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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

This project note describes iterative construction and evaluation of equity factors using amount, turnover, price, market capitalization, and price-to-book data. Candidate expressions combine standardized ratios and rolling statistics, including volatility,…

EquitiesFactor investingBacktestingMachine learning
SuperMind

This post describes a Chinese equity screen for stocks in a metaverse industry category. It requires the opening price to be less than 6% above the previous close and at least one session in the prior 25 days when the high exceeded the previous high by more…

EquitiesMomentumTechnical indicatorsChina markets
BigQuant

This post outlines a Chinese equity screening rule combining turnover, a rising DEA condition, and a cap on tradable share count. It selects stocks with turnover between 3% and 12%, a rising signal derived from moving-average differences, and a circulating…

EquitiesTechnical indicatorsChina marketsRisk management
SuperMind

The document surveys leveraged exchange-traded funds, their construction, daily exposure management, costs, and market footprint. Such funds seek a multiple of an underlying index’s return over a short period, using derivatives such as futures or…

EquitiesDerivatives pricingRisk managementExecution
SuperMind

The document describes a daily stock screen for companies classified in a metaverse-related sector. It selects stocks with a positive daily return, a price change below 2.6% and above -5%, and a price-to-earnings ratio below 30. It gives both a combined…

EquitiesMomentum
SuperMind

This document describes a Chinese equity screen that combines three conditions: price amplitude above 1%, a circulating share count no greater than 5.5 billion, and year-over-year growth in parent-attributable net profit above 20% and no more than 100%. It…

EquitiesChina marketsFactor investingVolatility
SuperMind

The document proposes an equity screen requiring turnover between 3% and 12%, a positive product of the day's price change and net large-order flow, and a rising DEA indicator. It frames the turnover band as a participation filter, the signed price-flow…

EquitiesTechnical indicatorsMomentum
SuperMind

The document describes a stock screen combining daily amplitude above 1, a price below 20, and a positive change in the KDJ K value. It presents the KDJ increase as a way to identify upward price momentum, then recommends checking it alongside other…

EquitiesTechnical indicatorsMomentum
SuperMind

This proposed A-share screen targets main-board stocks associated with robotics. Its stated criteria are turnover between 3% and 12% and circulating market value between 5 billion and 10 billion yuan. A sample indicator expression also requires the closing…

EquitiesChina marketsTechnical indicatorsMomentum
BigQuant

This document outlines a simple Chinese A-share strategy that ranks stocks by return on equity and selects the ten highest-scoring candidates. It describes filtering out special-treatment stocks, suspended shares, recently listed companies, and Beijing Stock…

EquitiesFactor investingPortfolio constructionBacktesting
SuperMind

This stock screen combines daily amplitude above one percent with a reversal or engulfing-style candle condition, then excludes stocks classified as belonging to the technology innovation segment. The article provides indicator formula examples and…

China marketsEquitiesTechnical indicatorsBreakout
SuperMind

This Chinese stock selection method combines three signals: daily amplitude above one percent, large-order net inflow in the afternoon, and a rising short-term price floor. The article gives indicator formulas and a Python example intended to screen stocks…

China marketsEquitiesTechnical indicatorsVolatility
SuperMind

The proposed equity screen looks for stocks with turnover between 3% and 12%, seven consecutive declining sessions, and a price pattern described as resembling a rounded base. The article presents formula examples for implementing the conditions and adds a…

EquitiesMean reversionTechnical indicatorsBacktesting
SuperMind

This Chinese equity selection note describes a three-part screen: daily high-low range above a stated threshold, a reversal pattern, and price above its five-day moving average. It frames the moving-average condition as a way to favor stocks in an upward…

EquitiesTechnical indicatorsMomentumChina markets
SuperMind

This note proposes screening Chinese equities for three conditions: membership in the metaverse industry, a rounded price pattern, and a reversal or engulfing-style signal. It suggests the combination may identify active stocks in strong market themes with…

EquitiesTechnical indicatorsMomentumChina markets
SuperMind

The article proposes screening stocks for turnover between 3% and 12%, membership in a beverage and alcohol import-export category, and a prior-day auction turnover measure above 0.26. It presents this as a way to find stocks with recent trading activity and…

EquitiesTechnical indicatorsExecutionChina markets
SuperMind

This short-term stock selection rule looks for equities with a daily price range above 1, a rising and spreading set of moving averages, and positive net buying by large participants during the opening auction. The stated rationale is to find shares showing…

EquitiesMomentumTechnical indicatorsChina markets
BigQuant

The document outlines a broad framework for evaluating equity factors. It combines periodic cross-sectional robust linear regression to estimate factor returns, information coefficients (IC) to measure the relationship between factor exposures and subsequent…

EquitiesFactor investingStatisticsBacktesting
BigQuant

This stock-selection strategy ranks shares using large-order net inflow and relative price performance. Its stated purchase screen looks at the prior 30 trading days, selecting stocks whose net inflow share from very large orders is among the top 5% of the…

EquitiesMomentumStatisticsBacktesting
SuperMind

This post presents a stock screen that combines a short-term trend condition with recent buying activity. It selects stocks whose 20-day moving average is above the 120-day average, whose prior session did not close at the daily price limit, and whose…

EquitiesChina marketsMomentumTechnical indicators
SuperMind

This post outlines a stock selection rule focused on companies associated with the metaverse theme. It requires the 30-day moving average to be rising and a nonempty name for an outstanding convertible bond. The accompanying explanation frames the…

EquitiesChina marketsTechnical indicatorsTrend following
SuperMind

This post proposes an A-share stock screen that combines trading activity, reported earnings growth, and short-term order flow. It selects stocks with turnover between 3% and 12%, parent-company net profit growth between 20% and 100%, and large-order net…

EquitiesChina marketsMomentumSentiment
BigQuant

This research note describes a method for detecting support and resistance lines in Chinese A-share price series. It adapts a local high and low point approach with quantile regression, defining line slopes for continuation patterns such as triangles,…

EquitiesChina marketsTechnical indicatorsBreakout
SuperMind

This proposed Chinese A-share screen selects companies classified in the metaverse industry, with circulating market capitalization at or below the stated 55 hundred million shares threshold, and a listing age greater than a configurable number of years. The…

China marketsEquitiesFactor investingBacktesting