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.
20 documents
This documentation explains how Zipline organizes risk and performance measurements for algorithm simulations. A metrics set defines which values a backtest tracks, and its metrics can report at different frequencies. The default set includes examples such…
This release note describes changes to Zipline, a Python framework for running algorithmic trading systems. It adds command-line and IPython notebook ways to execute algorithms, plus a history function that supplies rolling market data to a strategy. The…
This release note describes changes to Zipline 1.4.0, a quantitative research and backtesting platform. It removes implicit downloads of treasury and benchmark data, replacing benchmark retrieval with user-supplied files or instruments, or an option to run…
This example describes a simple moving-average trend strategy for Apple shares. It calculates 20-period and 40-period exponential moving averages from a 40-day history of daily prices. When the shorter EMA is above the longer one and the algorithm is not…
This tutorial explains Zipline’s event-driven structure for writing and running trading algorithms. A strategy defines an initialization function for persistent state and a handler that runs on each market event, where it can read current or historical…
These release notes describe changes to a quantitative trading and research platform. Pipeline additions include grouped ranking, filters that test conditions across lookback windows, and several technical factors such as Aroon, fast stochastic, Ichimoku,…
This guide explains how Zipline data bundles package pricing history, corporate-action adjustments, and asset metadata for backtesting. It covers listing available bundles, ingesting a data source, choosing a specific ingestion by timestamp, and cleaning up…
This reference catalogs Zipline’s strategy and backtesting interfaces. It covers algorithm setup, market data access, scheduling, asset lookup, order placement and cancellation, and trading controls such as limits on leverage, order count, order size, and…
This small Zipline example selects Apple shares during initialization and configures per-share commission and volume-share slippage. On every data callback, it submits an order for ten shares and records the current share price. The example therefore…
This notebook demonstrates how to use Alphalens to compare a deliberately non-predictive factor with a deliberately predictive one. It uses a universe of large-cap stocks with sector labels and daily opening prices. The baseline factor ranks stocks by their…
This release note describes Zipline changes relevant to building and running quantitative backtests. The main development is broader futures support alongside equities, including futures slippage and commission models, configurable continuous-futures…
This beginner tutorial explains Zipline’s event-driven structure for algorithmic trading simulations. An algorithm defines initialization and per-event data handling functions, using a persistent context to store state and a data object for current market…
The document implements Online Portfolio Moving Average Reversion (OLMAR), a portfolio strategy that adjusts asset weights using relative moving-average prices. For each stock, it divides the window’s average price by the current price, then compares each…
This release note describes changes to Zipline, a Python framework for algorithmic trading. It introduces the history API for retrieving prior bar data, early support for Quantopian-style algorithm scripts, new data sources, and a BMF&Bovespa trading…
These release notes describe Zipline 1.0's simulation redesign and new backtest workflows. Simulations request data as algorithms need it through a portal, while daily or minute timestamps drive the simulation clock. The release also introduces data bundles…
This document introduces Zipline Reloaded, a Python event-driven framework for testing trading algorithms. It describes using historical market data, running a strategy across a date range, and saving performance output for later analysis. The worked example…
This Zipline example runs a daily algorithm over Apple data from 2014 through 2018. At each data point, it places an order for ten shares and records the current Apple price. The setup specifies per-share commissions with a minimum trade cost and…
This release note describes Zipline 0.8.4, a set of updates to an algorithmic trading research and simulation framework. Pipeline gains an earnings calendar, factors for trading returns, average dollar volume, and exponentially weighted averages and…
A trading calendar defines an exchange’s sessions, timezone, opening and closing times, and holiday schedule. Session labels represent trading days rather than precise instants. These details matter when a strategy places orders or evaluates prices: a…
This release note describes changes to Zipline that affect strategy research and backtesting. It adds a daily pre-market callback and more flexible scheduling, including calls tied to market time and early closes. History data can expand as requested, and…