Tietokirjasto

QlibRL organizes a reinforcement learning workflow for trading around an environment wrapper that connects a policy to a market simulator. The wrapper accepts actions, advances the simulated market, and returns updated states and rewards. Its components…

This configuration specifies a Qlib workflow for training a LightGBM model on the Alpha158 feature set for China’s CSI 300 universe. It defines training, validation, and test periods, along with model settings such as mean squared error loss, tree depth,…

This Qlib workflow configuration specifies a linear ordinary least squares model using the Alpha158 feature handler for the CSI 300 universe. The data span begins in 2008 and ends in 2020; the training segment runs through 2014, validation covers 2015–2016,…

This configuration describes a Qlib workflow that trains an XGBoost model on China A-share data for the CSI 300 universe, using Alpha360 features. Its label is a forward close-to-close return over the next interval. Training and validation use earlier…

The document explains how to define and run a Qlib research workflow through a YAML configuration and the `qrun` command. It lays out the main stages: loading and preprocessing market data, training and applying a model, then analyzing forecast signals and…

This configuration defines a Qlib experiment using Alpha158 features for the CSI 300 and the Shanghai 300 index as benchmark. It normalizes features with robust z-scores, fills missing values, filters a selected subset of columns, and applies cross-sectional…

This Chinese-language post describes a short-term equity selection screen based on three conditions: prior-session price amplitude above a threshold, tradable share count at or below a stated limit, and appearance on the previous day’s exchange activity…

The code implements a time-series prediction model paired with a TRA component that can combine predictions across multiple states. A base model, such as an LSTM, produces hidden representations and state-specific predictions. When multiple states are…

This configuration describes a Qlib workflow that trains a PyTorch deep neural network on Alpha158 features for CSI 300 stocks, using the Shanghai 300 index as its benchmark. It defines training, validation, and test periods, drops the VWAP feature, fills…

This configuration specifies a Qlib workflow for training an ADARNN model on CSI 300 equities in the Chinese market. It uses Alpha360 features, robust z-score normalization with outlier clipping, feature filling for missing values, and cross-sectional rank…

This configuration describes a Qlib workflow that trains a CatBoost model on Alpha360 features for China’s CSI 500 universe. Its label is a forward close-to-close return, normalized cross-sectionally after missing labels are dropped. The data is divided into…

The document explains how Qlib serializes objects such as data handlers, datasets, processors, and models to disk using pickle-compatible formats. A Serializable object saves public attributes by default, with options to configure what is included or select…

This document defines an abstract data-formatting interface for experiments using the Temporal Fusion Transformer (TFT). Dataset-specific formatters are expected to define column names and roles, fit scalers, transform features, reverse prediction…

This configuration describes a Qlib workflow for training a PyTorch feedforward neural network on China’s CSI 300 universe. It uses Alpha360 features, robust feature normalization, missing-value filling, and cross-sectional rank normalization of labels. The…

This Qlib configuration defines a daily stock-ranking experiment for the CSI 300 universe, using the CSI 300 index as its benchmark. Its features combine price and volume transformations, including residual and fit measures, intraday range, and rolling…

This note presents a Chinese equity screen that selects stocks with turnover between 3% and 12%, excludes Beijing-listed shares, and applies a price-to-earnings cutoff below 20. The accompanying Python example also filters out names marked as special…

This configuration describes a Chinese equity forecasting experiment using Qlib’s Alpha158 features and an LSTM model. It selects 20 features, applies robust feature normalization and missing-value filling, and ranks labels cross-sectionally. The prediction…

The notebook describes an evaluation workflow for stock-return prediction models, including linear and neural models as well as a Transformer and an approach combining predictors with a learned router. It ranks predictions cross-sectionally each day,…

This Qlib example demonstrates an end-to-end simulation of rolling online model workflows. It initializes Chinese-market data and experiment settings, generates rolling tasks at a configurable step, and connects those tasks to an online manager and a…

This notebook walks through a minimal Qlib reinforcement learning setup, connecting a simulator, state and action interpreters, a reward function, a policy, a dataset, and training and backtest workflows. Its simulator runs for a fixed number of steps,…

This Qlib documentation explains how forecast models produce stock prediction scores and how to train and run a model independently or within an automated workflow. It describes the base model interfaces, including support for fine-tuning, and gives a…

This document introduces two high-frequency trading examples: handling a dataset for reinforcement learning and predicting price trends. It explains that the dataset is represented by a Qlib DatasetH object, which can be serialized to disk and reloaded.…

This configuration defines a Qlib equity forecasting workflow using the CSI 300 universe and the Shanghai CSI 300 index as benchmark. It sets a data window from 2008 through 2020, with training through 2014, validation over 2015–2016, and testing over…

This Qlib example shows an online manager coordinating rolling model training and strategy updates. Its workflow begins by training an initial strategy set, then runs a routine that updates online predictions, prepares new tasks and models, and generates…