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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

The document outlines a systematic stock-selection model built from valuation, profitability, growth, operating-efficiency, and technical factors. It describes a scoring approach: choose candidate factors, test their predictive usefulness, remove redundant…

EquitiesFactor investingStatisticsBacktesting
SuperMind

This Chinese stock-selection note proposes filtering for shares with price amplitude above 1, a daily maximum decline between 4% and 5%, and return on equity above 15% in each of the past five years. It combines a short-term price condition with a multi-year…

EquitiesChina marketsFactor investingRisk management
BigQuant

This Chinese-language manual explains a portfolio optimizer for constructing benchmark-aware equity portfolios. It describes initializing the optimizer with a dated stock universe and benchmark, refreshing factor data for each trading date, and then…

EquitiesPortfolio constructionFactor investingRisk management
BigQuant

This research digest summarizes two studies on equity signals. The first trains a deep neural network on five years of fundamental data to forecast future fundamentals. The summary reports that, relative to a simple forecast assuming current data persist,…

EquitiesMachine learningFactor investingSentiment
BigQuant

This document summarizes a framework for deciding whether and how to combine single-factor smart beta strategies with multifactor portfolios. It represents each candidate strategy as exposures to a small set of investable elementary smart betas—initially…

Factor investingPortfolio constructionRisk managementEquities
BigQuant

This strategy seeks to outperform the CSI 1000 while controlling industry, style, and individual-stock exposures. It proposes combining value, growth, size, quality, volatility or liquidity, and alternative signals. Factors are cleaned for outliers,…

China marketsEquitiesFactor investingPortfolio construction
BigQuant

The document summarizes a study of a composite fundamental factor based on the Piotroski framework and its stock-selection value in the Chinese A-share market. It reports that stocks in the highest factor decile outperformed those in the lowest by an average…

EquitiesChina marketsFactor investingStatistics
SuperMind

The document summarizes a study of a composite fundamental factor based on the Piotroski framework and its stock-selection value in the Chinese A-share market. It reports that stocks in the highest factor decile outperformed those in the lowest by an average…

EquitiesChina marketsFactor investingStatistics
BigQuant

A user asks how to express a specific six-session price pattern in a platform’s daily factor-expression syntax. The condition is that the latest close exceeds each of the previous five closes, while the close from five sessions earlier also exceeds the…

EquitiesTechnical indicatorsFactor investing
SuperMind

This Chinese-language post proposes an equity screen combining a positive MACD condition, positive price-to-earnings ratio, and an industry import-export measure for beverage and alcohol stocks. It interprets MACD above zero as a sign of an uptrend and…

EquitiesChina marketsTechnical indicatorsFactor investing
BigQuant

This forum discussion asks how a visual stock-selection strategy combines multiple factors when no explicit weights are visible, and how the same logic might be represented in SQL. The example lists dividend yield, price-to-book, return on equity, and profit…

EquitiesFactor investingPortfolio construction
BigQuant

This short Chinese-language forum response explains the meaning of delayed entry in a factor test. A delay of N days is described as shifting the start of stock positions by N days from the original entry point. The purpose is to examine what happens when a…

Factor investingBacktestingEquities
BigQuant

This post describes a reproduction of a research report's “moderate risk” factor, framed around information contained in moments of unusually high trading volume. The author says intermediate factors are calculated and stored, while factor analysis is…

EquitiesFactor investingBacktesting
BigQuant

This collection of answers addresses common quantitative investing problems, including fixed-holding-period backtest sensitivity, judging factor data, evaluating strategies that fail live, factor development, market timing, and combining signals across…

BacktestingStatisticsFactor investingRisk management
BigQuant

The report classifies companies into startup, growth, mature, volatile, and declining stages using the signs of financing, operating, and investing cash flows. It describes differences among the groups: mature firms have the strongest profitability, while…

EquitiesFactor investingChina marketsBacktesting
BigQuant

This report summary examines how to turn a supplied composite stock factor into a long-only index-enhancement portfolio. It contrasts common linear optimization, which is simple to compute but ignores relationships among stocks and can produce concentrated…

EquitiesMachine learningFactor investingPortfolio construction
BigQuant

The report asks whether property-level real estate returns can be explained by style characteristics in addition to property type and location. Using quarterly data on more than 25,000 UK retail, office, and industrial properties from 2002 to 2020, it…

Multi-assetFactor investingMomentumStatistics
BigQuant

The article examines whether momentum and long-term reversal reflect changing exposures to priced risks rather than standalone return patterns. It tests whether past returns predict future realized betas, then uses an Instrumented Principal Component…

EquitiesMomentumFactor investingStatistics
BigQuant

This installment of an overseas research review introduces two studies, one on strategic asset allocation and another on quantitative stock selection. The allocation study asks whether a portfolio should be rebalanced and how the frequency and size of…

Multi-assetPortfolio constructionFactor investing
BigQuant

This brief BigQuant forum exchange addresses how to construct a custom factor when it is not available as a precomputed field. The question proposes an expression combining single-quarter revenue less single-quarter cost, divided by average total assets over…

Factor investingEquitiesStatistics
SuperMind

This stock-screening proposal combines three conditions: company market value below 10 billion yuan, no reported losses, and more than two limit-up days in the prior 10 days. It also seeks stocks with at least five moving averages converging, which the…

EquitiesChina marketsMomentumTechnical indicators
SuperMind

This document describes an annual year-end screen for Chinese equities. It combines a positive MACD reading, positive trailing P/E, and ROE above 15% in each of the prior five years. The stated rationale is to pair a trend signal with positive earnings…

EquitiesTechnical indicatorsMomentumFactor investing
SuperMind

This Chinese-market stock screen combines a beverage and alcohol industry filter with a turnover range of 3% to 12% and a holder-concentration condition described as below 20%. The article presents the criteria as a way to narrow candidates, then gives…

EquitiesChina marketsFactor investingStatistics