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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 research overview explains how knowledge graphs represent entities and their relationships, then outlines a construction process: gather data, preprocess it, identify entities and links, reconcile information from different sources, form relationship…

EquitiesEvent-drivenFactor investingMachine learning
MQL5 code base

This stock-selection approach combines technical signals with a profitability filter for Chinese A-shares. It looks for positive net profit growth of more than 20% and up to 100%, an RSI below 65, and a newly formed KDJ golden cross. The example SQL also…

China marketsEquitiesTechnical indicatorsMomentum
BigQuant

This research note adapts the four phase investment clock to Chinese equities. It describes recovery, overheating, stagflation, and recession as distinct macroeconomic environments, and explains the traditional asset preferences associated with each. Because…

EquitiesChina marketsFactor investingPortfolio construction
BigQuant

The document describes an integrated model for estimating how crowded equity factor strategies may be. It combines five standardized signals: valuation spreads between high and low factor-ranked stocks, short-interest spreads, within-group return…

EquitiesFactor investingRisk managementVolatility
SuperMind

This report summary describes a time-series style attribution study using fund net asset values to examine the sources of returns among Chinese quantitative funds in 2017. It compares active quantitative funds with index-oriented products, reviews changes in…

China marketsEquitiesFactor investingStatistics
BigQuant

This study compares equity portfolios selected on profitability, growth, and cash flow, then tests combinations of these fundamentals. It reports quarterly rebalanced results against the CSI 300 from 2013 through June 2021. Each standalone top-50 factor…

EquitiesFactor investingPortfolio constructionBacktesting
BigQuant

This brief project note describes using small market capitalization as a factor and training a model to relate that factor to subsequent 10-day returns. It presents an initial quantitative research idea: use a size characteristic as an input and forward…

EquitiesFactor investingMachine learningBacktesting
BigQuant

The report outlines a fundamental framework for selecting Chinese technology and innovation stocks. It first identifies candidate industries through subjective classification or quantitative screening based on lower sensitivity to macroeconomic fluctuations,…

China marketsEquitiesFactor investingStatistics
BigQuant

This research tests whether lagged returns in economically related industries can help predict future industry returns, based on the idea that information and shocks may diffuse across sectors over time. To limit overfitting in a large predictor set, its…

EquitiesChina marketsMachine learningFactor investing
SuperMind

The document outlines a Chinese equity screen combining a daily high–low price range of at least 1, five consecutive years of return on equity above 15%, and a volume ratio between 1.5 and 6. Its rationale is to find consistently profitable companies trading…

EquitiesChina marketsFactor investingTechnical indicators
BigQuant

This Chinese-language research digest summarizes two topics. The first concerns systematic style investing in fixed income: it reports that value, momentum, carry, and defensive premia have shown notable results in both government and corporate bonds,…

Fixed incomeFactor investingMomentumCarry
BigQuant

The article describes selecting Chinese “monster stock” reversal candidates with a changing, multi-factor score. It examines recent profitable picks by industry and a normalized factor, then uses those observations to infer which sectors and factor ranges…

EquitiesChina marketsMachine learningFactor investing
BigQuant

This research summary describes applying XGBoost, a gradient-boosted tree method, to Chinese stock selection and index enhancement. The model uses CART trees and classifies each stock into one of three groups formed by sorting market returns. This turns…

EquitiesChina marketsMachine learningFactor investing
BigQuant

This research summary describes a method for extracting nonlinear relationships between equity style factors and returns. It keeps an interpretable linear factor model as a baseline, then trains machine learning models on the linear model’s residuals. The…

Machine learningFactor investingEquitiesBacktesting
SuperMind

The article introduces K-nearest neighbors (KNN) classification using Euclidean distance: a sample is assigned the class most common among its nearest training examples. It then outlines a stock-selection application in which financial and technical factors…

Machine learningEquitiesChina marketsFactor investing
SuperMind

The document describes a Chinese equity screen that combines a 14-period RSI below 65 with parent-company net profit growth above 20% and at or below 100%, plus a beverage and alcohol industry-related condition. It presents the approach as a blend of…

China marketsEquitiesTechnical indicatorsFactor investing
SuperMind

This stock screen selects companies with turnover between 3% and 12%, a rising DEA condition, market capitalization below 10 billion yuan, and positive past net profit. The accompanying examples express the filters in formula and Python-style screening…

EquitiesChina marketsMomentumTechnical indicators
BigQuant

This document summarizes two discussions of portfolio diversification. The first describes Bridgewater’s explanation of risk parity and its All Weather strategy, including a comparison with traditional portfolios and responses to common criticisms. The…

Portfolio constructionRisk managementMulti-assetFactor investing
BigQuant

This Chinese-language research digest discusses a common source of backtest overfitting: researchers run many experiments, then report only the strategy or factor with the strongest historical result. Selecting a winner from repeated trials can make apparent…

BacktestingStatisticsFactor investing
SuperMind

This Chinese equity screen is evaluated after each trading day and requires a positive MACD reading, a positive price-to-earnings ratio, and a negative MACD reading two trading sessions earlier. The proposed logic uses the earlier negative reading to…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

This Chinese equity screen selects stocks with a daily high-low range of at least one price unit, return on equity above 15% in each of the previous five years, and prior-day trading value above 60 million. The rationale is to combine price movement,…

EquitiesChina marketsFactor investingStatistics
BigQuant

This factor study examines stock selection in Hong Kong equities and contrasts the market with mainland China. It describes Hong Kong as having a more polarized company-size distribution and lower trading activity, then tests market-cap, momentum, growth,…

EquitiesChina marketsMomentumFactor investing
BigQuant

This 2020 weekly note reviews Chinese equity market behavior and considers which investment styles may be relevant during earnings-report season. It combines calendar patterns, market dispersion, fund flows, valuation levels, and macroeconomic events to…

EquitiesChina marketsFactor investingStatistics
SuperMind

This stock screen combines a metaverse classification with high prior-day turnover, Shenzhen main-board listing, valuation caps, and a return-on-equity threshold. The stated final criteria use turnover above 8%, price-to-earnings between zero and 29.01,…

EquitiesChina marketsFactor investingTechnical indicators