Tudástár

This shared modeling module supports quantitative research workflows by loading features, labels, temporal data, and configuration, then preparing walk-forward cross-validation folds. Its design emphasizes reproducibility and data lineage: hashes capture…

This notebook shows how to turn quarterly SEC 10-Q Management’s Discussion and Analysis text into two candidate equity signals. FinBERT scores text chunks for positive, neutral, or negative tone; chunk scores are aggregated into average sentiment and…

This notebook compares three ways to use market regimes in forecasting: a baseline model without a regime input, a single model given the filtered probability of a volatile state, and separate models trained within hard regime assignments. To avoid…

This notebook checks whether the data can support a weekly, cross-sectional futures strategy before any model is fitted. It describes a design that ranks CME products, takes long positions in the highest-ranked contracts and short positions in the lowest,…

This notebook expands an ETF feature evaluation from a single return horizon to a scan across ten features and three horizons, correcting for multiple tests. It then applies mechanism-based diagnostics to long-lookback 12-1 momentum and short-term reversal:…

This notebook estimates whether buyer-initiated signed volume share is associated with future 15-minute returns after accounting for relative spread, recent realized volatility, and trailing monthly return. Double machine learning predicts the outcome and…

This notebook uses Double Machine Learning (DML) to estimate the adjusted effect of a continuous ETF momentum measure on subsequent returns, controlling for recent and longer-term volatility, market regime, and yield-curve slope. It compares unadjusted…

This notebook explains Hierarchical Risk Parity (HRP), a portfolio construction method designed to limit sensitivity to noisy covariance estimates. It describes its three stages: cluster assets by correlation distance, reorder them according to the resulting…

This notebook assesses registered ETF backtests across the strategy pipeline, from signals and allocation through costs and risk overlays. It reads existing predictions and backtests rather than fitting models or adding new backtests, while deriving cohort…

This notebook sets up an LSTM forecasting run for an FX-pairs case study. The model carries a hidden state through a lookback sequence, and the notebook resolves architecture and training settings through the shared configuration and study-planning tools. It…

This notebook uses NASDAQ ITCH-derived trade records to examine intraday trading activity. It resamples individual trades into time bars and compares share volume, trade counts, last prices and volume-weighted prices for one ticker selected from each of…

This notebook explains how market impact grows with order size and uses the square-root participation model to frame strategy capacity. It distinguishes inputs available from market data, such as volatility and average daily volume, from the impact…

This guide introduces two sources of US equity order book tick data for microstructure research: Databento market-by-order events and NASDAQ TotalView-ITCH messages. It describes their formats, coverage characteristics, approximate data sizes, and tradeoffs.…

This notebook examines how return forecasts for ETFs can express uncertainty, comparing Monte Carlo dropout with deep ensembles. Dropout remains active across repeated inference passes, while an ensemble measures disagreement among separately trained…

This notebook compares six ways to weight ETF portfolios selected by the same predicted ranking: equal weight, score weighting, inverse volatility, risk parity, mean-variance optimization, and hierarchical risk parity. It varies the number of selected funds…

This notebook gathers registered double machine learning estimates from multiple trading case studies and compares their uncertainty and diagnostics. It loads current rows from each study’s registry, checks registry integrity and label uniqueness, makes…

This notebook fits a declared LSTM model for forecasting in an S&P 500 options study. The network receives chronological windows of each symbol’s history and uses gated recurrent state to represent information across sessions, rather than relying only on…

This notebook trains a PPO agent to schedule a parent sell order across hourly perpetual-futures bars. It augments price and volume history with the perpetual premium and time to the next funding settlement, while lagging information that would not yet be…

This notebook compares three ways to classify financial-news sentences as positive, neutral, or negative: TF-IDF word and phrase features with logistic regression, averaged static word vectors with a classifier, and a pretrained FinBERT sentiment model.…

This notebook explains how to build and inspect a quarterly equity fundamentals panel from SEC XBRL data. Each observation carries both the fiscal period it describes and the filing date when the information became public. That distinction is essential in…

This notebook evaluates accounting and price-based characteristics as standalone predictors of next-month US stock returns. It computes monthly cross-sectional rank correlations between each characteristic and the return label, then summarizes them across…

This notebook screens candidate features for a CME futures case study against forward returns. For each feature, it computes cross-sectional rank correlations between feature values and subsequent returns, summarizes the correlation over dates, adjusts…

This notebook presents a diagnostic framework for an ETF allocation backtest. It calculates risk and return measures such as Sharpe, Sortino, Calmar, volatility, value at risk, and maximum drawdown; tracks rolling performance and drawdown recovery; and…

This notebook clusters monthly macroeconomic indicators to describe market conditions independently of equity returns. It aligns FRED series to month-end observations, standardizes unemployment, the federal funds rate, the yield-curve spread, and…