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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

MQL5 code base

The document presents a Chinese equity screening rule that combines turnover between 3% and 12%, a rising DEA condition, and a newly formed KDJ golden cross. It frames turnover as a way to focus on moderately active stocks, DEA as a trend filter, and the KDJ…

China marketsEquitiesTechnical indicatorsMomentum
SuperMind

This Chinese stock-selection idea combines three filters: daily amplitude above 1%, a reversal or engulfing-style pattern, and previous-day auction turnover above 0.26. The article treats auction turnover as an indicator of activity during the opening call…

China marketsEquitiesVolatilityTechnical indicators
SuperMind

This screen focuses on stocks in a designated metaverse industry group and applies two moving-average conditions. The article describes selecting stocks whose five-day average price is above the five-day moving average and whose previous close is above the…

China marketsEquitiesTechnical indicatorsMomentum
SuperMind

This Chinese stock screen combines a market-cap ceiling of 100 billion yuan with two short-term activity filters: more than two limit-up sessions in the prior 10 days and a ranking by capital strength. The article presents volume as a proxy for capital…

China marketsEquitiesMomentumTechnical indicators
SuperMind

This Chinese stock-selection post proposes combining three filters: rank stocks by volume ratio and keep the top 100, exclude shares that hit the daily upper price limit the prior day while focusing on stocks that fell, and select stocks whose MACD histogram…

EquitiesTechnical indicatorsMomentumChina markets
SuperMind

This Chinese stock-screening post describes selecting shares with an RSI below 65, bid-side volume greater than ask-side volume, and a concentration measure below 20%. The author presents the combination as a short-term screen using a momentum indicator,…

EquitiesTechnical indicatorsSentimentRisk management
BigQuant

This document explains how to remove ChiNext stocks from both the training and test portions of a visual AI stock-selection strategy on BigQuant. It instructs users to insert a shared “filter market” module before the missing-data handling module in each…

EquitiesMachine learningChina marketsBacktesting
Lumibot

This documentation page catalogs practical Python examples for algorithmic trading, including buy-and-hold, momentum, bracket orders, historical data retrieval, quotes, technical indicators, position handling, persistent strategy state, and logging. It…

EquitiesTechnical indicatorsExecutionBacktesting
BigQuant

The report describes a multifactor stock-selection process that first evaluates and selects robust factors, then combines them into a composite score. It builds separate models for six stock universes and compares direct selection with portfolios that select…

EquitiesChina marketsFactor investingPortfolio construction
SuperMind

This A-share stock screen selects securities after each trading session when MACD is above zero, trailing price-to-earnings is positive, and at least one limit-up move occurred during the recent month. The document presents MACD as a trend-strength filter,…

EquitiesChina marketsTechnical indicatorsMomentum
BigQuant

The document explains how portfolio attribution compares a portfolio with its benchmark and decomposes excess return into drivers. It distinguishes return-based approaches, including Treynor-Mazuy, Henriksson-Merton, and related factor extensions, from…

Risk managementPortfolio constructionFactor investingEquities
BigQuant

This historical overview traces the independent development of the Capital Asset Pricing Model by Jack Treynor, William Sharpe, John Lintner, and Jan Mossin. It connects their different starting points to portfolio selection and corporate finance, and…

EquitiesMulti-assetFactor investingPortfolio construction
SuperMind

This stock screen combines three conditions: daily amplitude above a stated threshold, a weekly five-period moving average crossing above the ten-period average, and a share price below a stated cap. The article presents the price limit as a way to focus on…

EquitiesTechnical indicatorsMomentum
SuperMind

This proposed stock screen selects metaverse-related shares with a 14-period RSI below 65 and greater displayed volume at the best bid than at the best ask. The article frames the RSI threshold as a technical filter and the queue comparison as an order-book…

EquitiesChina marketsTechnical indicatorsMarket microstructure
SuperMind

This proposed Chinese stock screen combines a turnover ratio between 3% and 12%, seven consecutive days of falling prices, and an auction-period price change greater than -2% and below 5%. The article presents these conditions as a way to identify stocks…

EquitiesChina marketsTechnical indicatorsStatistics
SuperMind

This Chinese equity screening proposal combines companies with market value below 10 billion yuan and no reported losses with prior-day actual turnover between 3 and 28 and a ranking by capital-flow strength. The article interprets stronger fund flows as…

EquitiesChina marketsTechnical indicatorsMarket microstructure
BigQuant

This opinion piece argues that retail traders should be cautious about relying on remembered successes and discretionary short-term trading habits as markets become more influenced by institutions and quantitative models. Its central practical message is to…

EquitiesChina marketsRisk managementStatistics
SuperMind

This document describes a Chinese equity screen combining daily price amplitude above 1, circulating market value above 10 billion yuan, and a 2019 dividend ratio above 25%. It presents amplitude as a rough sign of trading activity, market value as a size…

EquitiesChina marketsFactor investingTechnical indicators
SuperMind

The document presents a Chinese equity screening rule that selects stocks with turnover between 3% and 12%, three consecutive declining sessions, and a previous-day 9:15 indicative quote at the limit-down level. It frames the combination as a way to identify…

China marketsEquitiesTechnical indicatorsMomentum
SuperMind

This Chinese equity screen combines trading activity, price movement, large-order flow, and exchange-board eligibility. It selects stocks with turnover between 3% and 12%, excludes the STAR Market, and requires the product of the day’s price change and net…

China marketsEquitiesMomentumMarket microstructure
BigQuant

This brief strategy outline proposes a weekly rotation among three of the smallest stocks that meet fundamental quality and growth screens. The listed criteria include sustained net-profit growth over three consecutive years and gross margins above 20%; it…

China marketsEquitiesFactor investingPortfolio construction
SuperMind

This Chinese A-share screening idea starts with metaverse stocks and ranks candidates by popularity. Its initial filter selects stocks with prior-day turnover above 8%; the proposed refinement adds return on equity above 10% and a 20-day average turnover…

China marketsEquitiesFactor investingSentiment
SuperMind

This A-share stock screen selects names with daily amplitude above 1, turnover above 2% and at most 9%, and more than two limit-up sessions in the preceding ten days. The conditions are presented as proxies for volatility, trading activity, and recent market…

EquitiesChina marketsMomentumBreakout
SuperMind

This A-share stock-selection rule combines RSI below 65 with seven consecutive sessions in which the close is below the open, and excludes stocks that hit the price limit on the previous day. The document frames the conditions as a technical screen that…

EquitiesChina marketsTechnical indicatorsMean reversion