Tudástár

This document describes a daily ETF dataset intended as a candidate pool for momentum and cross-asset research. It covers nine categories, including US and international equities, fixed income, commodities, specialty funds, and currencies. Data is sourced…

This notebook demonstrates single-factor evaluation using real ETF data and a price-derived momentum example. It measures cross-sectional Spearman information coefficients against forward returns, compares horizons, and explains the distinction between…

This case study screens candidate financial and model-based features against forward returns for a NASDAQ-100 microstructure strategy. It calculates cross-sectional rank information coefficients through time, adjusts inference for dependence and multiple…

This chapter presents retrieval-augmented generation as a way to make language models more useful for open-ended financial research, where unsupported claims and hallucinations can be costly. It walks through the system from document ingestion and…

This notebook selects a CME futures case study from registered validation backtests spanning signal rules, allocation, and risk overlays. It compares candidates across both return horizons and chooses the configuration with the highest validation Sharpe.…

This notebook develops features that require multiple asset series. It compares Engle-Granger and Johansen tests for cointegration, explains why co-movement alone does not imply a stationary spread, and estimates hedge ratios both with a full-sample…

This notebook explains how to construct forward intraday return labels for NASDAQ-100 stocks using minute bars and NBBO quotes. It defines returns from the next bar after a signal to a specified future endpoint, uses quote midpoints to reduce bid-ask bounce,…

This case study compares gradient boosting models that predict returns to expiry for short at-the-money straddles. It varies tree capacity and three fitting objectives: squared error, absolute error, and Huber loss. Walk-forward validation uses two periods,…

The document presents an operational workflow for keeping daily market data current: load an initial history, fetch a small overlapping delta, check for missing dates, and monitor freshness and validation issues. The overlap helps capture vendor revisions to…

The document describes how a CME futures study generates predictions for a holdout period after selecting a model configuration on validation data. The selected configuration and checkpoint are recovered from the validation results, then the model is…

This notebook fits a patched transformer to predict a primary label in an S&P 500 equity and options study. It divides each stock’s historical input window into contiguous patches and treats each patch as a token for attention. Attention can compare distant…

The document explains how to retrieve weekly CFTC Commitment of Traders data for selected futures products and save each product’s history as a Parquet file. COT reports capture Tuesday positioning and are released on Friday; trader categories vary between…

This exploratory analysis profiles hourly OHLCV data for Binance perpetual contracts alongside an eight-hour premium index. It checks dataset coverage, changing contract membership, units, missing values, and raw-bar OHLC consistency. The premium is stored…

This code presents a supervised autoencoder architecture for prediction from financial features. An encoder compresses normalized inputs into a bottleneck, while a decoder reconstructs the inputs as a regularizing task. An auxiliary prediction head operates…

This configuration describes a weekly at-the-money straddle-selling study on S&P 500 constituents. Positions are opened on Friday and held toward expiry, with daily stock hedges triggered by changes in option delta. The portfolio spreads capital across…

This notebook explains an ETF stochastic discount factor model that fits a single series of weights to price returns across funds. Unlike models that separately estimate fund exposures and factor returns, it trains two networks against pricing errors: one…

This notebook describes how to create holdout predictions for a previously selected US equities model configuration. Since model choices and strategy settings were made using validation results, those same results cannot independently establish the quality…

This notebook presents a workflow for monitoring a deployed trading model using holdout predictions as an offline stand-in for live data. It validates the incoming feature panel before calculating drift, then compares reference and current windows with…

This notebook presents GReaT, a method for generating synthetic financial tables with a language model. It serializes each row as text, fine-tunes a GPT-based model on ETF features, and parses generated text back into mixed numerical and categorical columns.…

This notebook explains how to construct model-based features for a US equities panel without letting future observations influence historical values. Its central method is an estimation schedule: models use a burn-in period, fit only on data preceding the…