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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

4,194 documents

SuperMind

This Chinese equity screen combines a technical condition with earnings growth and a price-structure filter. It selects stocks with a 14-period RSI below 65, parent-company net profit growth above 20% and at most 100%, and a rising-low pattern. The post…

EquitiesTechnical indicatorsMomentumFactor investing
BigQuant

The document proposes an equity factor combining turnover with changes in shareholder counts. It first scales shareholder change by the total shareholder count, aiming to reduce the influence of differences in company size. It then applies a signed…

China marketsEquitiesFactor investingMomentum
SuperMind

This Chinese-language post outlines a stock-selection screen for Shenzhen main-board listings. It combines an amplitude threshold above 1, exclusion of ST-designated stocks, selection before 10 a.m., and a “five-part limit-up” condition with…

EquitiesChina marketsBreakoutFactor investing
SuperMind

The proposed screen selects stocks in the metaverse industry that show a rounded-base price pattern and have 2021 revenue more than 1.1 times 2018 revenue. It combines a chart formation with a multi-year revenue comparison, intending to identify companies…

EquitiesChina marketsTechnical indicatorsFactor investing
BigQuant

The report introduces unsupervised learning, focusing on dimensionality reduction and clustering for financial analysis. It describes factor analysis and principal component analysis as ways to compress many stock or bond characteristics into a smaller set…

Machine learningStatisticsFactor investingPortfolio construction
BigQuant

This Chinese-language research digest introduces two overseas studies in quantitative stock selection and timing. The first examines factor timing empirically, asking whether investors can adjust factor exposure over time, and discusses the challenges and…

EquitiesFactor investingStatisticsBacktesting
SuperMind

This document describes an A-share stock screen that combines trading activity, earnings growth, and a short-term price condition. It selects stocks with turnover between 3% and 12%, year-over-year growth in net profit attributable to parent-company…

EquitiesChina marketsTechnical indicatorsFactor investing
BigQuant

This research summary outlines a progression from single-factor IC and IR analysis toward attribution in machine-learning models and causal analysis. It says IC and IR are foundational for traditional multifactor research but do not capture interactions…

Machine learningStatisticsFactor investing
SuperMind

The post outlines an equity screen for companies classified under the metaverse theme. It combines a volume-ratio band with a circulating market-cap range, then proposes limiting the price-to-earnings ratio as an added valuation filter. The stated rationale…

EquitiesTechnical indicatorsFactor investingRisk management
SuperMind

The post proposes a stock screen combining daily price range, five years of return on equity above a threshold, and high turnover on the previous day. It presents the turnover condition as a way to focus on more actively traded stocks, then notes that high…

EquitiesFactor investingRisk management
BigQuant

The document describes monthly stock-selection factors built from news sentiment. Summing a stock’s monthly article sentiment scores combines the direction of coverage with its volume, while averaging scores captures sentiment without the same direct…

EquitiesSentimentFactor investingBacktesting
SuperMind

This post presents an equity screen for companies with positive earnings, market capitalization below its stated ceiling, and parent-company net profit growth within a specified range. It also requires price amplitude above a threshold, using recent price…

EquitiesFactor investingMomentumChina markets
BigQuant

This report examines how corporate tax figures relate to business performance and how tax accounting differs from tax cash flows. It distinguishes tax payable, income tax expense, taxes and surcharges, deferred tax assets or liabilities, and cash taxes paid.…

EquitiesFactor investingChina marketsBacktesting
SuperMind

This post outlines a Chinese equity screening rule combining a daily high–low range above one percent, market capitalization below 10 billion yuan, positive earnings, and exclusion of Beijing-listed shares. The expanded rule specifies positive net profit in…

EquitiesTechnical indicatorsFactor investingRisk management
BigQuant

This report applies dynamic drawdown control to an enhanced equity strategy based on GARP, combining valuation and growth ideas. It describes using relative price strength (RPS) to adapt the drawdown-control signal to the market’s relative strength, with the…

EquitiesChina marketsRisk managementFactor investing
BigQuant

This report outlines a strategic and tactical framework for multi-asset allocation. Strategic allocation favors assets with relative strength over a medium-term horizon, while tactical timing adjusts asset weights according to changing conditions. The…

Multi-assetFactor investingPortfolio constructionRisk management
SuperMind

The document proposes a Chinese equity screening rule combining daily price amplitude above 1%, membership in a robotics concept group, circulating market capitalization below 10 billion yuan, and weekly MACD above zero. It frames the screen as a blend of…

China marketsEquitiesMomentumTechnical indicators
SuperMind

This stock-selection rule screens companies associated with the metaverse theme using a volume-ratio band, a short-term price-change range, and a positive price-to-earnings ratio below a stated ceiling. The post gives both an indicator-style selection…

EquitiesTechnical indicatorsChina marketsFactor investing
SuperMind

This article describes a China-focused stock screen combining liquidity, earnings growth, and company age. It selects stocks with turnover between 3% and 12%, year-over-year net profit growth attributable to parent-company shareholders above 20% and up to…

EquitiesChina marketsFactor investingPosition sizing
SuperMind

The document introduces factor-based stock selection and groups factors into company fundamentals, external conditions, and market behavior. It surveys classic models, including Fama–French, Carhart, and other factor sets, and explains that there is no…

EquitiesFactor investingStatisticsArbitrage
BigQuant

This Chinese-language Q&A covers practical questions about equity factors, model templates, backtest assessment, and trading tools. It names market capitalization, turnover, momentum, quality, valuation, growth, and profitability as commonly used factors,…

EquitiesFactor investingBacktestingRisk management
SuperMind

The report evaluates 16 technical factors previously identified in research as significant and independent, testing their performance in China’s A-share market. Most fare poorly in the study. Five factors—DOWNILLIQ, UPILLIQ, NCSKEW, DUVOL, and IVmonthly—show…

EquitiesFactor investingStatisticsTechnical indicators
BigQuant

This Chinese research summary explores using data-mining models to time factor exposure and rotate among equity factors. It describes random forests conditioned on external market variables to assess when factors may be effective, alongside approaches that…

EquitiesFactor investingMachine learningStatistics
BigQuant

This research note summarizes tests of profitability and earnings-quality factors. It reports examining 14 profitability factors, with two additional measures of return on assets and return on invested capital calculated using EBIT. Factor effectiveness is…

Factor investingEquitiesChina marketsBacktesting