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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

22,592 documents

BigQuant

The article argues that algorithmic trading has changed the experience of retail equity investors. It describes quant systems as data-driven and fast, and claims their trading can contribute to index moves that diverge from the performance of individual…

EquitiesMarket microstructureRisk managementChina markets
SuperMind

This post describes a simple Chinese A-share stock screen: retain shares with turnover rates between 3% and 12%, exclude Beijing-listed stocks, and require return on equity to exceed 15% for five consecutive years. The stated rationale is to favor companies…

EquitiesChina marketsFactor investingRisk management
BigQuant

The report outlines a framework for timing equity factors whose performance has become less stable. It first examines indicators such as valuation spreads and pairwise correlations, testing their relationship with future factor returns. It then uses a random…

EquitiesFactor investingMachine learningPortfolio construction
SuperMind

This post describes a Chinese equity screen that combines a 14-period RSI below 65 with membership in the beverage and alcohol import-export industry, Shenzhen main-board listing, and specified price-to-earnings and price-to-book ranges. It presents the…

EquitiesChina marketsTechnical indicatorsFactor investing
SuperMind

This post describes an equity screen restricted to the metaverse industry. It selects companies with simultaneous bullish crossovers in MACD, the five-day and ten-day moving averages, and the five-day and twenty-day moving averages. It further requires…

EquitiesChina marketsMomentumTechnical indicators
BigQuant

This assignment response translates two discretionary stock approaches into rule-based proposals. One combines recent institutional fund inflows, positive company earnings, improving per-share profit, elevated trading volume, and a price ceiling relative to…

EquitiesMomentumTechnical indicatorsBacktesting
SuperMind

This Chinese stock-screening post proposes selecting shares with daily amplitude above 1, more than one year since listing, and seven consecutive down sessions. The author frames large amplitude as a sign of active trading and the extended decline as a…

EquitiesMean reversionTechnical indicatorsRisk management
BigQuant

The document summarizes a 2020 study on whether investor attention measured through Baidu search activity can help forecast volatility in Chinese equities. The researchers compare a baseline GARCH model with an expanded version that includes search volumes…

EquitiesStatisticsSentimentChina markets
BigQuant

This presentation interprets findings from a 2021 survey of Chinese quantitative investment institutions and discusses how the sector was developing at that time. It covers strategy mixes, research organization, talent, artificial intelligence, alternative…

EquitiesFuturesMachine learningFactor investing
BigQuant

This meetup Q&A contrasts futures CTA strategies, often framed around trend following, with equity multi-factor strategies that combine signals such as value, momentum, quality, and size. It outlines a Bollinger Band example for futures: calculate a…

FuturesEquitiesTrend followingTechnical indicators
SuperMind

This document describes a Chinese equity screen that combines a 14-period RSI below 65, market capitalization of at least 200 million, and earnings growth in 2021. It presents the screen as a way to join a technical condition with size and fundamental growth…

EquitiesTechnical indicatorsFactor investingChina markets
SuperMind

The document describes a Chinese stock screen using ranked fund-flow strength, a 2019 dividend ratio above 25%, and a 15-minute MACD histogram whose negative bars are shortening. The MACD condition is presented as a possible sign of improving short-term…

EquitiesMomentumTechnical indicatorsSentiment
SuperMind

This proposed equity screen selects beverage and alcohol companies with turnover between 3% and 12% and a 2019 dividend ratio above 25%. The document supplies example screening formulas and Python logic that combine industry membership, historical dividend…

EquitiesFactor investingChina markets
SuperMind

The document presents a Chinese equity screen combining a daily price range greater than 1%, market capitalization below 10 billion yuan, positive net profits over the latest four quarters, and a share price of at least 18.5 yuan. It frames these conditions…

EquitiesTechnical indicatorsFactor investingChina markets
SuperMind

This stock-selection approach combines a daily turnover band of 3% to 12%, a positive product of price change and large-order net flow, and a Morning Star candlestick signal associated with Kute Intelligent. The document describes the pattern as a way to…

EquitiesTechnical indicatorsSentimentChina markets
SuperMind

The document outlines a proposed screen for stocks associated with the metaverse theme. Its final stated rule selects companies with market value no higher than 10 billion yuan, circulating shares no greater than 5.5 billion, and a daily increase in holdings…

EquitiesChina marketsSentimentRisk management
SuperMind

This document presents an A-share selection rule combining turnover between 3% and 12%, circulating market value between 5 billion and 10 billion yuan, and an intraday range of at least 1% relative to the prior close. It frames turnover and range as measures…

EquitiesTechnical indicatorsVolatilityMarket microstructure
SuperMind

The document describes an A-share screen requiring an intraday high-low range above 1%, circulating market capitalization above 10 billion yuan, and a daily increase above 5%. Although the narrative calls the last condition an increase in holdings, the…

EquitiesMomentumVolatilityRisk management
SuperMind

This proposed daily screen selects stocks in the metaverse sector when KDJ has just formed a bullish crossover and the previous trading day was not a limit-up session. It proposes making the selection before 10 a.m. for trading on the same day. The note…

EquitiesTechnical indicatorsMomentumChina markets
SuperMind

This proposed stock screen combines a daily volatility filter, a price trend signal, and a profitability criterion. It looks for stocks with an intraday range above 1%, a close crossing above a five-period moving average, and return on equity above 15% for…

EquitiesTechnical indicatorsTrend followingFactor investing
SuperMind

This proposed screen targets stocks classified in the metaverse sector that show a bullish MACD crossover and had a dividend ratio above 25% in 2019. It aims to combine a technical signal with a historical shareholder-payout measure. The article describes…

EquitiesTechnical indicatorsMomentumChina markets
SuperMind

This Chinese-equity screening idea combines daily price movement with a concentration filter. It selects stocks whose intraday range exceeds 1%, whose daily decline falls between 4% and 5%, and whose concentration measure is no higher than 20%. The…

EquitiesVolatilityRisk managementChina markets
BigQuant

This research summary examines quantitative stock selection among Chinese technology companies. It highlights research and development spending as a candidate signal and also discusses profitability, earnings growth, valuation, company size, turnover, and…

China marketsEquitiesFactor investingPortfolio construction
SuperMind

This stock screen combines three conditions: daily high-low range exceeds one percent of the low, the J line of a KDJ indicator has just crossed above its D line, and the security has a nonempty name for an outstanding convertible bond. The article provides…

EquitiesChina marketsTechnical indicatorsMomentum