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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

246 documents

QuantStart

The article compares ways to organize a trading business: managed accounts, commodity trading advisory firms, proprietary funds, hedge funds, and family offices. Managed accounts are presented as a lower-cost way to manage separate client accounts and build…

Risk managementFuturesForexEquities
QuantStart

This tutorial introduces the notation and basic objects of linear algebra used in machine learning and quantitative finance. It defines scalars, vectors, matrices, and higher-order tensors, explains their dimensions and indexing, and gives examples such as…

Machine learningStatistics
QuantStart

The article introduces time series analysis as a statistical way to study sequential data modeled as outcomes of an underlying stochastic process. It highlights trends, seasonal patterns, and serial dependence, including volatility clustering, as features…

StatisticsVolatilityTrend followingMachine learning
QuantStart

The document distinguishes four common quantitative finance roles: quantitative trader, quantitative researcher, financial engineer, and quantitative developer. Traders search for profitable signals and build trading algorithms. Researchers develop…

Machine learningDerivatives pricingExecutionHigh-frequency trading
QuantStart

The article compares Windows, macOS, and Ubuntu/Linux as environments for quantitative trading research and deployment. It frames the choice around the user's research workload, preferred tools, need for automation, and comfort with command-line work.…

Machine learningBacktestingExecution
QuantStart

The document explains how virtual destructors support safe cleanup in C++ inheritance hierarchies. When code deletes a derived object through a pointer to its base class, a non-virtual base destructor may prevent the derived destructor from running. If the…

Statistics
QuantStart

The document explains the Position component in an early event-driven trading system. A position records buys and sells, average prices, commissions, cost basis, net exposure, and realized and unrealized profit and loss. The broader design separates this…

EquitiesPortfolio constructionRisk managementPosition sizing
QuantStart

The article develops an object-oriented framework for generating synthetic correlation matrices as an initial component of a tool for creating correlated financial time series. An abstract base class defines a common generation interface so different models…

StatisticsPortfolio constructionBacktestingMachine learning
QuantStart

This conference trip report summarizes a talk about seeking trading signals in alternative data. Examples include satellite and drone imagery, purchase receipts, social media, industrial sensor data, agriculture, energy supply and demand, weather, and…

Machine learningSentimentCommoditiesEvent-driven
QuantStart

The article introduces serial correlation, also called autocorrelation, as dependence between observations at different times. It reviews expectation, variance, covariance, and correlation, then explains why correlation is a normalized measure of linear…

StatisticsMean reversionPairs tradingBacktesting
QuantStart

The article explains an event-driven backtesting design that separates a lean Portfolio class from a PortfolioHandler. The Portfolio stores cash and positions, updates position values after transactions, and calculates portfolio cash, equity, and realized…

BacktestingPortfolio constructionRisk managementExecution
QuantStart

This article introduces Markov Chain Monte Carlo as a numerical way to approximate Bayesian posterior distributions when analytical calculations, including conjugate-prior shortcuts, are unavailable. It explains the Metropolis algorithm as a sequence of…

StatisticsMachine learning
QuantStart

This article recommends five less commonly cited reading choices for people preparing for quantitative finance roles. The list spans mathematical finance, continuous-time arbitrage and derivative pricing, career accounts from practitioners, evaluation of…

Derivatives pricingArbitragePortfolio constructionRisk management
QuantStart

This beginner's guide explains Bayesian statistics as a framework for updating uncertainty when new evidence arrives. It contrasts Bayesian probability, interpreted as confidence in possible outcomes, with the frequentist view of probability as long-run…

StatisticsMachine learning
QuantStart

This June 2020 update reports several releases of the QSTrader backtesting engine. Its main technical change was an overhaul of portfolio, position, transaction, and simulated broker components to support short selling. The platform moved from long-only…

BacktestingPairs tradingMean reversion
QuantStart

This career guide describes steps for PhD graduates pursuing junior quantitative roles. It surveys several paths—quant trading, structuring, financial engineering, and quant development—and advises candidates to research how different firms use each role…

Derivatives pricingStatistics
QuantStart

The article introduces matrix inversion through systems of simultaneous linear equations. It represents the equations as A x = b, defines the identity matrix, and explains that when an inverse exists, multiplying by it gives the solution x = A⁻¹b. This…

StatisticsDerivatives pricingMachine learning
QuantStart

The diary entry describes an early event-driven forex system and its roadmap toward more realistic trading and backtesting. It identifies components already present, including price streaming, signal generation, order execution, local portfolio replication,…

ForexBacktestingExecutionRisk management
QuantStart

The document explains why no single programming language is best for every algorithmic trading system. Language choice follows system requirements: research and backtesting, signal generation, portfolio construction, risk management, and order execution have…

BacktestingPortfolio constructionRisk managementExecution
QuantStart

The article argues that entering quantitative finance in one’s thirties is feasible and frames the transition around skills and preparation rather than age. It recommends an honest assessment of mathematical background, especially linear algebra, calculus,…

StatisticsMachine learning
QuantStart

The document explains how to separate random number generation from Monte Carlo pricing code through an abstract generator interface. It describes exposing seed controls, draw dimensionality, integer generation, and uniform samples so that downstream…

StatisticsDerivatives pricing
QuantStart

This article describes a mean-reversion strategy trading the spread between TLT, a long-duration Treasury ETF, and IEI, an intermediate-duration Treasury ETF. A recursive Kalman filter estimates a time-varying linear relationship between the pair, along with…

Pairs tradingMean reversionFuturesFixed income
QuantStart

This article introduces statistical learning as the task of estimating a relationship between response variables and predictor features. A quantitative finance example frames index values as responses and company fundamentals as possible predictors. It…

Machine learningStatisticsEquitiesUS markets
QuantStart

This article describes a directional S&P 500 strategy that refits a return model on a rolling window, forecasts the next day, and takes a long or short position according to the forecast sign. For each window, it selects an ARMA specification by Akaike…

EquitiesUS marketsStatisticsVolatility