Qlib Ai Quantv0.3.2

Investors need to convert high-frequency 1-minute stock data to daily frequency for feature computation and model training. This is triggered when users want to aggregate intraday data to reduce noise and align with daily trading signals.

Extract a specific minute bar each day (e.g., close of first minute) from 1-minute data to create a daily time series. Useful for trading strategies that rely on opening or closing auction patterns.

Leverage both daily and 15-minute frequency features simultaneously, e.g., daily fundamentals plus intraday momentum, to train Temporal Fusion Transformer (TFT) models. This enhances model accuracy by capturing cross-frequency patterns.

Use the Alpha158 factor library to construct features like alpha_001 to alpha_158, then backtest a strategy on A-share stocks. The pipeline integrates data fetch, factor computation, and execution with proper next-bar semantics.

Provide end-to-end development of quantitative trading strategies, from data collection and feature engineering to backtesting and optimization. Clients pay for strategy development and customization.

Offer access to a cloud-based platform that automates multi-frequency data processing, factor computation, and model training. Users pay a monthly subscription for usage.

Allow users to download or deploy pre-built models (e.g., TFT trained on Alpha158) for a fee. The platform handles hosting and execution, while creators earn royalties.