Biblioteca de conhecimento

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 Qlib documentation describes visual reports for evaluating intraday portfolios and prediction models. Portfolio reports display benchmark and portfolio cumulative returns, returns with and without transaction costs, turnover, drawdowns, and cumulative…

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…

Qlib separates forecasting signals from portfolio construction. A strategy turns prediction scores into trading decisions, while a weight-based base class lets users specify target holdings and delegates order generation to the framework. The documented…

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…

The document explains how Qlib’s tuner searches hyperparameters and combinations of models, trainers, strategies, and data labels. A configuration defines each tuner’s search spaces and evaluation limit, then organizes tuners into a pipeline. Users choose a…

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 Qlib demonstration explains how to reuse a processed data handler across repeated model training runs. It first trains the same configured task more than once without explicitly reusing the handler, then constructs the configured data handler in memory…

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,…

Qlib is introduced as a modular platform for researching quantitative investment strategies with AI and machine learning. Its components are loosely coupled, so parts of the platform can be used independently. The architecture is organized into…

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 example outlines an end-to-end workflow for training and evaluating reinforcement learning agents for order execution. It covers preparing five-minute HS300 data and order files, configuring PPO and OPDS training tasks, saving checkpoints, and running a…

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.…

The document motivates adapting forecasting models to changing market conditions: financial data distributions can shift over time, so models trained on earlier periods may lose predictive strength. It compares two approaches, RR and DDG-DA, using linear and…