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.
28 documents
The document introduces VeighNa, an open-source Python framework for quantitative trading, with particular attention to its vnpy.alpha module. That module organizes research into feature creation, model training, strategy development, and workflow…
This documentation explains how VeighNa Elite Trader’s option strategy module supports strategy setup, initialization, automated trading, monitoring, and removal. It describes the ContractManager’s role in loading daily contract information and maintaining…
This guide explains the CTA strategy workflow in VeighNa Fusion, from connecting to the trading gateway and creating a strategy instance to configuring parameters, initializing, starting, and stopping it. Each instance has its own target contract, parameter…
This code example outlines a vn.py workflow for backtesting an ATR-RSI strategy on a Chinese equity index futures contract. It configures the instrument, minute interval, historical dates, commissions, slippage, contract size, tick size, and starting…
This guide explains how to use Fusion’s data center to download domestic futures one-minute history into a local database, inspect existing records, update them, and build continuous contracts. Users first load the available instruments, choose an exchange,…
This guide explains how pre-trade controls can block orders that exceed preset limits, helping reduce accidental oversizing, excessive order flow, and other operational errors. It describes common controls for order frequency, reset intervals, single-order…
This guide explains how to use historical backtests and parameter optimization as research checks before deploying a trading strategy. It outlines setup choices such as the instrument and exchange, bar interval, date range, fees, slippage, contract…
The document explains how to use VeighNa’s DataRecorder module to save live market data to a database. Recorded ticks and one-minute bars can later be viewed in DataManager, used for historical backtests, or supplied to trading strategies during live…
The document demonstrates a vn.py workflow for backtesting a statistical arbitrage strategy on a two-leg futures spread. It defines a spread as the price difference between two futures contracts, sets the backtest interval and trading assumptions, loads…
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…
These release notes describe changes across versions of the VeighNa trading framework. For quantitative research, notable updates include a cross-sectional percentile ranking function, revised factor and signal performance analysis, an added VWAP matching…
The document introduces Zhice, a strategy research workspace within VeighNa Fusion. It describes a staged workflow that takes a user's trading idea through clarification of the logic, code drafting and review, backtesting, parameter optimization, and results…
This example demonstrates a portfolio-strategy backtest for a pair trading strategy on two Dalian Commodity Exchange continuous contracts. It configures minute data over a specified historical interval and supplies commission rates, slippage, contract sizes,…
This VeighNa guide compares database choices for storing trading data, including embedded SQLite, relational systems such as MySQL and PostgreSQL, and non-SQL options such as MongoDB, InfluxDB, DolphinDB, Arctic, and LevelDB. It describes their broad storage…
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 document describes a guided process for turning a trading idea into a strategy that can be reviewed and tested. It moves through defining the idea, drafting and confirming its logic, generating and checking code, running a backtest, planning parameter…
This document explains a graphical workflow for researching CTA strategies with historical data. It covers downloading market data, configuring a backtest with instrument details and trading costs, and reviewing equity, drawdown, daily profit and loss, and…
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 guide explains how to use VeighNa’s CTA strategy module to load strategy classes, create instances, initialize them with historical data, and start or stop automated trading. It describes how instances can trade different futures contracts with separate…
This guidance explains why AI-generated strategy logic, code, reviews, backtests, and parameter recommendations should be treated as research aids rather than final trading decisions. Outputs may contain errors or omissions, vary across models or settings,…
This example runs two existing futures strategies independently, using separate instruments, date ranges, trading costs, contract sizes, and capital settings. It then adds their result data frames, removes missing rows, and passes the combined data to a…
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…
The document explains how historical market data supports CTA strategy initialization, backtests, parameter optimization, and research. It outlines importing externally sourced data into a local environment and recommends checking file reliability, timestamp…