Skip to content

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

4,194 documents

SuperMind

This Chinese equity screening example selects stocks using daily amplitude, a measure labeled today's controlling share change, and circulating market capitalization. It then expands the screen with positive price-to-earnings below a stated ceiling and a…

EquitiesChina marketsTechnical indicatorsFactor investing
BigQuant

This forum post asks how to migrate several legacy factors from an earlier version of a quantitative research platform into a newer one. The expressions cover an industry-grouped beta adjustment, a rolling correlation between log volume and absolute returns,…

EquitiesFactor investingStatistics
BigQuant

This research summary explains how alpha factors can be represented by factor-mimicking portfolios (FMPs) under a stock covariance model, and how linear combinations of factors correspond to combinations of their mimicking portfolios. It frames factor…

EquitiesFactor investingPortfolio constructionStatistics
SuperMind

This stock-selection post screens equities in the metaverse theme, requiring a positive price-to-earnings ratio and a price-change condition below six percent at 9:25. It presents the sector filter as a way to focus on a popular theme and positive P/E as a…

EquitiesChina marketsFactor investing
SuperMind

This Chinese equity-screening proposal ranks companies by capital-flow strength and combines that ranking with a market-cap limit, a no-loss condition, and a dividend payout ratio above 25% for 2019. The discussion interprets stronger capital flow as…

EquitiesFactor investingChina marketsRisk management
SuperMind

This post describes an equity screening approach that combines a minimum daily high-to-low range, consistently high return on equity over five years, and weekly MACD above its zero line. The rationale is to pair a price movement filter with a profitability…

EquitiesFactor investingMomentumTechnical indicators
BigQuant

This research report studies portfolio construction around a weekly stock-selection model called AlphaNet, built from price and volume data. It examines several portfolio optimization approaches intended to accommodate different risk and return objectives.…

EquitiesFactor investingPortfolio constructionRisk management
BigQuant

This research summary proposes a stock illiquidity factor based on the shortest path through intraday candlestick data. It aims to improve on a conventional proxy that relates price returns to trading volume, which the authors say can be unreliable during…

EquitiesFactor investingHigh-frequency tradingMarket microstructure
BigQuant

This research summary reviews the low-volatility equity factor across overseas markets and mainland Chinese shares. It describes the behavioral explanation that highly volatile stocks may be more vulnerable to speculation and rich valuations, while…

EquitiesChina marketsVolatilityFactor investing
SuperMind

The document describes an A-share stock screen that combines price amplitude, an outstanding convertible-bond name field, and valuation filters. It first discusses positive price-to-earnings as a profitability condition, then revises the proposed final…

EquitiesChina marketsFactor investingTechnical indicators
SuperMind

This stock-selection screen combines a technical condition with an earnings-growth filter. It selects Chinese A-shares whose 14-period RSI is below 65 and whose year-over-year growth in net profit attributable to parent shareholders is above 20% and no more…

China marketsEquitiesTechnical indicatorsMomentum
SuperMind

This post describes a Chinese equity selection rule that ranks stocks using a proxy for capital participation: yesterday’s turnover multiplied by the ratio of today’s auction volume to yesterday’s volume. It proposes keeping values within a stated band and…

EquitiesChina marketsExecutionFactor investing
SuperMind

This post outlines a stock screen that selects shares with a price range above a threshold, excludes Beijing-listed A-shares, and requires a positive price-to-earnings ratio. It presents the screen as a way to combine price movement with a basic valuation…

EquitiesChina marketsTechnical indicatorsFactor investing
BigQuant

This article explains the theoretical link between beta pricing models, including the Fama-French three-factor model, and stochastic discount factors (SDFs). It distinguishes absolute pricing, which connects asset values to macroeconomic risks, from relative…

Factor investingEquitiesStatistics
SuperMind

This Chinese equity selection approach combines a technical filter with a fundamental growth condition. It looks for stocks with MACD above zero and a rising DEA line, then requires revenue in 2021 to exceed its 2018 level by more than ten percent. The…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

The post describes a Chinese equity screen combining a recent increase in fund holdings, a minimum scale threshold, and revenue growth from 2018 to 2021. It presents the revenue ratio threshold as evidence of business growth and treats rising fund…

EquitiesChina marketsMomentumFactor investing
Amberdata research

This document describes a Chinese A-share stock screen combining a technical condition, an earnings-growth filter, and price trend checks. The initial version selects stocks with RSI below 65, year-over-year growth in net profit attributable to…

China marketsEquitiesTechnical indicatorsFactor investing
BigQuant

This review summarizes research testing whether small-cap stocks continued to outperform large caps across calendar effects in US equities. The underlying study forms portfolios from the smallest and largest size groups and examines the return spread around…

EquitiesFactor investingStatisticsUS markets
vn.py community

The document outlines a training program on cross-sectional multi-factor strategies, also described as alpha strategies. Its curriculum spans factor data preparation, supervised learning, model evaluation and interpretation, portfolio construction, and…

Machine learningFactor investingPortfolio constructionBacktesting
BigQuant

This report summarizes stock-selection results for several machine-learning models across broad Chinese equity universes and constituent groups tied to the CSI 300, CSI 500, and CSI 800. It compares weekly, recent-month, and 2018-to-date excess returns, and…

China marketsEquitiesMachine learningFactor investing
BigQuant

This brief forum entry addresses an SQL error encountered while trying to neutralize a rolling turnover measure against total market capitalization. It points readers toward a regression-residual function as an alternative to a direct neutralization call.…

EquitiesFactor investingStatistics
BigQuant

This brief Q&A explains why neutralizing individual factors for market capitalization does not guarantee that a trained stock-selection model will have no size exposure. After neutralization, linear groupings of the factor may contain both large- and…

EquitiesFactor investingMachine learningRisk management
BigQuant

This Chinese research note proposes using capital flow ratios within style-factor portfolios to guide style rotation. It argues that raw inflow and outflow totals mostly move alongside prices, while flows measured against trading value or by…

EquitiesFactor investingChina marketsBacktesting