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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

3,481 documents

BigQuant

This report introduces environmental, social, and governance investing and outlines six approaches, including exclusions, positive screening, ESG integration, thematic investing, and shareholder engagement. It compares MSCI and Thomson Reuters rating methods…

EquitiesFactor investingPortfolio constructionChina markets
BigQuant

This report overview describes how artificial intelligence may be applied across investment management, including portfolio research, investment decisions, trading, risk control, product development, and client marketing. It highlights information…

Machine learningMulti-assetRisk managementExecution
BigQuant

This weekly report surveys Chinese equity-selection factors built from intraday trading behavior. Examples include return skewness, downside-volatility share, buying interest and large-order net buying after the open, late-session trading share, average…

EquitiesChina marketsHigh-frequency tradingFactor investing
BigQuant

This document describes an Expert Advisor built around the Adaptive Cyber Cycle indicator. It generates a signal when the indicator crosses its signal line at a bar close. The inputs also allow use of related Adaptive CG Oscillator, CyberCycle, or Adaptive…

ForexTechnical indicatorsBacktesting
BigQuant

This article explains how to simplify factor research by converting many distinct raw values into a smaller number of bins. Its example is a daily return factor across thousands of stocks, where nearby values may be treated as equivalent for exploratory…

EquitiesChina marketsStatisticsFactor investing
BigQuant

This research summary reviews the 2021 commodity futures market and examines whether exchange increases in trading fees affect commodity prices and CTA strategies. It describes a year of broad price gains followed by sharp reversals amid market…

FuturesCommoditiesTrend followingMarket microstructure
BigQuant

The article explains how a backtest can look convincing while relying on conditions that may not hold in live markets. It examines three assumptions: orders fill immediately at the signal price, market data forms a continuous sequence, and prices from a…

BacktestingExecutionMarket microstructureRisk management
BigQuant

The document outlines a financial forum presentation on the use of artificial intelligence in quantitative investing. Its listed examples include a random forest approach to market timing and hedging, a pattern matching method for industry rotation, and a…

Machine learningPortfolio constructionDerivatives pricing
BigQuant

This introductory tutorial explains machine learning concepts before focusing on linear regression. It defines features and labels, distinguishes supervised from unsupervised learning, and outlines classification, regression, clustering, and parameterized…

Machine learningStatisticsFactor investingBacktesting
BigQuant

This research summary examines whether analyst earnings forecast errors can help select stocks. It reports that realized forecast errors are strongly negatively associated with subsequent returns: stocks with smaller errors performed better in the described…

EquitiesFactor investingStatistics
BigQuant

This document summarizes a documentary series on the development of money and financial systems. Its topics include the origins and social role of credit and debt, the rise of securities markets and sovereign borrowing, insurance and the welfare state,…

Multi-assetFixed incomeEquitiesRisk management
BigQuant

This weekly report presents diagnostics for the ten highest-ranked anonymous factors in a Chinese quantitative research competition. It describes average style exposures estimated with daily multivariate regressions and average industry-level Rank ICs, the…

EquitiesChina marketsFactor investingPortfolio construction
BigQuant

StockRanker is a tree-based learning-to-rank tool for ordering stocks from supplied features. Its listwise approach optimizes the quality of a stock list’s ranking, and the guide connects the method to the RankNet, LambdaRank, and LambdaMART line of work.…

EquitiesMachine learningStatisticsBacktesting
BigQuant

The document addresses a backtest or trading-log puzzle: a buy order is calculated by rounding available cash down to a whole multiple of 100 shares, yet the recorded final purchase quantity is not a multiple of 100. The explanation given is that the system…

EquitiesExecutionBacktesting
BigQuant

The document explains two differences between backtests and simulated or live trading in a notebook-based platform. In the described live modes, the initialization function runs during each trading period, whereas a backtest runs it once. It also says…

BacktestingExecutionRisk management
BigQuant

This update log describes changes to a Chinese equity screening expression. The initial filters included a trailing price-to-earnings ceiling and a minimum listing age. The author then replaces a fixed floating-market-capitalization threshold with a…

EquitiesFactor investingBacktestingChina markets
BigQuant

This literature digest covers two topics. The first evaluates asset-pricing factor models using the maximum squared Sharpe ratio. It describes comparisons among nested models, from CAPM through three-, five-, and six-factor specifications, and non-nested…

Factor investingPortfolio constructionRisk managementEquities
BigQuant

This report summarizes tests of equity factors across industries, covering cash flow, valuation, company size, momentum, liquidity, volatility, beta, and alpha. It compares factor performance using portfolio net value and ranking differences, and discusses…

EquitiesFactor investingStatistics
BigQuant

This review describes a model in which equity factors move through normal conditions, periods of value weakness, and subsequent reversals. The value drawdown may reflect a rally in growth stocks or a collapse in value stocks; reversals then differ according…

Factor investingEquitiesMomentumStatistics
BigQuant

The document discusses the rapid growth of China’s quantitative private fund industry since 2019. It attributes that expansion to growing volumes of data, better data processing, and market inefficiencies that may offer more alpha opportunities than mature…

China marketsEquitiesMulti-assetRisk management
BigQuant

This research summary explains a model in which product-market competition and expected asset returns influence one another. Competition can reduce profit margins, increasing firms’ operating leverage and exposure to systematic shocks. Threats from new…

EquitiesStatisticsFactor investingUS markets
BigQuant

This Chinese-language research summary describes a top-down framework for institutional fund allocation that connects macro asset views with strategy and product selection. It argues that asset allocation recommendations need to flow through to specific…

Multi-assetFactor investingPortfolio constructionRisk management
BigQuant

The document introduces the Alligator technical indicator as three moving-average lines calculated from the midpoint of each bar's high and low. It names the lines Lips, Teeth, and Jaw, and describes each as a moving exponential average with a separate fast,…

Technical indicatorsEquities
BigQuant

This summary of a May 2020 China A-share market-timing report combines sector performance, valuation, market breadth, fund positioning, and an intermediate-term model. It lists the strongest and weakest sectors, notes that valuations fell across several…

China marketsEquitiesTechnical indicatorsStatistics