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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

4,194 documents

BigQuant

This research summary introduces price-impact bias, derived from five-minute stock order-flow and market data. It compares how similar proportions of aggressive buying and selling move prices. Stocks with stronger upward impact are described as easier to…

EquitiesMarket microstructureMean reversionFactor investing
SuperMind

The post proposes a Chinese equity screen combining daily price movement, recent limit-up activity, and revenue growth. Its stated selection logic looks for amplitude above one, more than two limit-up days within ten days, and 2021 revenue greater than 1.1…

EquitiesChina marketsMomentumFactor investing
SuperMind

This document describes a Chinese stock-screening rule that combines a sector filter, a price condition, and reported earnings growth. It selects companies classified in the metaverse industry when the current open is around the ten-day moving average and…

EquitiesChina marketsTechnical indicatorsFactor investing
SuperMind

This stock-selection idea combines daily amplitude above 1, a positive weekly MACD histogram, and return on equity above 15% for five consecutive years. It pairs a price-activity condition and a technical momentum signal with a measure of sustained…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

This Chinese stock-screening post combines price activity, a positive weekly MACD reading, and a measure relating the prior day’s turnover to current auction volume relative to prior volume. Its final proposed screen adds valuation and growth filters: a…

China marketsEquitiesTechnical indicatorsMomentum
SuperMind

The document describes an A-share stock screen combining three filters: membership in the metaverse theme, a closing price above the 250-day moving average, and specified upper bounds for price-to-earnings and price-to-book ratios. It also limits the…

EquitiesChina marketsTechnical indicatorsFactor investing
SuperMind

This post describes a Chinese equity screening rule combining a capital-interest measure, positive price-to-earnings ratio, and a revenue comparison. Capital interest is represented by indicators such as turnover rate or volume ratio, with stocks ranked from…

EquitiesChina marketsFactor investingStatistics
SuperMind

This Chinese A-share screening proposal combines three conditions: an intraday range greater than one, return on equity above 15% in each of five consecutive years, and a stated daily maximum decline between 4% and 5%. The explanation interprets the range as…

EquitiesChina marketsFactor investingMean reversion
SuperMind

This stock-selection rule screens for turnover between 3% and 12%, year-over-year net profit growth attributable to parent-company shareholders above 20% and no more than 100%, and a 9:25 price change below 6% in absolute value. The article combines a…

China marketsEquitiesFactor investingTechnical indicators
BigQuant

This research summary explains how turnover controls affect portfolio construction and factor evaluation. Portfolio weights change as risk estimates and expected returns change, while portfolio constraints and relative stock performance can impose a minimum…

EquitiesPortfolio constructionFactor investingExecution
SuperMind

This Chinese stock-screening post combines trading activity, recent price weakness, and historical revenue growth. It selects stocks with turnover between 3% and 12%, seven consecutive declining sessions, and 2021 revenue more than 10% above 2018 revenue.…

EquitiesChina marketsMean reversionTechnical indicators
SuperMind

This Chinese equity screen selects stocks associated with the robotics concept, with turnover between 3% and 12%, circulating market value below 10 billion, and a stated scale threshold above 200 million. The post says the added scale filter is intended to…

EquitiesChina marketsFactor investingPortfolio construction
BigQuant

The document summarizes research on whether investor sentiment genuinely helps explain cross-sectional stock return anomalies or appears predictive because it is persistent. Persistent predictors can spuriously forecast returns when expected returns also…

EquitiesSentimentStatisticsFactor investing
BigQuant

This article proposes ListFold, a listwise learning-to-rank loss for factor-based long-short equity strategies. Its central design treats the top and bottom of a predicted stock ranking as equally important, so one coherent ordering can select both long and…

EquitiesChina marketsFactor investingMachine learning
FMZ forum

The article explains how liquid factor portfolios can approximate some hedge fund returns, potentially offering an accessible, lower cost substitute for investments with limited liquidity, high fees, or high entry barriers. It reviews linear multifactor…

Multi-assetFactor investingPortfolio constructionRisk management
SuperMind

This post describes a daily stock screen for Shenzhen Main Board listings. It selects stocks with price amplitude above one, excludes stocks that closed at the daily limit-up on the prior session, and applies positive price-to-earnings and price-to-book…

EquitiesChina marketsTechnical indicatorsFactor investing
BigQuant

This research summary describes high-frequency equity factors constructed from Chinese Level 2 market data, including minute bars, order-book snapshots, order queues, and trade-by-trade records. The proposed signals measure features such as return skewness,…

EquitiesChina marketsHigh-frequency tradingFactor investing
BigQuant

The document introduces a stock factor study based on cumulative net money flow over the previous 20 trading days, scaled by the stock’s market capitalization on the current day. It presents this normalization as a way to compare flows across stocks and…

EquitiesFactor investingStatistics
BigQuant

This strategy combines a valuation screen with price, volume, and trend signals. It favors stocks with low price-to-book ratios, a new 60-day high, short moving-average crossovers in sequence, a daily volume increase relative to the prior day, and a positive…

EquitiesFactor investingMomentumTechnical indicators
BigQuant

This research summary describes a monthly industry rotation method built from price and volume behavior. It groups candidate signals into six categories: momentum, trading volatility, turnover, long-short comparison, price-volume divergence, and alignment…

EquitiesFactor investingMomentumPortfolio construction
SuperMind

The document presents a Chinese stock screening idea combining a MACD condition, money flow strength ranking, and a focus on stocks that performed well during 2021. Its final stated logic adds a market capitalization floor of 10 billion yuan. The…

China marketsEquitiesTechnical indicatorsMomentum
SuperMind

This stock screen selects Shenzhen Main Board shares using a mix of recent price activity, trading liquidity, and valuation. It requires an amplitude above 1, prior-day turnover above 60 million, a price-to-earnings ratio between 0 and 29.01, and a…

EquitiesChina marketsTechnical indicatorsFactor investing
SuperMind

This stock screen combines three filters: an amplitude threshold, a dividend payout ratio above 25% for 2019, and return on equity above 15% for five consecutive years. The accompanying discussion treats the payout ratio and sustained ROE as signs of…

EquitiesFactor investingChina marketsRisk management