Tudástár

This notebook develops a financial feature matrix for a cross-asset ETF momentum hypothesis: assets with stronger relative performance may continue to outperform over the following month. It combines trailing returns at several horizons, risk-adjusted…

This document presents a cross-market inventory of model-based feature artifacts from nine case studies. It reads parquet schemas rather than loading their rows, excludes identifier columns, counts feature columns, and groups names by tokens associated with…

This document explains how to build and inspect a feature matrix for a cross-sectional ETF momentum and rotation hypothesis. It defines each feature’s lookback and information lag, then constructs trailing returns, risk-adjusted returns, volatility, trend…

This document reframes a five-session equity return prediction by sampling daily data on Fridays. The label remains a five-session return, but on the weekly grid it spans about one model step. The notebook compares direct regression, using a fixed lookback…

This document describes refitting the configuration selected by earlier validation stages on all eligible pre-2021 history, then generating predictions for the 2021 holdout. It derives the training interval from the declared evaluation window, label buffer,…

This notebook evaluates gradient-boosted trees on equity option analytics, where features such as implied volatility, skew, term structure, and variance risk premium encode market expectations. It asks whether a nonlinear model can combine those forecasts…

This notebook tests whether gradient-boosted trees improve cross-sectional ranking across currency pairs beyond a penalized linear model. The FX universe contains pairs sharing currencies, so observations are dependent: a move in one currency affects…

This utility module supports deep learning workflows for financial time series across multiple assets. It resolves dataset aliases and loads canonical case study data, then creates sliding-window sequences independently for each symbol. The sequence…

This notebook compares two locally run, open-weight embedding models on passages from recent 10-K filings and a fixed set of financial research queries. It builds two-sentence passages, embeds the same documents and queries with each model, and evaluates…

This notebook constructs price-derived features for a broad US equities panel, including momentum, moving averages, and volatility measures. It is designed to rank stocks against one another, using a tradability screen, per-symbol rolling calculations, and…

This notebook compares three ways to orchestrate a four-phase forecasting workflow: direct composition in Python, a role-prompted CrewAI version, and a LangGraph version that delegates to the same specialist classes as the native implementation. It examines…

This notebook builds minute-level features from NASDAQ-100 quote and trade data to study short-horizon price pressure. It treats normalized order-flow imbalance as the main signal candidate and uses spread, depth, price impact, off-exchange trading,…

This document describes a ledger for applying funding cash flows to perpetual futures positions during a backtest. At each funding timestamp, it uses the position’s signed quantity, the current mark, any contract multiplier, and the funding rate to calculate…

This notebook checks whether four-hour spot FX data can support a daily cross-sectional strategy that ranks currency pairs using momentum and carry. It tests whether the declared instruments have prices at each decision point, whether the universe represents…

This notebook evaluates stop-loss, trailing-stop, and fixed-duration exits as overlays on CME futures strategies. For each prediction horizon, it applies configured rules to the strongest validation-Sharpe parent selected from prior signal and allocation…

This notebook screens financial and model-based features for their ability to rank stocks by a forward return. It computes daily cross-sectional information coefficients, estimates uncertainty while accounting for serial dependence, adjusts significance for…

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 notebook describes a double machine learning analysis of the effect associated with an FX momentum treatment after adjustment for configured confounders. Flexible nuisance models estimate the outcome and treatment from those confounders; cross-fitting…

This notebook compares three linear forecasters, a Transformer encoder, and two parameter-free forecasts on daily SPY returns. Linear models map a historical window to a multi-day forecast; variants first separate a smoothed component or account for the last…

This notebook configures an NLinear forecasting run for FX pairs. The model uses a fixed consecutive lookback and subtracts the last observed level, directing its fit toward changes over that window. It resolves the lookback, normalization, device, folds,…

This guide explains how to turn hourly continuous futures data into daily bars aligned to CME trading sessions. Because a session ends at 4 PM Central Time, bars from Sunday evening belong to Monday's session, and bars after the close generally count toward…

This code defines safeguards for reproducible cross-validation, eligibility tracking, and fold-scoped temporal features. It normalizes fold boundaries, compares requested folds with the boundaries used to create temporal artifacts, and rejects incompatible…

This notebook outlines validation-only diagnostics for a 10-session delta-hedged S&P 500 options return label. It organizes financial predictors into implied-volatility-dependent and independent groups, then compares Ridge models using a single volatility…

This notebook recasts prediction of 21-session ETF forward returns as a binary task: positive returns are labeled up, and all others down. It fits L2- and L1-regularized logistic regression using chronological walk-forward folds with a purge gap. Scaling is…