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.
7 documents
This tutorial explains how to bring market data from external providers or existing CSV files into QTPyLib for strategy backtesting. It outlines supported download routes for daily and intraday bars from Yahoo Finance, Google, and Interactive Brokers, with…
This guide explains how to bring market data from an outside provider into QTPyLib for backtesting. The workflow module’s preparation step converts a data frame into the library’s expected format and can write the result as a CSV file. The example uses…
This reference page catalogs technical indicators and data utilities available in QTPyLib for use with bar data. The built-in list covers volatility and range measures, moving averages, channels, momentum oscillators, returns, volume-related measures, price…
The document explains how QTPyLib’s Blotter connects to Interactive Brokers through TWS or IB Gateway, receives market data, and distributes updates to algorithms through ZeroMQ. It can also store tick and minute data in MySQL for later research and…
This documentation explains the structure of QTPyLib trading algorithms. It describes optional callbacks for startup, quotes, ticks, bars, order-book updates, and fills, and shows how strategies can use these events to inspect instrument history and…
This QTPyLib example illustrates a simple event-driven futures strategy for the S&P E-mini. It counts incoming ticks and acts on every tenth tick. When flat and without a pending order, it randomly chooses a side and submits a one-contract limit order around…
This guide describes QTPyLib, an event-driven framework for building algorithmic strategies with historical testing, paper trading, and live execution through a broker connection. Its architecture separates market data collection, broker operations, strategy…