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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

BigQuant

This industry-rotation approach identifies turning points in broad or sector indexes to classify market conditions as rising or falling. It then ranks industries by their performance rankings over the prior year within those market states, on the premise…

EquitiesChina marketsMomentumTrend following
SuperMind

This Chinese equity screen combines a minimum daily price range, exclusion of ST-designated stocks, a five-day closing-high condition, and a name-based filter for the metaverse theme. The accompanying example computes a range measure from the day’s high and…

EquitiesChina marketsMomentumTechnical indicators
SuperMind

This A-share stock screen combines a price-amplitude threshold, appearance on the previous day's top-trader list, and simultaneous bullish crossover signals. The examples use moving averages, MACD, and KDJ to represent the technical signals, then intersect…

EquitiesChina marketsTechnical indicatorsMomentum
BigQuant

This review describes a framework for separating portfolio returns into fundamental growth and changes in valuation. It applies the decomposition to value, momentum, quality, and a combined value-quality factor, using long-short portfolios formed from U.S.…

EquitiesFactor investingPortfolio constructionStatistics
SuperMind

This review describes a framework for separating portfolio returns into fundamental growth and changes in valuation. It applies the decomposition to value, momentum, quality, and a combined value-quality factor, using long-short portfolios formed from U.S.…

EquitiesFactor investingPortfolio constructionStatistics
SuperMind

This A-share screening rule combines a daily price-range threshold, the presence of an outstanding convertible bond name, and a weekly bullish-bar condition. The accompanying formula uses a high-low range calculation, a bond-name check, and moving-average…

EquitiesChina marketsTechnical indicatorsVolatility
BigQuant

This guide explains why a submitted end-to-end Transformer example can run out of memory before training or inference begins: the data-loading step materializes minute bars for many stocks across the full date range in one DataFrame. Although the eventual…

Machine learningExecutionStatisticsEquities
SuperMind

This A-share stock selection rule looks for a positive MACD reading, a short-term upward moving-average condition, and more than two limit-up days within a ten-day window. The accompanying explanation interprets positive MACD and rising averages as signs of…

EquitiesMomentumTechnical indicatorsChina markets
SuperMind

The proposed stock screen combines daily price amplitude, a listing-age requirement, and a condition described as evidence of dominant-player control on the prior day. The accompanying rationale treats larger movement as a sign of activity, a longer listing…

EquitiesTechnical indicatorsMomentumRisk management
SuperMind

This Chinese A-share screening proposal combines a positive MACD reading, upward divergence of short moving averages, positive price change, and super-large-order net flow. It also proposes requiring a stock’s circulating share count to meet or exceed its…

EquitiesChina marketsMomentumTechnical indicators
SuperMind

This stock-screening idea selects A-shares with turnover between 3% and 12%, excludes Beijing-listed shares, and looks for the weekly closing price to cross above its 30-week moving average. It presents the rule as a way to identify stocks entering an upward…

EquitiesChina marketsTrend followingTechnical indicators
SuperMind

This screening idea targets main-board Chinese stocks meeting three conditions: a 15-day maximum amplitude above 1, institutional participation above 25% over 15 days, and a declining seven-day moving average. The article presents the combination as a way to…

EquitiesChina marketsTechnical indicatorsSentiment
SuperMind

This stock selection method combines a 14-period RSI below 65 with positive parent-company net profit growth between 20% and 100%, and a current daily low below the previous day’s low. The article describes screening for eligible Shanghai-listed shares,…

EquitiesChina marketsTechnical indicatorsMean reversion
SuperMind

This document describes screening metaverse-related stocks using two auction and opening conditions: positive net buying attributed to large participants during the auction and a 9:25 price rise below 6%. The stated rationale is to focus on a thematic group,…

EquitiesChina marketsMarket microstructureSentiment
SuperMind

This document outlines a historical stock-selection screen using metaverse association, an upward-moving 30-day average, and the year 2021. It describes selecting stocks linked to the metaverse concept whose 30-day moving average is rising, with a date…

EquitiesTechnical indicatorsTrend followingChina markets
SuperMind

The document describes a stock screen combining daily price range, MACD, company size, and annual profitability. It selects shares with an amplitude above 1, MACD above its zero line, market capitalization below 10 billion yuan, and positive net profit. The…

EquitiesTechnical indicatorsVolatilityRisk management
SuperMind

The document describes a Chinese equity screening rule combining daily price range, turnover, and the opening price relative to the 10-day moving average. It seeks stocks with a price range above 1%, turnover above 2% and no more than 9%, and an open near…

EquitiesChina marketsTechnical indicatorsVolatility
SuperMind

The document describes a Chinese stock-selection rule for beverage-related industries. It selects stocks with MACD above zero, membership in beverage manufacturing, beer, or liquor categories, and a 10-day price gain from zero up to but excluding 35 percent.…

China marketsEquitiesTechnical indicatorsMomentum
SuperMind

The document describes a Chinese equity screen combining daily price range, a low KDJ reading, and evidence of a limit-up event within the prior 25 days. It presents the conditions as a way to find volatile, potentially oversold stocks that have recently…

China marketsEquitiesTechnical indicatorsMomentum
BigQuant

This document summarizes three research topics. The first compares earnings announcement returns (EAR) with standardized unexpected earnings (SUE), describing EAR as a measure of the market response to unexpected information in earnings announcements. It…

EquitiesEvent-drivenFactor investingVolatility
SuperMind

This article describes a China A-share screen focused on the metaverse theme. It selects stocks with prior-day turnover above 8%, a weekly five-period moving average crossing above the ten-period average, and a closing price above its five-day average. The…

EquitiesChina marketsMomentumTechnical indicators
SuperMind

This A-share screening strategy first restricts candidates by turnover, circulating market value, and a positive-earnings condition. It then selects companies whose year-over-year growth in net profit attributable to parent-company shareholders falls within…

China marketsEquitiesFactor investingRisk management
SuperMind

This stock-selection idea screens for shares whose daily high-low range exceeds a threshold, whose RSI is below a ceiling, and whose current high matches the highest high over the latest two days. The accompanying explanation frames the recent high as a way…

China marketsEquitiesTechnical indicatorsMomentum
SuperMind

This Chinese-language post describes an equity screen combining turnover, year-over-year net profit growth attributable to parent-company shareholders, and a ratio involving prior turnover, current auction volume, and previous volume. It selects stocks with…

EquitiesChina marketsTechnical indicatorsStatistics