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.
50 documents
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,…
The document describes a software workflow for managing option volatility strategies. It covers model selection for European and American options, choosing a futures or synthetic underlying, monitoring option quotes and Greeks, and calibrating pricing…
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 Python example describes a process manager for a CTA futures strategy using vn.py and the CTP gateway. A parent process checks the clock and launches a child process during configured daytime and overnight trading windows. The child creates the event…
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…
This documentation explains how to load and operate VeighNa’s CTA strategy module. It covers adding strategy instances, selecting contracts and parameters, loading historical data, restoring saved variables, subscribing to market data, and enabling automated…
This reference catalogs calculation functions available in the VeighNa Elite Trader CTA module. It groups common tools by their required inputs and outputs, covering moving averages, momentum and rate-of-change measures, volatility, trend strength and…
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…
This guide describes a user interface for running execution algorithms and explains how to configure an order’s instrument, side, price, quantity, duration, interval, and open-or-close instruction. It focuses on order execution rather than deciding what to…
This operational guide explains how to connect a VeighNa Fusion account to a CTP futures interface and check that contract lookup, market-data subscription, and order functions are working. It outlines prerequisites such as obtaining the correct broker…
The document explains how VeighNa’s PaperAccount module simulates trading against live market data while keeping orders local. It supports limit, market, and stop orders, with configurable slippage for market and stop executions. Orders generally wait for…
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.…
The document explains a plugin-based pre-trade risk module for VeighNa trading systems. Its built-in rules can cap active orders and daily order, cancel, and trade counts; detect repeated identical orders; limit order size or notional value; and validate…
The document explains how VeighNa’s RPC service lets one trading process act as a server for separate client processes. Using ZeroMQ, the server accepts requests such as market-data subscriptions, orders, cancellations, and account queries, while…
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…