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.
191 documents
This reference explains how the Nautilus trading framework models a listed share or ETF as an equity instrument. It describes required identifiers, venue symbol, quote currency, price precision and increment, timestamps, and optional metadata such as lot…
This reference explains how a bar represents open, high, low, close, and volume data for a specified bar type. A venue or provider may supply bars, or a trading system may build them from quote ticks, trade ticks, or smaller bars. Bar type carries…
This indicator extends a price channel with two intermediate levels, crossover signals, and optional stop-loss and take-profit markers. It defines five channel levels: the high and low boundaries, the midpoint, and two intermediate levels positioned between…
This overview explains how NautilusTrader simulates strategies against historical data. A backtest engine processes a historical data stream through components that are also used in live trading, including portfolios, strategies, execution algorithms, and…
This reference explains an order-rejection event in an execution system. A rejection marks an order as terminal, updates the order and cache, and is published to the message bus for handlers such as a strategy’s rejection callback. The event commonly follows…
This design document explains how NautilusTrader captures state-changing messages in a durable, ordered event log. Each run has its own sequence numbers, entries, and manifest; the log can be inspected, verified, or replayed to rebuild cache state. Captured…
This documentation explains platform support for listed, crypto, spread, and binary options, including differences in their metadata and identifiers. It describes subscribing to venue-provided Greeks either for an individual contract or for a series-level…
The document explains how a trading system represents instruments across spot assets, futures, options, swaps, CFDs, betting markets, and synthetic instruments. Each instrument has a unique symbol-and-venue identity, while its definition carries details such…
This guide explains how an execution system applies order events, interprets command outcomes, retries requests, persists state, and reconciles local orders with venue reports. It distinguishes definitive local failures, confirmed venue results, and unknown…
This technical reference explains how a synthetic instrument represents a locally calculated price derived from other instruments. Formulas can express averages, spreads, baskets, or ratios; the instrument is assigned a synthetic venue and uses specified…
The document explains an order-canceled event in an execution system: it records an order entering the terminal canceled state, updates the order and cache, and is published to the message bus. Cancellation events may originate from a venue, a simulated…
This guide explains deterministic simulation testing for a concurrent trading system. It describes how a seed-controlled runtime can make task scheduling, timer events, random draws, and channel delivery repeatable, allowing a failure to be replayed and…
This example configures a live-node application to run a Bollinger Band mean-reversion strategy against the Architect AX sandbox on a EUR/USD perpetual instrument, using one-minute midpoint bars. It sets a Bollinger period of 20 with a two-standard-deviation…
This tutorial demonstrates a component-level backtest workflow using NautilusTrader. It loads historical Binance ETH/USDT trade ticks, configures a simulated spot venue with a cash account and maker-taker fees, and aggregates ticks into bars. A strategy…
This guide explains how NautilusTrader stores and accesses market data through a Parquet catalog backed by a Rust storage layer. It covers local and cloud storage, timestamp precision, compression choices, file organization, typed data queries, and…
This documentation describes an order event raised when a trading venue refuses a cancellation request. The execution engine applies the event to the order, updates the cache, and publishes it through the message bus. A typical state change moves an order…
This document explains how NautilusTrader builds, publishes, and verifies release artifacts across Python packages, Rust crates, Docker images, and GitHub Releases. Its release process anchors package integrity to a draft GitHub release: artifacts are…
This document explains how NautilusTrader’s shared network clients add trading-system behavior to HTTP, WebSocket, and raw TCP transports. It covers quota sharing, proxy selection, connection reuse, retries, response limits, streaming deadlines, and…
A stop-limit order waits for a specified trigger price, then submits a limit order at the chosen limit. This combines a conditional trigger with control over the worst acceptable execution price, making it useful for price-protected exits or breakout…
This example configures a backtest for a mean-reversion strategy on an AUDUSD perpetual contract. It feeds quote data into a backtest engine, forms one-minute midpoint bars, and instantiates a strategy configured with Bollinger Bands and RSI. The listed…
This tutorial explains a two-input market-making setup for a Lighter perpetual linked to Nvidia shares. The Lighter order book supplies the price anchor, while Databento US equity top-of-book quotes provide a normalized signal: the equity mid is compared…
This example demonstrates how a backtest engine can model automatic liquidation on a margin account holding an inverse Bitcoin perpetual. It configures a simulated venue with liquidation enabled, starts with one BTC, and submits a market buy for 10,000,000…
This reference explains how NautilusTrader connects to exchanges, brokerages, and data providers through modular adapters. It lists supported integrations and their categories and stability labels, then outlines the common functions these adapters are…
This documentation explains how to build Nautilus trading systems in Rust or Python. The Rust path supports actors, strategies, data and execution engines, risk management, backtesting, portfolios, and live trading; Python components can run on the shared…