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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
Quantopian lectures
45 documents
Binance API docs
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

14 documents

NautilusTrader

The document explains how a trading system can represent a tokenized asset that tracks an external security, such as a stock or fund traded on a crypto venue. It defines the instrument’s identity, currencies, asset classification, price and quantity…

CryptoEquitiesSpot marketsMarket 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 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…

Market makingEquitiesCryptoPerpetual futures
NautilusTrader

This Chinese-language post describes a stock screen combining three filters: RSI below 65, exclusion of the STAR Market, and a positive return over ten days that remains below 35%. It also gives an illustrative implementation outline using historical prices…

EquitiesChina marketsTechnical indicatorsMomentum
NautilusTrader

This technical guide explains how NautilusTrader’s Interactive Brokers adapter connects to Trader Workstation or IB Gateway for market data, order execution, instrument discovery, and historical requests. It covers socket access, paper and live connection…

ExecutionMarket microstructureEquitiesFutures
NautilusTrader

This example configures a composite market-making strategy that uses NVDA equity quotes as its signal and quotes an NVDA perpetual contract on Lighter. The settings specify a maximum position and trade size, a half-spread, inventory and signal skew factors,…

Market makingPerpetual futuresEquitiesExecution
NautilusTrader

This example configures a live market-making strategy for an NVDA perpetual contract on Lighter, using NVDA equity quotes from Databento as an external signal. The strategy combines a configured half-spread with inventory and signal skew, limits position…

Market makingPerpetual futuresEquitiesExecution
NautilusTrader

This guide shows how to retrieve historical market data from Databento, save it locally in compressed DBN files, convert it into Nautilus data objects, and store those objects in a Parquet catalog. Its examples cover an E-mini S&P futures order book depth…

FuturesEquitiesMarket microstructureBacktesting
NautilusTrader

This Chinese equity screen looks for stocks with daily price amplitude above 1%, at least one year since listing, and large-order net volume above 0.05 for more than three consecutive days. The rationale is that a minimum level of movement indicates market…

EquitiesChina marketsTechnical indicatorsStatistics
NautilusTrader

The document proposes screening Chinese stocks in the metaverse theme by recent trading activity and company size, then adds a net-profit growth condition. Its final stated rules require the prior day’s turnover to exceed 8%, market capitalization to be at…

EquitiesChina marketsMomentumFactor investing
NautilusTrader

This plotting script illustrates a composite quoting framework for NVDA equity-linked perpetual trading. It uses an external equity mid-price as a directional signal and the perpetual market mid-price as the quote anchor. The quote center shifts with the…

EquitiesPerpetual futuresMarket makingExecution
NautilusTrader

This Chinese-language post outlines a stock screen for companies in the metaverse theme whose prior-day price is above the 250-day moving average and whose price-to-earnings ratio is positive. The stated rationale combines a thematic category, a long-term…

EquitiesChina marketsTechnical indicatorsFactor investing
NautilusTrader

This note outlines a mainland China stock screen requiring RSI below 65, a daily gain above 1%, a main-board listing, and first-level bid volume greater than ask volume. Its stated aim is to combine a technical condition and positive price movement with an…

EquitiesTechnical indicatorsMarket microstructureChina markets
NautilusTrader

This article argues that traders should generally follow the prevailing stock trend instead of automatically taking the opposite side of popular sentiment. It says countertrend buying during a decline can mean facing persistent selling, while selling into an…

EquitiesTrend followingTechnical indicatorsRisk management