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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
Quantpedia
86 documents
TqSdk
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 note outlines a Chinese equity screen using turnover between 3% and 12%, a positive ten-day price gain below 35%, and a revenue-growth condition. The written selection rule compares 2021 revenue with 2018 revenue and requires the ratio to exceed 1.1.…

EquitiesMomentumFactor investingChina markets
SuperMind

This Chinese stock selection note describes a screen combining RSI below 65, a circulating market capitalization between 5 billion and 10 billion yuan, and an “institutional bottom-fishing” condition. It presents the approach as a mix of technical,…

EquitiesTechnical indicatorsFactor investingChina markets
SuperMind

This Chinese-language post describes an equity screen that combines a positive MACD condition and rising DEA with a 2019 dividend ratio above 25%. It applies the rules to constituents of the CSI 300 and gives illustrative selection and portfolio-management…

EquitiesMomentumTechnical indicatorsFactor investing
BigQuant

This short commentary describes pressures facing quantitative investment firms: crowded strategy areas, convergence among approaches, thin profits, and greater barriers to entry for researchers. It argues that widespread use of machine learning can produce…

Machine learningMarket microstructureFactor investingHigh-frequency trading
BigQuant

This article presents a four-stage account of quantitative trading. It describes early strategies built on financial theory, multi-factor stock selection, and linear regression; a later high-frequency phase centered on low-latency hardware and algorithms; an…

Machine learningHigh-frequency tradingFactor investingSentiment
SuperMind

This Chinese equity screen looks for stocks classified under the metaverse theme and applies thresholds to free-float market capitalization and company scale. The article’s initial description specifies free-float capitalization above 10 billion yuan and…

China marketsEquitiesFactor investing
SuperMind

This article proposes a stock screen combining a technical condition with valuation and size filters. It selects equities with an RSI below a stated threshold, positive trailing price-to-earnings ratio, and free-float market capitalization within a specified…

EquitiesChina marketsTechnical indicatorsFactor investing
BigQuant

The document outlines four portfolio approaches: a calendar effect fund of funds (FOF), a performance enhancement FOF, a fund size undervaluation FOF, and a core-satellite ETF portfolio. The calendar strategy selects five active equity funds equally using…

Factor investingPortfolio constructionBacktestingEquities
SuperMind

This post proposes screening Chinese A-share stocks using a daily price-range threshold above one percent and a requirement related to outstanding convertible-bond names, then ranking candidates by average traded value. Its stated rationale combines trading…

EquitiesChina marketsVolatilityFactor investing
BigQuant

This research reviews technology-focused equity indices in China, comparing their historical performance, construction rules, and constituent distributions. It then examines whether patent information can improve index selection. The proposed patent measures…

China marketsEquitiesFactor investingBacktesting
SuperMind

The article describes a screen for companies with market value below 10 billion yuan and assets above 200 million yuan, alongside a daily high-low range greater than 1%. Its expanded version also requires no losses over the previous five years, positive…

EquitiesChina marketsFactor investingRisk management
BigQuant

This report adapts hidden Markov models (HMMs), a technique associated with speech recognition, to rank stocks by the likelihood of an upward move. It assumes that rising and falling stocks follow distinct patterns and models those patterns separately. Six…

EquitiesMachine learningFactor investingBacktesting
BigQuant

This report outlines a method for analyzing open-ended bond fund returns and deriving a residual alpha measure for fund screening. It groups risk exposures into eight factors spanning interest-rate curve movements, credit risk, equity markets, and money…

Fixed incomeFactor investingStatisticsBacktesting
BigQuant

The discussion clarifies the meaning of a factor-crowding value shown in BigQuant’s factor analysis module. The reply says the value is the recent average turnover rate of stocks after they have been grouped by a factor. It is therefore a group-level average…

Factor investingEquitiesStatistics
BigQuant

This research summary presents a framework for timing equity factors whose returns have become less reliable. It reviews indicators such as valuation spreads and pairwise correlations, and examines their relationship with future factor returns. A random…

Factor investingMachine learningEquitiesPortfolio construction
SuperMind

This stock-selection idea screens for companies with market capitalization below a stated threshold, positive profit, and daily price amplitude above one percent, then ranks eligible names by a measure of stock attention. It presents the approach as a way to…

EquitiesChina marketsTechnical indicatorsFactor investing
SuperMind

This document describes a Chinese equity screen combining daily turnover with growth in net profit attributable to the parent company. It selects stocks with turnover between 3% and 12%, profit growth above 20% and up to 100%, and an earnings forecast for…

EquitiesChina marketsFactor investingBacktesting
BigQuant

This practitioner note compares very high-return, moderate-return, and lower-return backtests for short-horizon equity models, focusing on whether their apparent performance persists out of sample. The author reports that aggressive timing filters and models…

EquitiesMachine learningFactor investingBacktesting
BigQuant

This research summary presents factor timing as a risk-control problem rather than solely as a forecast of factor returns. In a multi-factor portfolio, the approach adjusts upper and lower bounds on factor exposures. Those bounds are tied to factor risk,…

Factor investingRisk managementPortfolio constructionBacktesting
SuperMind

This note describes an A-share screening rule combining a daily high-low range threshold, return on equity above 15% in each of the prior five years, and a positive change from the previous close. It presents the criteria as a way to find consistently…

EquitiesFactor investingTechnical indicatorsChina markets
SuperMind

This Chinese stock-selection example combines a technical filter with a basic revenue-growth condition. It selects shares with RSI below 65, a latest high matching the highest level of the last two days, and 2021 revenue more than 1.1 times 2018 revenue. The…

EquitiesChina marketsTechnical indicatorsFactor investing
SuperMind

This Chinese equity selection recipe combines three filters: return on equity above 6% in each of the prior two years, high dividend yield, and net inflows attributed to major market participants. It also excludes stocks that closed at the daily price limit…

EquitiesFactor investingMomentumChina markets
SuperMind

This document proposes a Chinese equity screen combining amplitude above 1, rising lows, and return on equity above 15% for five consecutive years. The technical conditions aim to capture price movement and a strengthening price floor, while the ROE…

EquitiesChina marketsTechnical indicatorsFactor investing
SuperMind

This Chinese equities screening rule selects stocks with turnover between 3% and 12% and year-over-year growth in net profit attributable to shareholders of the parent company above 20% and no more than 100%. It then ranks qualifying stocks by…

EquitiesChina marketsSentimentFactor investing