reads performance measures with block-bootstrap confidence intervals and uses paired bootstraps to compare successive pipeline stages,…

This notebook implements DeePM, an end-to-end portfolio policy for a diversified ETF universe. Its model combines temporal features, asset metadata, cross-sectional attention, and a macro graph prior that permits attention within asset classes and across…

This case study turns stored model predictions for US stocks into long-short, equal-weight portfolios. It sweeps multiple entry schemes, each selecting the highest-ranked names for long positions and the lowest-ranked names for short positions, to examine…

This document describes several ways to convert prediction scores into trading signals: fixed cutoffs, rolling per-asset percentiles, and cross-sectional percentiles. Fixed thresholds use a chosen score level; rolling thresholds adapt to an asset’s recent…

This document describes utilities for comparing registered trading strategies and assembling structured case-study assessments. A central ranking method restricts return series to their exact shared timestamps before calculating portfolio metrics, so each…

This chapter surveys methods for recovering common structure from return and characteristic panels, from PCA and eigenportfolios to IPCA, risk-premium PCA, conditional autoencoders, stochastic discount factor estimation, and supervised autoencoders. Its…

This notebook turns cross-sectional stock predictions into long-short portfolios by ranking stocks at each rebalance, buying the top k and shorting the bottom k with equal capital per position. Equal weighting provides a baseline that isolates differences in…

The document presents a machine-learning trading research workflow that carries ideas from data and feature construction through model training, backtesting, transaction costs, portfolio and risk decisions, deployment, and monitoring. It emphasizes an…

This notebook runs the already selected FX strategy specification against registered holdout predictions. It preserves the strategy’s signal, allocation, risk controls, rebalance rule, costs, and account settings; the prediction set and price-frame identity…

This notebook studies six allocation methods applied to selected crypto perpetual strategy configurations. The rankings and entry rules remain fixed while the capital assigned to each position changes. The methods use model scores, individual contract…

This notebook compares equal weighting with two allocation rules for US firm characteristic strategies: score weighting, which assigns more capital to higher model predictions, and conformal weighting, which reduces capital for predictions with wider…

This notebook builds an end-to-end portfolio allocator that places a Temporal Fusion Transformer style variable-selection network ahead of an LSTM. Separate gated residual networks embed individual features, and learned soft weights combine those embeddings…

This notebook presents Tail-GAN, a generative adversarial approach designed to reproduce financial scenario tail risk, especially value at risk (VaR) and expected shortfall (ES). It uses differentiable sorting so quantile-based risk measures can contribute…

This configuration module describes a framework for declaring backtest sweep settings per case study. It reads entry schemes, allocation methods, trading cost grids, and risk controls from setup files, with explicit errors when required configuration is…

This chapter explains how to build and use financial knowledge graphs when questions depend on relationships such as ownership, supply chains, or contagion. It covers entity identity, typed relationship schemas, provenance, and LLM-assisted extraction from…

This notebook analyzes a portfolio of equity ETFs with CAPM and Fama-French factor regressions, then expands the analysis to rolling exposures, return attribution, and residual dependence. It uses aligned daily returns and factor data, estimates regression…

This implementation describes a neural stochastic discount factor model and an adversarial moment network for testing asset pricing relations. The SDF network maps asset characteristics, optionally combined with a macroeconomic regime representation from an…

This document describes a producer for paired performance comparisons across strategy-selection stages. It forms comparisons between signal selections and equal-weight references, between holdout and validation selections, and across allocation,…