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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

4,194 documents

BigQuant

This document compares long-only index funds, index-enhanced funds, and quantitative funds in the Chinese public fund market. It notes differences in fees and subscription or redemption efficiency, and frames market, sector, size, and other style exposures…

Multi-assetPortfolio constructionRisk managementFactor investing
SuperMind

This stock screen combines three conditions: prior-day price amplitude above 1%, a prior-day appearance on the market’s top-trading list with buying value greater than selling value, and revenue in 2021 more than 1.1 times its 2018 level. The author presents…

China marketsEquitiesMomentumEvent-driven
BigQuant

This report summary evaluates historical fundamentals and price information as inputs to industry allocation in China. It highlights trailing-twelve-month ROE, single-quarter ROE, and ROE growth as useful historical factors, while finding industry-level…

EquitiesChina marketsFactor investingMomentum
SuperMind

This report summary examines whether Chinese public mutual funds’ past risk-adjusted performance predicts future performance, with fund selection and fund-of-funds investing as the motivation. It evaluates managers using alpha from several asset-pricing…

StatisticsFactor investingMomentumPortfolio construction
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
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 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
BigQuant

This tutorial outlines a preprocessing workflow for equity factor data. It filters out records without an industry classification, fills missing factor values with date and industry group means, and describes clipping outliers using percentile, standard…

EquitiesFactor investingStatisticsMachine learning
SuperMind

This stock screen combines a turnover-rate band of 3% to 12% with an exclusion of Beijing-listed A shares and a parent-company net-profit growth condition. The final rule specifies year-over-year growth, on a trailing-twelve-month basis, above 20% and no…

EquitiesChina marketsFactor investingStatistics
BigQuant

This report describes a multi-factor model for analyzing stock returns and risk in China’s A-share market. Instead of assessing each stock independently, the framework represents exposures and returns through a smaller set of factors. It highlights…

China marketsEquitiesFactor investingRisk management
BigQuant

This Chinese market report examines sector allocation using fund quarterly holdings, institutional ownership, and company earnings expectations. It describes which sectors funds increased or reduced during the first half, compares public fund and northbound…

China marketsEquitiesFactor investingPortfolio construction
SuperMind

The document describes a Chinese equity screening rule that selects stocks with turnover between 3% and 12%, year-over-year net profit growth attributable to parent-company shareholders above 20% and at most 100%, and a share price below 12. It presents the…

EquitiesChina marketsFactor investingRisk management
SuperMind

The article examines how training-window length can affect an AI stock-selection model. It compares longer historical samples with shorter windows using manual rolling training and a held-out backtest period. In the reported experiments, changing the…

Machine learningStatisticsBacktestingFactor investing
SuperMind

The strategy ranks stocks each month using five inputs: trailing-twelve-month price-to-earnings and price-to-sales ratios, price-to-book value, one-month price momentum, and one-month trading value. The author reports that trailing-twelve-month PE and PS…

EquitiesFactor investingMomentumPortfolio construction
SuperMind

This A-share stock screen combines a relative strength index below 65, a historical dividend payout ratio above 25% for 2019, and a ranking by stock popularity. Its stated rationale is to blend a technical filter, market activity, and a dividend-related…

EquitiesChina marketsTechnical indicatorsFactor investing
BigQuant

This brief discussion describes how to display a sector’s price-to-earnings ratio, using banking as an example. It points to a demonstration that builds an industry P/E using an arithmetic mean approach and notes that the calculation can instead use market…

EquitiesStatisticsFactor investing
BigQuant

This article presents a supervised learning-to-rank approach to stock selection. It defines a universe using the constituents of the SSE 50, labels stocks by discretizing their subsequent five-day returns into 20 ordered classes, and trains on data from 2011…

EquitiesMachine learningFactor investingBacktesting
BigQuant

The article examines how global equities, bonds, commodities, and diversified long-short style premia behave across economic growth, inflation, real-rate, volatility, and liquidity regimes. It constructs standardized macro indicators from U.S. economic and…

Multi-assetPortfolio constructionFactor investingRisk management
BigQuant

The document describes a partial-replication method that selects a small set of stocks to track an index by capturing the systematic factors driving its returns. It estimates the number of factors, uses principal components to estimate factor loadings, then…

EquitiesFactor investingPortfolio constructionBacktesting
SuperMind

This document presents a Chinese stock selection idea combining three signals: today’s increase in holdings above 5%, the prior day’s price above its 250-day moving average, and a 2019 dividend payout ratio above 25%. It frames these as indicators of capital…

China marketsEquitiesMomentumTechnical indicators
BigQuant

This article adapts a machine-learning value strategy to Chinese A-shares. It trains an XGBoost regression model on accounting and company characteristics to forecast future price-to-book ratios. The gap between forecast and current price-to-book, scaled by…

EquitiesMachine learningFactor investingBacktesting
BigQuant

This Chinese-language post reviews several strategy ideas in the context of a reported shift from leading stocks toward reversal patterns. It describes a multi-factor approach that looks for rebound or reversal behavior after attention-grabbing stocks pull…

China marketsEquitiesMean reversionMachine learning
BigQuant

This study asks whether cash flow measures predict stock returns better than conventional profitability ratios. It constructs a direct cash flow template that groups operating receipts and payments and separates them from financing, taxes, investment, and…

EquitiesFactor investingStatisticsPortfolio construction