Adapting Stock Forecasts with AI
· 7 min read
8 posts tagged with "quant-dataset"
View All TagsLeveraging Qlib and MLflow for Unified Experiment Tracking
· 5 min read
Introduction
Qlib’s Nested Execution for High-Frequency Trading with AI
· 7 min read
Introduction
A Comprehensive Guide to Qlib’s Portfolio Strategy, TopkDropoutStrategy, and EnhancedIndexingStrategy
· 8 min read
Introduction
Understanding Score IC in Qlib for Enhanced Profit
· 7 min read
Introduction
One of the core ideas in quantitative finance is that model predictions—often called “scores”—can be mapped to expected returns on an instrument. In Qlib, these scores are evaluated using metrics like the Information Coefficient (IC) and Rank IC to show how well the scores predict future returns. Essentially, the higher the score, the more profit the instruments—if your IC is positive and statistically significant, the highest-scored stocks should, on average, outperform the lower-scored ones.
Powering Quant Finance with Qlib’s PyTorch MLP on Alpha360
· 5 min read
Introduction
Qlib is an AI-oriented, open-source platform from Microsoft that simplifies the entire quantitative finance process. By leveraging PyTorch, Qlib can seamlessly integrate modern neural networks—like Multi-Layer Perceptrons (MLPs)—to process large datasets, engineer alpha factors, and run flexible backtests. In this post, we focus on a PyTorch MLP pipeline for Alpha360 data in the US market, examining a single YAML configuration that unifies data ingestion, model training, and performance evaluation.
Harnessing AI for Quantitative Finance with Qlib and LightGBM
· 6 min read
Introduction
In the realm of quantitative finance, machine learning and deep learning are revolutionizing how researchers and traders discover alpha, manage portfolios, and adapt to market shifts. Qlib by Microsoft is a powerful open-source framework that merges AI techniques with end-to-end finance workflows.
This article demonstrates how Qlib automates an AI-driven quant workflow—from data ingestion and feature engineering to model training and backtesting—using a single YAML configuration for a LightGBM model. Specifically, we’ll explore the AI-centric aspects of how qrun orchestrates the entire pipeline and highlight best practices for leveraging advanced ML models in your quantitative strategies.
Adaptive Deep Learning in Quant Finance with Qlib’s PyTorch AdaRNN
· 6 min read
Introduction
AdaRNN is a specialized PyTorch model designed to adaptively learn from non-stationary financial time series—where market distributions evolve over time. Originally proposed in the paper AdaRNN: Adaptive Learning and Forecasting for Time Series, it leverages both GRU layers and transfer-loss techniques to mitigate the effects of distributional shift. This article demonstrates how AdaRNN can be applied within Microsoft’s Qlib—an open-source, AI-oriented platform for quantitative finance.