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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

22,592 documents

SuperMind

This post outlines a stock screen that combines a daily price-range threshold with a rising DEA signal, while applying geographic exclusions. It then suggests expanding the screen with valuation measures such as price-to-earnings and price-to-book ratios,…

EquitiesTechnical indicatorsMomentumChina markets
BigQuant

This article contrasts retail investors' execution environment with that of quantitative firms. It describes exchange co-location and direct connectivity as ways to reduce signal and order latency, then discusses how automated systems may react quickly to…

EquitiesHigh-frequency tradingExecutionMarket microstructure
SuperMind

This post describes a historical A-share stock screen using an RSI reading below 65, a price range greater than 1%, and a 2021 date filter. It presents RSI as a gauge of price strength and overbought or oversold conditions, while the range criterion is…

China marketsEquitiesTechnical indicatorsBacktesting
BigQuant

The article discusses two reported measures intended to reduce speed advantages for quantitative firms in China’s A-share market: removing servers located inside exchange facilities and adding latency equivalent to a stated 200-kilometer separation. It…

China marketsEquitiesHigh-frequency tradingExecution
SuperMind

This stock-selection example screens Shenzhen main-board shares associated with the metaverse theme. Its stated criteria require price above the five-day moving average, positive price-to-earnings and price-to-book ratios, and upper bounds of 29.01 and 3.11…

EquitiesChina marketsTechnical indicatorsMean reversion
BigQuant

This 2022 overview describes Hong Kong as a base for international and Chinese quantitative asset managers and as a channel for overseas investors seeking exposure to mainland China. It cites hiring and regional-office examples involving Citadel and Two…

Multi-assetChina marketsFuturesEquities
BigQuant

The report describes stock-return prediction with machine-learning models built for short, medium, and long forecast horizons. It groups selection factors according to their information coefficients at different horizons, reflecting the idea that factor…

EquitiesMachine learningFactor investingBacktesting
SuperMind

This stock screen combines three conditions: membership in the metaverse industry, a 2021 listing year, and an opening price near the ten-day moving average of closing prices. The accompanying example describes both a crossover-style technical condition and…

EquitiesChina marketsTechnical indicators
BigQuant

This report outlines a first step in building a multifactor model: choosing which factors to combine. It proposes screening combinations using three criteria: how strongly a factor differs from benchmark exposure, how correlated the factors are with each…

Factor investingEquitiesPortfolio constructionStatistics
SuperMind

This Chinese equity screen first limits candidates to the metaverse theme, then requires the opening price at 9:25 to be less than six percent above the previous close. It also looks for the weekly five-period moving average to cross above the ten-period…

China marketsEquitiesTechnical indicatorsMomentum
SuperMind

This note describes a short-term screen for Chinese equities using three conditions: daily high-low amplitude above one percent, a closing price below 20, and a prior-day close that was not near the stated upper limit threshold. It presents the rules as a…

China marketsEquitiesTechnical indicatorsVolatility
BigQuant

This research summary outlines three refinements to genetic programming for finding stock-selection factors: fitness measures based on mutual information and long-only excess return, ways to transform nonlinear factors, and validation procedures intended to…

EquitiesFactor investingMachine learningStatistics
SuperMind

This stock-selection rule combines an amplitude threshold, a weekly moving-average crossover condition, and membership in the beverage and alcohol import-export industry. The article presents the amplitude filter as a way to constrain price movement, the…

EquitiesTechnical indicatorsMomentumChina markets
SuperMind

This stock screen combines three conditions: membership in the metaverse theme, a positive institutional-flow measure, and MACD above zero. The document frames theme membership as a way to focus on a popular sector, institutional activity as a directional…

EquitiesMomentumTechnical indicatorsChina markets
MQL5 code base

Internal Strength is an oscillator intended to show where the close sits within the day’s high-low range. In its HLC mode, it calculates the close’s position as a fraction of the range, using the high, low, and close. In its RSI mode, it applies RSI to that…

Technical indicatorsEquities
SuperMind

This Chinese A-share screening example combines three conditions: a 14-period RSI below 65, market capitalization of at least 200 million, and a 10-day price gain above zero but below 35%. It describes the screen in plain language and provides sample formula…

EquitiesTechnical indicatorsMomentumChina markets
SuperMind

The article proposes screening Chinese stocks using three conditions: daily amplitude above 1%, at least two limit-up events within a 500-day lookback, and prior-day auction turnover above 0.26. It supplies indicator expressions and a Python-style example…

China marketsEquitiesTechnical indicatorsMomentum
BigQuant

The article examines China’s A-share T+1 rule, which generally prevents investors from selling shares on the same day they buy them. It presents four arguments in the debate: the rule may curb impulsive retail trading, constrain some forms of repeated…

China marketsEquitiesMarket microstructureExecution
SuperMind

This Chinese stock screen combines three conditions: amplitude above 1, a shortening negative MACD histogram on a 15-minute interval, and exclusion of companies classified as science and technology innovation listings. The MACD condition is intended to find…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

The document describes a Chinese stock selection rule combining price amplitude above 1, tradable shares no greater than 5.5 billion, and exclusion of stocks that hit the daily upper price limit on the previous day. Its rationale is to seek stocks with…

EquitiesChina marketsVolatilityTechnical indicators
SuperMind

This community post describes a China A-share stock screen that combines recent price behavior with valuation and listing filters. The stated criteria include Shenzhen main-board stocks with positive price-to-earnings and price-to-book ratios below specified…

EquitiesChina marketsMomentumTechnical indicators
BigQuant

This report studies the accuracy of analysts’ consensus earnings-per-share forecasts for China A-shares and develops forecast-bias signals for stock selection. Its full-sample statistics indicate substantial errors and an overall optimistic bias. An…

EquitiesFactor investingEvent-drivenStatistics
BigQuant

This study evaluates 19 factors derived from sell-side analyst consensus data, including forecasts of financial measures, analyst ratings, and attention. It tests the factors in several Chinese equity universes and across industries. For financial forecast…

EquitiesFactor investingStatisticsChina markets
SuperMind

This stock screen combines three technical conditions: daily high-low amplitude above a threshold, MACD above the zero line, and a rising, ordered set of short- to longer-term moving averages. The intended interpretation is that price movement indicates…

EquitiesChina marketsTechnical indicatorsMomentum