Knowledge library

This case study explains how to generate predictions for a holdout period after model and strategy choices have been made on validation data. It resolves the previously selected configuration, reconstructs its training specification, and fits it again using…

This notebook builds an LSTM allocator that maps recent ETF return and volatility features directly to long-only portfolio weights. A softmax output keeps weights nonnegative and fully invested, while a differentiable Sharpe-ratio objective trains the…

This notebook describes an LSTM sequence model for predicting stock returns from ordered windows of recent features. Each example uses 60 consecutive sessions for one stock; windows that cross listing gaps, halts, or other interruptions are excluded so that…

This notebook synthesizes registered validation results for five latent-factor estimators—PCA, IPCA, CAE, SDF, and SAE—across nine case studies. It first reports which estimators have results in each panel, then identifies the highest mean daily Spearman…

This notebook applies TabM, a weight-sharing neural ensemble, to a US equities panel represented as one feature row per stock and session. A shared two-layer backbone feeds multiple members, each with its own scaling vector and output layer; averaging their…

This notebook applies principal component analysis to a panel of US stock returns to represent broad shared movements with a smaller set of factors. Each factor is a direction of common variation, and each stock’s loading describes its exposure to that…

This notebook compares gradient boosting configurations for crypto perpetual futures using features built around the premium of perpetual contracts over spot. It varies tree capacity and loss function, including squared, absolute, and Huber losses, and…

This notebook uses double machine learning to estimate whether futures carry predicts subsequent returns independently of volatility, momentum, and cross-sectional carry rank. It residualizes both carry and returns against these confounders with flexible…

This notebook develops model-based features for crypto perpetual futures: per-asset conditional volatility forecasts from GJR-GARCH and a market-wide stressed-regime probability from a two-state Gaussian hidden Markov model fitted to funding settlements.…

This case study lays out a research pipeline for crypto perpetual futures, treating funding payments exchanged between long and short positions at regular settlements as a potential return source. It describes data and model stages from label construction…

This analysis compares feature triage results across nine trading case studies and asks whether features that pass screening predict stronger strategies. Its screening combines Benjamini–Hochberg false-discovery control with a second route based on a case…

This notebook describes DeePM, an end-to-end ETF portfolio policy designed to improve performance across varying market conditions. Its components include volatility-normalized return and trend features, an LSTM-based model, variable selection, conditioning…

This case study applies causal estimation to ETF momentum and asks whether its effect on forward returns varies with market volatility. It uses a continuous momentum treatment, a 21-day forward-return outcome, volatility and yield-curve controls, and a…

This notebook introduces NLinear as a simple sequence-model baseline for predicting returns across US equities. Each example uses a window of 60 consecutive sessions for one stock, with features arranged in time order. For each feature, the model subtracts…

This notebook applies pretrained FinBERT to financial headlines from FinMarBa and examines why its accuracy is far below figures reported for its Financial PhraseBank evaluation. The central diagnostic is label provenance: PhraseBank labels reflect…

This notebook evaluates NLinear, a minimal sequence model that subtracts the last observed level from each lookback window and maps the remaining values to a forecast. Its simplicity makes it a useful benchmark for more complex sequence architectures. The…

The document describes a walk-forward study of TabM neural networks predicting monthly returns from firm characteristics. Because the shared training runner fixes the objective to squared error for regression and cross-entropy for classification, the…

The document presents a workflow connecting standardized market-data loaders, a registry of prebuilt features, and signal diagnostics. Feature computation can be specified by names, parameter dictionaries, or YAML, supporting repeatable pipelines and…

This notebook presents a family-level view of five latent-factor approaches applied to an equity option analytics case study. PCA estimates common movements directly from the return panel; IPCA maps characteristics to exposures linearly; a conditional…

This chapter treats a backtest as an attempt to falsify a strategy through explicit assumptions about signal timing, execution, rebalancing, sizing, costs, constraints, and benchmarks. It compares vectorized and event-driven simulation by their treatment of…

This source describes safeguards for reconstructing a model request from its immutable training specification and validating the resulting run. It checks that the request uses a supported identity version, canonical execution tier, matching model family and…

This notebook presents instrumented principal component analysis as a model of cross-sectional returns. Instead of assigning each firm’s characteristics direct return weights, IPCA estimates a shared linear map from characteristics to factor exposures,…

This notebook compares four neural network designs on the same task: predicting the next daily return from a window of past returns, pooled across eight exchange-traded funds. It describes how a dense network, a one-dimensional convolution, an LSTM, and a…

This notebook evaluates sequence models for ranking S&P 500 stocks using windows of their option-surface and price histories. It pairs a normalized linear model, which has no recurrent memory, with an LSTM whose gates carry information through time. The…