Knowledge library

This document turns cross-sectional ETF predictions into simulated trades. It distinguishes ranking quality, measured by information coefficient, from realized strategy performance: a top-k portfolio depends on the relative score values, rebalance schedule,…

This notebook describes a read-only comparison of validation predictions from several model families on a US equities panel. It first checks that each predefined prediction set is complete, then assesses cross-sectional ranking with the information…

This notebook explains a family of ETF models that represents returns through shared latent directions and estimates how fund features map to exposures. It distinguishes five approaches: unconditional principal components, instrumented PCA with a linear…

This notebook uses a synthetic asset panel to demonstrate Instrumented PCA, where factor loadings depend linearly on characteristics observed before returns. Alternating least squares estimates the characteristic-to-loading map and realized factors. Because…

This case study compares predictive models for monthly cross-asset rotation across ETFs spanning equities, fixed income, commodities, currencies, and real estate. Its central lesson is that information coefficient (IC) and trading performance can rank models…

This notebook tests whether standard portfolio allocation can improve an every-bar NASDAQ-100 trading strategy that is already burdened by transaction costs. It selects predictions using validation performance on the declared cost-feasible universe, then…

This notebook studies how position sizing affects FX backtests after the model has already selected which currency pairs to trade. It preserves the winning baseline’s predictions, signal mapping, costs, and execution settings, then varies allocation rules.…

This notebook queries backtest registries across nine case studies and assembles comparable tables for downstream analysis. It organizes results by asset class and data frequency, then records performance across signal, allocation, cost, and risk stages,…

This notebook presents a deployment bridge between an offline machine-learning pipeline and QuantConnect's LEAN trading engine. It selects a model run reproducibly from a registry, exports its holdout predictions as date-indexed JSON, and uses a small…

This chapter presents portfolio construction as the process of converting return forecasts, risk estimates, and constraints into weights, leverage, and rebalancing decisions. It lays out a research workflow for documenting allocator choices, avoiding…

This notebook explains how to evaluate position-level exits and combine them with portfolio-wide controls. Fixed stop-loss, take-profit, and time exits are contrasted with trailing and tightening stops; a scaled exit reduces a position at successive profit…

This notebook synthesizes results from nine market case studies into a cumulative strategy-screening funnel. It tests, in order, whether a model has positive information coefficient, whether its selected configuration has positive validation Sharpe, whether…

This notebook builds a portfolio allocator that places a Temporal Fusion Transformer-style variable-selection network before an LSTM encoder. The selection network embeds each input feature separately and assigns softmax weights, allowing the model to vary…

This notebook applies principal component analysis to changes in Treasury yields across maturities. Standardizing changes gives each maturity equal influence, and the resulting components are interpreted as level shifts, steepening or flattening, and…

This tutorial derives the Kelly fraction for a binary wager by maximizing expected logarithmic wealth growth, then extends the idea to continuous returns and a multi-asset portfolio. It uses symbolic differentiation and simulations with shared coin-toss…

This chapter treats transaction costs as a constraint throughout strategy research and deployment, from factor evaluation and backtesting to portfolio construction, risk oversight, and production monitoring. It distinguishes explicit fees, implicit spread…

This notebook turns institutional 13F holdings into a bipartite institution-to-stock graph and derives features for research, including stock co-ownership similarity, ownership breadth and concentration, and changes in reported holdings. It aggregates…

This notebook explains how to decompose ETF returns and risk using CAPM and Fama–French factor regressions. It estimates full-sample exposures with heteroskedasticity and autocorrelation robust standard errors, tracks changing betas with rolling windows, and…

This notebook adapts skip-gram Word2Vec to institutional holdings by treating each 13F portfolio as a sentence, each stock identifier as a token, and position size rank as token order. Nearby positions form the context, so stocks that institutions place in…

This notebook compares ways to allocate capital across US equity positions while holding the model, checkpoint, rebalance dates, and selected stocks fixed. It examines weights based on prediction strength, prediction-interval width, individual stock…

This notebook turns validation predictions from multiple model families into equal-weight, long-short portfolios. It ranks stocks by predicted return, buys the top names and shorts the bottom names, and sweeps several portfolio concentrations using a shared…

This notebook compares six long-only ETF allocation methods designed to reduce reliance on unstable estimates. It applies Ledoit-Wolf covariance shrinkage to estimators that use covariance, and contrasts mean-variance maximum Sharpe with minimum variance,…

This chapter synthesizes nine case studies that take machine-learning signals through portfolio construction, trading costs, risk overlays, and frozen holdout evaluation. It treats each case study’s progression as the unit of analysis instead of ranking…

This notebook compares ways to size positions in a US equities panel while holding the model, checkpoint, rebalance dates, and selected stocks fixed. Prediction-based methods scale capital by forecast magnitude or interval uncertainty; inverse volatility and…