নলেজ লাইব্রেরি

The document describes loading ETF market data with optional symbol and date filters, plus a deterministic limit on the symbols returned. Its main analytical point is to match the price series to the quantity being measured: adjusted prices are appropriate…

This notebook builds model-based conditional volatility features for S&P 500 shares with a GJR-GARCH(1,1) model, then compares an option-implied volatility spread measured against realized volatility with one measured against a model forecast. Since…

This CME futures study varies gradient boosting tree capacity and loss function while recording predictions at multiple training checkpoints. Tree leaf count controls how finely a model partitions the feature space, while squared, absolute, and Huber losses…

This notebook studies how Ridge regularization choices affect information coefficient on an ETF panel and how hyperparameter search can inflate reported performance. It first evaluates a wide alpha grid across walk-forward folds, scaling features within each…

This notebook explains how to construct one-, five-, and 21-session forward spot returns for a fixed FX-pair universe. It first maps four-hour bars into trading sessions using a New York rollover calendar, then builds returns without removing rows before…

This notebook runs the already selected FX strategy specification against registered holdout predictions. It preserves the strategy’s signal, allocation, risk controls, rebalance rule, costs, and account settings; the prediction set and price-frame identity…

This notebook presents a current audit comparing VectorBT Pro and VectorBT OSS with ML4T on supported real-data strategy workloads. Both VectorBT editions participate in ETF allocation, USD-quoted foreign-exchange allocation, and a US equity panel. Pro also…

This notebook explains instrumented principal component analysis (IPCA), which models each ETF’s factor exposures as a shared linear function of that fund’s observed features. Alternating least squares estimates the feature-to-exposure map and the factor…

This notebook studies six allocation methods applied to selected crypto perpetual strategy configurations. The rankings and entry rules remain fixed while the capital assigned to each position changes. The methods use model scores, individual contract…

The notebook presents an autonomous research workflow in which a language model uses general-purpose tools to inspect files, run commands, query a backtest registry, and examine data. Rather than hard-coding research actions, the operator discovers…

This notebook fits a PCA latent-factor model to a panel of stock returns, without using option-surface features, characteristics, or target information. PCA extracts leading directions of historical cross-sectional co-movement, estimates each stock's…

This shared feature-engineering module defines construction rules and audits for financial predictors used across multiple case studies. It records each feature family with its hypothesis, inputs, rolling lookback, information lag, role, and potential…

This notebook explains why a reported Sharpe ratio is an uncertain estimate, not a definitive measure of strategy quality. It develops confidence intervals and the probabilistic Sharpe ratio, which estimates the chance that a strategy's true Sharpe exceeds a…

This notebook compares equal weighting with two allocation rules for US firm characteristic strategies: score weighting, which assigns more capital to higher model predictions, and conformal weighting, which reduces capital for predictions with wider…

This analysis compares learned signals for monthly cross-sectional US stock returns using accounting and market characteristics, with next-month return as the target. It reviews linear models, boosted trees, deep tabular methods, latent-factor estimators,…

This chapter presents agentic workflows for financial research tasks where gathering, filtering, and synthesizing uneven evidence is the main challenge. It contrasts these workflows with conventional models that work well on stable structured inputs, and…

This audit compares Backtrader and Zipline with ML4T on real strategy cases, using only asset and contract combinations that each engine and the frozen data bundle can represent natively. It covers ETF allocation and a US equity panel for both engines, plus…

This notebook defines and validates monthly return labels for a cross-sectional US firm-characteristics study. It checks how the data provider pairs each row’s characteristics with the return outcome, emphasizing that the panel is already aligned and must…

This notebook builds an end-to-end portfolio allocator that places a Temporal Fusion Transformer style variable-selection network ahead of an LSTM. Separate gated residual networks embed individual features, and learned soft weights combine those embeddings…

This notebook explains how to evaluate financial models without allowing information unavailable at a decision date to leak into training or model selection. It distinguishes training, validation, and a final holdout set, and shows why randomly shuffled…

This notebook compares four ways to discover relationships in a panel of ETF returns: NOTEARS for contemporaneous linear DAGs, VAR-LiNGAM for lagged and instantaneous structure, PCMCI for conditional-independence tests, and Granger tests as pairwise…

This notebook studies how Ridge regularization affects information coefficient (IC) performance and how hyperparameter search can bias reported validation results. It first evaluates a wide alpha grid with walk-forward folds, scaling features inside each…

This notebook presents Tail-GAN, a generative adversarial approach designed to reproduce financial scenario tail risk, especially value at risk (VaR) and expected shortfall (ES). It uses differentiable sorting so quantile-based risk measures can contribute…

This notebook compares commission and slippage models for equities and futures, then examines how commission assumptions affect a fixed ETF momentum strategy. It explains how percentage, per-share, minimum, combined, and tiered commissions produce different…