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Knowledge library

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.

Quant Q&A
20,364 documents
SuperMind
12,226 documents
OKX Learn
8,431 documents
Strategy library
7,910 documents
MQL5 code base
7,090 documents
BigQuant
3,481 documents
Bitget Academy
3,298 documents
MQL5 articles
3,012 documents
TradingView scripts
1,976 documents
ProRealCode
1,507 documents
Deribit Insights
1,232 documents
Machine Learning for Trading
1,124 documents
arXiv papers
1,033 documents
Amberdata research
766 documents
FMZ forum
682 documents
FMZ digest
662 documents
vn.py community
560 documents
QuantInsti blog
511 documents
Galaxy Research
340 documents
QuantStart
246 documents
Stratmill research code
219 documents
Robot Wealth
195 documents
NautilusTrader
191 documents
Hummingbot docs
181 documents
Paradigm research
175 documents
Lumibot
164 documents
Kraken Learn
163 documents
Quant course library
157 documents
OctoBot
152 documents
Cryptohopper blog
144 documents
Systematic trading blog (Rob Carver)
132 documents
Qlib
116 documents
TqSdk
86 documents
Quantpedia
86 documents
Hyperliquid docs
79 documents
Freqtrade
68 documents
Hudson & Thames
62 documents
Awesome Systematic Trading
61 documents
backtrader
54 documents
vn.py
50 documents
Binance API docs
45 documents
Quantopian lectures
45 documents
FMZ guides
38 documents
pysystemtrade
34 documents
Freqtrade docs
32 documents
quant-trading
31 documents
FinRL
28 documents
Zipline
22 documents
FMZ live strategies
21 documents
Jesse
17 documents
pyfolio
16 documents
Alphalens
14 documents
WonderTrader
14 documents
backtesting.py
11 documents
Technical Analysis
9 documents
QTPyLib
8 documents
QuantRocket
7 documents
Lumibot strategies
7 documents
Awesome Quant
1 documents

Search the library

144 documents

NautilusTrader

The document explains configuration conventions in NautilusTrader, covering typed settings for data and execution clients, engines, and strategies. It distinguishes concrete fields from optional fields, whose absent values can mean disabled behavior, an…

ExecutionRisk management
NautilusTrader

The guide explains how NautilusTrader connects to Bybit for live market data and order execution across spot, linear and inverse contracts, and options. It describes product-specific symbol suffixes, instrument loading, and the differences among mainnet,…

CryptoExecutionMarket microstructureSpot markets
NautilusTrader

This technical reference explains how an order-expiry event is processed in an execution pipeline. The event is applied to the order, updates the cache, and is published on the message bus. It may originate from a venue, a simulated matching engine, or…

ExecutionMarket microstructure
NautilusTrader

This guide describes how NautilusTrader builds and maintains positions from fills. It covers signed exposure, average entry and exit prices, realized and unrealized PnL, commissions, funding adjustments, and closure when net quantity reaches zero. It…

ExecutionRisk managementPosition sizingPerpetual futures
NautilusTrader

This engineering guide explains how to build Rust-native adapters that connect NautilusTrader to exchanges and data providers. It covers venue-specific data and execution clients, configuration and Python exposure through PyO3, plus contracts for…

ExecutionMarket microstructureRisk managementBacktesting
NautilusTrader

This example outlines a staged workflow for obtaining option data through an Interactive Brokers connection. It configures an instrument provider for an underlying futures contract and a put option, then checks whether the gateway or trading workstation is…

OptionsFuturesDerivatives pricingExecution
NautilusTrader

This document explains how a backtest matching engine assigns fill prices across depth books, top-of-book data, and bar-based triggers. Market orders can walk available levels; limit orders use crossed prices when taking liquidity and their limit when…

BacktestingExecutionMarket microstructureRisk management
NautilusTrader

This example describes a live data actor that subscribes to a slice of Bybit BTC options. At startup, it searches cached instruments for unexpired Bybit options, selects the soonest expiry, prefers USDT settlement when available, and constructs the…

CryptoOptionsDerivatives pricingExecution
NautilusTrader

An order book delta represents one incremental change to a book and is used when a venue or data provider sends updates that a trading system must apply locally. The document distinguishes three supported granularities: order-level Level 3 data, price-level…

Market microstructureExecution
NautilusTrader

This documentation explains how an execution algorithm receives a primary order and can break it into spawned orders. Its built-in TWAP implementation divides an order across a configured time horizon and interval, submitting the first slice immediately and…

ExecutionMarket microstructureRisk managementBacktesting
NautilusTrader

This example demonstrates an execution test that streams public Binance spot market data into a local sandbox matching engine. The built-in strategy opens a position with an immediate-or-cancel order, maintains post-only buy and sell limit quotes, then…

ExecutionMarket microstructureSpot marketsRisk management
NautilusTrader

This example configures a strategy that monitors top-of-book order imbalance for a perpetual gold instrument and submits sandbox orders when configured thresholds are met. Its settings specify a maximum trade size, a minimum size for triggering, an imbalance…

Market microstructureExecutionPerpetual futures
NautilusTrader

This example shows how to run an options backtest from a catalog containing option instruments, quote ticks, and Greeks. It subscribes to periodic option-chain snapshots for a chosen series and selects a contract either at a specified strike or near a target…

OptionsDerivatives pricingBacktestingExecution
NautilusTrader

This example configures a live trading framework to connect to Derive’s test environment and exercise its built-in execution tester on an ETH perpetual instrument. At startup, the strategy can open a position using an immediate-or-cancel order and maintain…

ExecutionPerpetual futuresMarket microstructure
NautilusTrader

This quickstart walks through a bar-based exponential moving average crossover strategy in a backtesting engine. The strategy waits for its fast and slow averages to initialize, then buys when the fast average is at or above the slow one and sells when it is…

ForexTrend followingTechnical indicatorsBacktesting
NautilusTrader

The script runs an EMA-crossover backtest on USD/JPY five-minute bid bars built from one-minute FXCM data. It configures a simulated margin venue, balances, fees, rollover interest, and probabilistic fills, then collects bars and fills from the engine. The…

ForexTechnical indicatorsBacktestingExecution
NautilusTrader

This document describes an adapter for collecting DeFi data from EVM blockchains and making it available through a trading system’s data model. It covers historical and live block feeds, DEX pool discovery, pool event replay, snapshots, and an experimental…

CryptoDeFiExecutionMarket microstructure
NautilusTrader

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…

EquitiesSpot marketsExecution
NautilusTrader

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…

Market microstructureBacktestingExecution
NautilusTrader

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…

BacktestingExecutionMarket microstructureRisk management
NautilusTrader

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…

ExecutionMarket microstructureRisk management
NautilusTrader

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…

ExecutionBacktestingMarket microstructureRisk management
NautilusTrader

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…

Multi-assetRisk managementExecution
NautilusTrader

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…

ExecutionMarket microstructureRisk management