Knowledge library

This configuration defines a Qlib workflow for training a Temporal Fusion Transformer model on Alpha158 features for CSI 300 stocks. It sets Chinese market data from 2008 through mid-2020, using 2008–2014 for training, 2015–2016 for validation, and 2017–2020…

This paper description presents a learnable scheduler for sequence-learning problems with related prediction tasks, such as forecasting returns at different future horizons. During training, the scheduler chooses an auxiliary task based on the current model…

The document introduces Temporal Routing Adaptor (TRA), a model designed to learn multiple trading patterns from stock market data. It describes using TRA with Qlib datasets and workflows, and notes that the paper’s reproduction setup first trains a backbone…

This configuration describes a Qlib experiment using a graph attention model, GATs, with an LSTM base model to predict near-term returns for CSI 300 constituents. It sets Chinese market data, defines a close-to-close forward return label, normalizes features…

This configuration describes a Qlib machine-learning workflow that trains a CatBoost regression model on Alpha158 features for CSI 300 instruments. It defines separate training, validation, and test periods, then records signal analysis and portfolio…

This configuration defines a Qlib experiment that trains an IGMTF model on Alpha360 features for CSI 300 stocks. It uses historical data from 2008 through 2020, with training through 2014, validation in 2015–2016, and a held-out test period beginning in…

This configuration defines a Qlib workflow for training a TabNet model on Alpha158 features for CSI 300 stocks, using Chinese market data and the CSI 300 index as benchmark. It sets a historical data window, separates fitting, validation, and test periods,…

This configuration defines a Qlib workflow for predicting short-horizon CSI 300 stock returns with a temporal convolutional network (TCN). It uses Alpha158 features, filters a specified set of feature columns, applies robust cross-sectional normalization,…

This QlibRL example describes how to configure and run a reinforcement learning workflow for executing orders in one asset. The training setup defines a simulator with 30-minute steps, a categorical action space, a full-history state representation, a…

This configuration describes a Qlib workflow for training a binary LightGBM model on one-minute CSI 300 data from China. It uses the Alpha158 feature handler, robust feature normalization, missing-value filling, and cross-sectional label ranking. The label…

This configuration defines a Qlib workflow that trains a PyTorch feedforward neural network on Alpha360 features for CSI 500 stocks. It uses a forward close-price return label, robust feature normalization, missing-value filling, and cross-sectional rank…

This configuration specifies a Qlib workflow for training the HIST model on Alpha360 features for CSI 300 stocks and evaluating its signals in a portfolio backtest. The data handler normalizes features, fills missing feature values, drops rows without…

This configuration describes a Qlib workflow for training a KRNN model on China’s CSI 300 universe with Alpha360 features. It sets a historical data range, uses robust feature normalization and cross-sectional label ranking, and defines a two-day forward…

This Qlib documentation explains a workflow for preparing financial data for quantitative research. Users convert market data into Qlib’s binary format, derive features with its expression engine, apply more complex transformations through data handlers, and…

This documentation explains formulaic alpha factors: signals represented as mathematical expressions that can be computed from market data. It uses MACD as an example, defining the signal from the difference between short- and long-period exponential moving…

This configuration sets up a Qlib experiment that uses a gated recurrent unit model with Alpha360 features to rank CSI 300 constituents. Feature values are robustly normalized with outlier clipping and missing-value filling; labels use cross-sectional rank…

This configuration defines a Qlib workflow that trains an ordinary least squares linear model on Alpha158 features for CSI 500 stocks. The data spans 2008 through mid-2020, with training through 2014, validation in 2015–2016, and testing from 2017 onward.…

This configuration defines an equity prediction workflow for China’s CSI 300 universe. It applies robust feature normalization and missing-value filling, drops labels with missing values, and ranks labels cross-sectionally. An ordinary least squares linear…

This notebook demonstrates an end-to-end Qlib workflow for a China-market stock ranking model. It initializes Qlib data, uses the CSI 300 universe and Alpha158 features, and trains a LightGBM model on historical data with separate training, validation, and…

This Qlib documentation explains why historical strategies need the versions of financial data that were available at each past decision time. Financial statements can be revised after publication; using only the latest value in a historical simulation can…

This document describes a data preparation workflow for daily risk estimates on China A-shares. For each date, it selects the CSI 300 constituents, gathers a rolling window of closing prices, calculates returns, and clips extreme returns at the…

This configuration specifies a Qlib workflow for a TabNet model using the Alpha360 feature handler and CSI 300 instruments, with the index as benchmark. It sets a two-day-ahead close-price return label, applies robust normalization and missing-value filling…

This configuration specifies a Qlib workflow for training a CatBoost model 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. The model…

This configuration describes a Qlib workflow for training a Localformer model on China A-share data and evaluating its predictions as a portfolio signal. It uses the CSI 300 universe and benchmark, an Alpha360 data handler, robust feature normalization,…