Tietokirjasto

This Qlib guide explains how to connect a user-defined forecast model to the framework. A custom class subclasses Qlib’s model base and implements initialization, fitting, and prediction; the fit and prediction methods receive a dataset through the expected…

This configuration sets up a Qlib experiment that uses a double-ensemble model built from gradient-boosted trees to rank CSI 300 stocks. The dataset uses Alpha158 features and divides the history into training, validation, and test segments. Model settings…

This configuration describes a Qlib workflow that trains a gated recurrent unit (GRU) model on Alpha158 features to rank CSI 300 stocks. It uses 20-step time-series samples, robust feature normalization, missing-value filling, and cross-sectional label…

This example explains nested decision execution in Qlib backtesting: one strategy forms a portfolio at a slower frequency while another handles orders at a faster frequency. The first workflow generates portfolios weekly with DropoutTopkStrategy, described…

This configuration specifies a Qlib experiment using Alpha158 features for CSI 300 equities, with the Shanghai Composite 300 index as benchmark. It divides the history into training, validation, and test periods, applies robust feature normalization and…

This configuration specifies a Chinese equity research workflow using Qlib, an XGBoost model, and the Alpha158 feature handler. The dataset is divided chronologically into training, validation, and test periods, with the model fitted on the training interval…

This configuration specifies a Qlib experiment that trains an attention-based LSTM model with a GRU recurrent layer on Alpha158 features for CSI 300 instruments. It selects 20 features, applies robust normalization and missing-value filling, and uses a…

This tutorial walks through assembling a quantitative equity research workflow with Qlib. It covers retrieving and inspecting market data, interpreting adjusted prices, working with dynamic universes and point-in-time fundamentals, and constructing features.…

This Qlib configuration describes a machine-learning workflow for ranking CSI 300 stocks. It uses the Alpha360 data handler and a double-ensemble model built from gradient-boosted trees. The configured label is a forward close-price return, while the…

This stock screen targets companies associated with beverage and alcohol imports or exports. It filters for turnover between 3% and 12% and a daily price change above -5% but below 2.6%. The article presents these as industry, liquidity, and price-movement…

This source code implements components of a Temporal Fusion Transformer, a neural network architecture for time-series forecasting. The visible sections define feed-forward layers, gated linear units, gated residual networks, skip connections with layer…

This configuration defines a Qlib workflow that trains an LSTM on Alpha360 features to rank CSI 300 stocks. It normalizes feature data with robust z-scores, fills missing feature values, drops missing labels, and cross-sectionally ranks labels. The…

This Qlib configuration sets up a time-series forecasting and portfolio backtest workflow for the CSI 300 universe, using the Shanghai-Shenzhen 300 index as its benchmark. The dataset uses Alpha158 features, applies robust feature normalization and…

This Qlib configuration defines a Chinese-equity ranking workflow using the Alpha158 feature handler and a ridge linear model. It assigns CSI 300 instruments and the related index benchmark, with historical data split into training, validation, and test…

This example demonstrates how to query tick, transaction, and order data with Qlib and resample irregular observations into minute-level series. It constructs candidate features from multiple levels of the bid and ask books, including normalized spread and…

The document introduces DDG-DA, a method for adapting forecasting models when streaming data changes over time. Rather than waiting to detect a shift and then fitting to recent observations, it first predicts how the data distribution may evolve, generates…

This benchmark page compares stock-ranking and return-prediction models in Qlib workflows using the Alpha158 and Alpha360 datasets. It evaluates signals with information and rank correlations, and evaluates portfolios with annualized return, information…

This configuration specifies a Qlib workflow that trains a PyTorch deep neural network on Alpha158 features for the CSI 500 universe. The data spans 2008 through mid-2020, with training through 2014, validation during 2015–2016, and testing from 2017 onward.…

This configuration specifies a Qlib experiment that trains an ALSTM model on China’s CSI 300 universe and evaluates stock selections in a portfolio backtest. The data handler uses Alpha360 features, robust feature normalization, missing-value filling, and…

This Qlib configuration defines a Chinese equity workflow using the CSI 300 universe and its associated benchmark. It prepares Alpha360 features with robust score normalization and missing-value filling, while labels are rank-normalized after missing labels…