Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
8 documents
This guide explains how to enable a trading gateway in VeighNa Station or load one from a startup script, connect through VeighNa Trader, and view account, position, order, trade, and contract information. Gateway settings can be edited in the application or…
This notebook outlines a machine learning workflow for daily CSI 300 constituent stocks. It loads historical bars and changing index membership filters, constructs an Alpha101 dataset, and divides the sample into training, validation, and test periods. The…
This data-preparation example builds a historical dataset for research on the CSI 300 and its constituent stocks. It retrieves the index membership history over a selected date range, converts provider-specific exchange symbols to the format used by the…
This notebook outlines a daily equity research workflow using CSI 300 constituents. It loads historical bars, builds an Alpha158 feature dataset, and defines training, validation, and test periods. The target is a forward VWAP return over a three-day…
This document outlines a workflow for assembling historical data for a CSI 300 research project. It downloads historical constituent information, retrieves the index membership for each trading date, converts vendor symbols into vn.py format, and saves the…
This workflow demonstrates an equity prediction pipeline using CSI 300 constituent data, Alpha158 features, and a multilayer perceptron. It defines training, validation, and test periods, prepares constituent-filtered data, normalizes features using robust…
This workflow demonstrates an end-to-end daily equity modeling process using CSI 300 constituents and vn.py’s AlphaLab tools. It loads constituent histories, builds an Alpha158 dataset, and divides observations into training, validation, and test periods.…
This notebook outlines an end-to-end equity alpha research workflow for CSI 300 constituents. It loads daily constituent data, constructs an Alpha158 dataset, divides observations into training, validation, and test periods, applies missing-label removal and…