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 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 guide explains a configurable pre-trade risk engine that checks orders before they are sent through a trading API. Rules can be enabled in a JSON settings file and include symbol blacklists and whitelists, order size and value caps, cancellation limits,…
The document explains how an algorithmic execution module accepts order parameters, launches and monitors algorithms, and supports pausing, resuming, or stopping them. It also describes launching jobs from newly created CSV files and tracking their status in…
The document explains how to use a market-depth trading interface for live, manual intraday trading in a single futures contract. After connecting a trading gateway and opening a contract chart, the ladder displays price levels, bid and ask quantities, best…
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 guide explains how to construct, monitor, and trade synthetic spreads in the VeighNa SpreadTrading module. A spread can combine several contract legs using a formula, including pricing legs that are not traded, which supports relationships involving…
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 operational guide explains how to run multi-contract portfolio strategies through VeighNa Elite Trader’s PortfolioStrategy module. It covers loading strategy classes, creating instances with symbols, gateways, and typed parameters, then initializing…
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…
This guide describes an execution module that runs algorithms in a separate process, lets users configure and monitor orders, and supports manual order routing across multiple accounts. Its five examples illustrate different execution behaviors: TWAP divides…
This operational guide explains how to route selected VeighNa Elite Trader logs to a DingTalk group through a custom chat robot. The setup requires creating the robot, enabling signed requests, and entering its token and signing secret in the platform’s…
This guide explains how VeighNa Elite Trader’s CTA template can filter synthetic bars received outside configured trading sessions, preventing out-of-session data from affecting strategy indicators. It describes obtaining a sample filter configuration…
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…
The document explains how VeighNa WebTrader provides browser access to basic manual trading functions. Its architecture separates the strategy trading process from a FastAPI web service. REST requests from the browser are relayed to the trading process…
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 notebook demonstrates a basic command-line workflow for operating a trading engine through a CTP gateway. It loads connection settings, initializes the engine, and connects to the server. The example then queries available contracts, account balances,…
The document explains how to connect VeighNa trading software to Excel through its ExcelRtd module and PyXLL. After installing and configuring the commercial PyXLL add-in, users can enable the module in VeighNa Station or load it in a startup script. The…
This document explains a portfolio management interface for monitoring strategy-level positions, trades, and profit and loss during the trading day. It treats each order source, such as manual trading or a strategy module, as a separate portfolio and…
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…