Kunnskapsbibliotek

The guide lays out different entry paths for using QlibRL, depending on whether the reader is new to reinforcement learning, researches RL algorithms, or already has quantitative finance experience. It recommends learning RL fundamentals, understanding…

This configuration describes a Qlib data pipeline for five-minute CSI 300 constituent data over a specified two-year period. It defines training, validation, and test segments, with the training interval also used to fit feature processing. The feature…

This Qlib documentation explains how a client can access market data managed on a central server. The client configuration points to a provider location, a local mount path, and a data service endpoint; NFS mounts the shared files, while a Flask service…

This guide outlines an end-to-end quantitative research workflow using Qlib. It describes installing the library, preparing Chinese market data from public sources, and running an example LightGBM configuration with the qrun tool. That workflow combines…

This configuration defines a Qlib workflow that trains a LightGBM model on Alpha158 features for the CSI 500 universe. It assigns data from 2008 through mid-2020 to training, validation, and test segments, with the fit period ending in 2014. The model uses…

This documentation excerpt explains how to access market data through Qlib after initializing it with a local data provider. It demonstrates loading a trading calendar, retrieving instruments from a market universe, and filtering that universe by instrument…

GeneralPtNN is presented as a redesign intended to support both time-series and tabular datasets through a common PyTorch workflow. The stated approach is to keep the workflow configurable and change the network and dataset classes when moving between data…

This configuration specifies a Qlib experiment for generating equity signals on the CSI 300 universe, using the Shanghai Shenzhen 300 index as its benchmark. It sets a historical data range and separates training, validation, and test periods. The data…

This configuration defines a Qlib workflow that trains a CatBoost model on Alpha360 features for CSI 300 constituents. It uses Chinese market data from 2008 through mid-2020, with training through 2014, validation over 2015–2016, and testing from 2017…

This configuration describes a Qlib workflow for training a LightGBM model and using its forecasts in an enhanced-indexing portfolio strategy. It specifies CSI 300 instruments and Shanghai’s CSI 300 index as the benchmark, with Alpha158 features and…

The code describes a manager for tuning model hyperparameters through random search over discrete parameter ranges. For each trial it samples one value per tunable parameter, adds fixed experiment parameters, checks that the parameter set is complete, and…

This configuration describes a Qlib workflow for training a LightGBM model on Alpha360 features and evaluating its predictions on CSI 300 constituents. The label compares closing prices across the next two reference periods. The data handler drops missing…

This configuration specifies a Chinese equities prediction and portfolio backtest using Qlib, LightGBM, and the Alpha158 feature handler. It pairs daily labels with one-minute features, resampling the minute data at 14:56. The listed data span begins in 2008…

This Qlib configuration defines a temporal convolutional network workflow for ranking CSI 300 stocks. It uses Alpha360 features, robust feature normalization, missing-value filling, and cross-sectional rank normalization of labels. The prediction target is…

The document introduces a periodically rolling retraining framework for forecasting models. Its central idea is to refresh a model using up-to-date data at intervals so its forecasts can adapt as market conditions change. The default model is linear, and…

This configuration specifies a Qlib workflow for predicting CSI 300 stock returns with a gated recurrent unit (GRU) neural network. It uses Alpha158 features, filters to a selected feature set, applies robust normalization and missing-value filling, and…

The document explains Qlib’s meta-controller framework for learning patterns across forecasting tasks and using them to guide future tasks. A meta-task packages data for a meta-model, while a meta-dataset manages how task information is generated and…

This configuration defines a Qlib equity-prediction experiment for China’s CSI 500 universe. It trains a double-ensemble model built on gradient boosting, using Alpha360 features and a forward close-to-close return label. Training and validation segments…

This example shows how to assemble a quantitative equity research workflow in Python using Qlib. It initializes Chinese market data, creates a model and dataset from a task configuration, fits the model, and records predictions and signal analysis. It then…

This configuration specifies a Qlib time-series Transformer workflow for predicting Chinese CSI 300 stock returns from Alpha158 features. It defines a Chinese market data source, uses the CSI 300 as the instrument universe and SH000300 as the benchmark, and…

This configuration lays out a Qlib workflow for training a Transformer model on Alpha360 features and ranking CSI 300 stocks. It specifies Chinese market data, a close-to-close forward return label, robust feature normalization, missing-value filling, and…

This Qlib documentation explains its experiment-management structure for organizing quantitative research. An experiment manager oversees experiments, each experiment groups recorders, and each recorder represents an individual run. The high-level Qlib…

This Qlib configuration defines a supervised stock-ranking experiment using the CSI 300 universe and the Sandwich neural model. Its data handler applies robust feature normalization with outlier clipping, fills missing feature values, drops missing labels,…

This Qlib workflow example combines an LightGBM model trained on Alpha158 features with a top-ranked stock strategy for the CSI 300 universe. It sets training, validation, and test periods, then demonstrates nested execution across daily, 30-minute, and…