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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

SuperMind

This short-term A-share screening method looks for stocks with amplitude above 1, a circulating share count no greater than 5.5 billion, and a volume ratio between 1.5 and 6. The article frames these filters as a way to find stocks with meaningful price…

EquitiesChina marketsVolatilityTechnical indicators
SuperMind

This A-share screen combines daily amplitude above 1 with a positive weekly MACD reading and a listing history longer than one year. The article then adds valuation and profitability constraints: price-to-earnings below 20, price-to-book below 3, and return…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

This post describes a Chinese equity screening idea that combines membership in the metaverse concept with positive net buying by large participants during the opening auction and a prior-day measure of main-fund control. Its stated interpretation requires…

EquitiesChina marketsSentimentFactor investing
BigQuant

This report discusses fixed-amount periodic investing in funds as a way to align contributions with investor cash flows and reduce the pressure to time volatile equity markets. It distinguishes investing an existing balance from investing new contributions…

EquitiesPortfolio constructionBacktestingChina markets
SuperMind

The post describes a simple random stock-selection process: buy ten stocks, hold them for five days, sell the two weakest performers, then randomly choose two replacements and repeat. It frames the strategy as a challenge to expert stock picking and notes…

EquitiesPortfolio constructionBacktesting
Cryptohopper blog

This stock-selection rule first filters for shares with amplitude above one and ranks candidates by the day’s auction amount, keeping the top five. It then selects those whose 15-minute MACD green bars are shortening, using recent indicator values as a proxy…

China marketsEquitiesMomentumTechnical indicators
SuperMind

This stock screen combines three criteria: rank equities by capital strength, require yesterday’s turnover rate to exceed 8%, and select companies whose 2019 dividend payout ratio exceeded 25%. The accompanying explanation treats capital strength as an…

EquitiesChina marketsFactor investing
BigQuant

This event-driven equity strategy buys stocks at the open after financial results are announced, selecting companies whose year-over-year net profit attributable to parent shareholders has grown by more than 30%. Eligible stocks are ranked by growth, and the…

EquitiesEvent-drivenFactor investingBacktesting
SuperMind

This post describes a stock screen combining two signals: an increase in holdings above 5% and moving averages that diverge upward. It interprets the holdings measure as possible recent buying interest and the moving-average pattern as an upward trend. The…

EquitiesTechnical indicatorsMomentumRisk management
SuperMind

This short-term A-share screening idea combines three filters: a prior-session price range above a stated threshold, appearance on the previous day’s trading activity list, and a high ranking in large-order net flow. The post interprets the combination as a…

EquitiesChina marketsMomentumMarket microstructure
SuperMind

This note describes an A-share screen that combines a metaverse industry filter, a stated free-float threshold, and a three-candle pattern described as a morning star. It presents the pattern as a possible reversal setup after a pullback, and suggests adding…

EquitiesChina marketsTechnical indicatorsMean reversion
SuperMind

This proposed Chinese stock screen selects companies classified in the metaverse theme, with prior-day actual turnover between 3% and 28%, and a listing or issuance date in 2021. The post treats the industry classification as a growth-oriented universe…

EquitiesChina marketsTechnical indicatorsRisk management
SuperMind

This proposed stock screen combines three filters: turnover between 3% and 12%, a newly formed KDJ bullish cross, and a positive institutional net-purchase measure. The post frames turnover as a liquidity and activity filter, the KDJ signal as a trend or…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

This proposed Chinese equity screen focuses on stocks classified in the metaverse theme. It combines a price condition involving the five-day moving average with a directional filter: the product of the stock’s daily return and a measure of net large-order…

EquitiesChina marketsMomentumTechnical indicators
SuperMind

The document proposes a stock screen combining intraday amplitude above 1, excluding ST-designated stocks, selection before 10 a.m., a named five-step limit-up method, and floating shares no greater than 5.5 billion. It describes float as a measure related…

EquitiesBreakoutTechnical indicatorsRisk management
OctoBot

The document summarizes a study of Alpha101 factors in China’s A-share market and a method for generating additional price-volume signals with genetic programming. It says the authors assessed existing factors using Rank IC, portfolio stratification, and…

EquitiesFactor investingMachine learningStatistics
SuperMind

The document describes a short-term stock screen combining a 14-period RSI below 65, three consecutive declining sessions, and a daily price change between -5% and 2.6%. It frames the conditions as a way to find weak stocks that may be positioned for a…

EquitiesTechnical indicatorsMean reversionBacktesting
SuperMind

This Chinese equity screening strategy selects stocks with turnover between 3% and 12%, three consecutive declining sessions, and return on equity above 15% for five consecutive years. The article presents the ROE filter as a way to favor firms with…

EquitiesFactor investingTechnical indicatorsRisk management
SuperMind

This document describes a Chinese equity screening rule combining daily amplitude above a threshold, higher successive lows, and upward separation among short, medium, and longer moving averages. Its indicator example compares five-, ten-, and twenty-period…

EquitiesTechnical indicatorsTrend followingRisk management
BigQuant

This report examines whether removing stocks selected by high-frequency multi-factor short portfolios can improve a CSI 300 index-enhancement strategy. It compares three approaches: combine standardized factor scores, use a regression model to forecast…

China marketsEquitiesFactor investingHigh-frequency trading
SuperMind

This stock screening proposal selects shares with turnover between 3% and 12%, seven consecutive days of falling prices, and positive net buying attributed to major market participants on the prior day. The rationale is to look for heavily declining stocks…

China marketsEquitiesMean reversionStatistics
SuperMind

This proposed Chinese stock screen targets companies classified in the metaverse theme, with market capitalization below 10 billion yuan, positive earnings, and an indicated gain below 6% at 9:25. The post frames the theme as a growth opportunity and…

China marketsEquitiesEvent-drivenRisk management
SuperMind

This screening proposal selects Chinese A-shares with turnover between 3% and 12%, excludes Beijing-listed shares, and requires the 9:25 indicated gain to be below 6%. It presents turnover as a way to target trading activity and liquidity while avoiding a…

China marketsEquitiesTechnical indicatorsStatistics