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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

3,481 documents

BigQuant

This report summary describes ways to derive equity selection factors from the distribution and time patterns of individual trade amounts within each minute. Proposed statistics include quantiles, standard deviation, skewness, and kurtosis. It also discusses…

EquitiesFactor investingMarket microstructureMean reversion
BigQuant

This forum question concerns two lines in a portfolio trading template that limit how much cash may be allocated to an instrument. The poster asks whether the instrument is a stock selected by a prediction step, whether the maximum cash parameter is the…

Position sizingRisk managementEquities
BigQuant

The document outlines a portfolio construction method for fund-of-funds portfolios targeting absolute returns. It combines Black-Litterman expected-return estimates, which blend market equilibrium returns with investors’ absolute or relative views according…

Multi-assetPortfolio constructionRisk managementStatistics
BigQuant

The speaker argues that quantitative investing should combine statistical description, machine learning prediction, and economic or financial theory. These serve different purposes: describing data, forecasting outcomes, and explaining underlying mechanisms.…

Machine learningStatisticsRisk managementUS markets
BigQuant

This Chinese equity screening proposal selects stocks with MACD above zero, applies filters based on company type, and excludes the technology innovation board. The post presents the approach as combining a technical signal with company classification. It…

China marketsEquitiesTechnical indicatorsMomentum
BigQuant

This document examines Chinese A-share stocks that lose an ST designation after financial or other problems are resolved. It frames the removal as a potentially positive corporate event and discusses prediction based on periodic financial reports and…

EquitiesChina marketsEvent-drivenBacktesting
BigQuant

This article describes a customizable stock-selection strategy built around five rules, mainly intended to identify oversold shares that may be stabilizing, with some rules aimed at potential upward moves. It contrasts platform-generated AI strategies, which…

EquitiesChina marketsBacktestingTechnical indicators
BigQuant

The document explains the Sharpe ratio as portfolio excess return over a risk-free rate divided by portfolio return volatility. It identifies the portfolio return, risk-free rate, and standard deviation as the inputs, with short-term government debt offered…

StatisticsRisk managementPortfolio construction
BigQuant

This research summary compares 17 machine learning approaches with conventional linear methods for predicting stock excess returns from 51 alpha factors. It describes splitting the prediction into two parts: an AR(1) forecast for return dispersion and a…

EquitiesMachine learningFactor investingStatistics
BigQuant

The document describes a monthly equity strategy based on market capitalization. At each month end, it ranks eligible stocks from smallest to largest by market value and buys the ten smallest. The portfolio holds those selections for the following month,…

EquitiesFactor investingChina marketsPortfolio construction
BigQuant

This research note describes an equity timing framework that combines more than thirty indicators across liquidity, economic conditions, valuation, capital flows, technical signals, and investor sentiment. It aggregates the indicators into a score ranging…

EquitiesTechnical indicatorsSentimentStatistics
BigQuant

This review summarizes research applying machine learning to monthly and annual returns for Chinese A-shares. The study combines stock characteristics, macroeconomic variables, and industry indicators, then compares linear models, tree methods, and neural…

China marketsEquitiesMachine learningFactor investing
BigQuant

This polemical essay argues that quantitative funds have an unfair advantage over retail traders, focusing on differences in settlement and trading rules. It claims that institutional strategies can exit or reverse positions rapidly while individual…

EquitiesHigh-frequency tradingMarket microstructureExecution
BigQuant

This document is a brief synopsis of a research report on strategic asset allocation and fund-of-funds investing. It says the report covers the research context, an asset-allocation framework, calculation models, and empirical strategy analysis. The subject…

Multi-assetPortfolio constructionBacktesting
BigQuant

This practitioner report describes experiments using a deep neural network to select stocks for a rapid rotation strategy. The model labels stocks by their subsequent return over a short holding interval, divides outcomes into ranked classes, and uses a…

EquitiesMachine learningHigh-frequency tradingBacktesting
BigQuant

This assignment analyzes how a stock selection strategy’s exposure to market styles changes over time, then outlines a framework for rotating among factor strategies. The initial analysis uses a small-capitalization selection factor and compares strategy…

EquitiesFactor investingStatisticsBacktesting
BigQuant

This note explains how to use TA-Lib functions in feature definitions. It distinguishes the function name from its inputs and clarifies that a feature label such as “ma” is only a name, while the expression supplies the price series and parameters. For…

Technical indicators
BigQuant

The document summarizes research on shared movements across commodity markets. It reports that commodity return fluctuations often move together and that a common factor explains a substantial portion of their variation. The factor is closely associated with…

CommoditiesFactor investingStatistics
BigQuant

This research summary proposes an information-distribution uniformity factor, UID, based on the idea that uneven arrivals of information create changes in share-price volatility. It uses intraday stock data to calculate daily high-frequency volatility, then…

EquitiesChina marketsVolatilityFactor investing
BigQuant

This forum exchange addresses how to skip a planned stock purchase when the stock opens more than 9% above its previous close. The suggested workflow is to add the opening price and prior close to the prediction dataset, merge those values with the…

EquitiesBacktestingExecution
BigQuant

DynamicRS is a price indicator built from comparisons among the current bar's high or low, the corresponding value on the previous bar, a value from a specified number of bars earlier, and the prior indicator reading. When the high-side conditions are met,…

Technical indicatorsEquities
BigQuant

The article reviews research on salience theory in equity pricing. Investors are proposed to overweight unusually prominent past returns relative to the market: strong standout gains can attract demand and raise prices, while conspicuous declines can depress…

EquitiesFactor investingSentimentStatistics
BigQuant

A user asks whether a futures position should be counted at its full market value or at a value adjusted for margin when a strategy calculates returns by adding asset value and cash. The reply emphasizes that futures use margin and daily settlement, so their…

FuturesRisk managementStatistics
BigQuant

This research summary considers how the CSI 300 might change if Chinese companies listed overseas returned to mainland China. It notes that trade tensions and tighter US oversight were associated with weaker performance of US-listed Chinese stocks relative…

EquitiesChina marketsPortfolio constructionStatistics