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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

246 documents

QuantStart

The document explains a C++ implementation of the Thomas algorithm, a specialized form of Gaussian elimination for solving tridiagonal linear systems. It describes the diagonal and off-diagonal coefficient vectors, the forward sweep that computes modified…

Statistics
QuantStart

The article demonstrates a machine-learning use of path signatures: classify handwritten digits by treating pen coordinates recorded over time as a path. It describes training a signature-based model on the UCI Pen-Based Recognition dataset, using ordered…

Machine learningStatistics
QuantStart

The article introduces the Johansen procedure for testing cointegration among multiple time series and estimating stationary linear combinations. It describes expressing a vector autoregressive model as a vector error correction model, then using the rank of…

StatisticsMean reversionPairs tradingPortfolio construction
QuantStart

The article explains how discrete Asian options use sampled prices along an asset path to determine their payoff. It distinguishes arithmetic averaging from geometric averaging and models price paths with geometric Brownian motion. Monte Carlo pricing…

OptionsDerivatives pricingVolatilityStatistics
QuantStart

The article explains how the analytic Black–Scholes formulas for European vanilla calls and puts can be translated into a procedural C++ implementation. It defines the underlying price, strike, interest rate, volatility, and time to maturity, then uses the…

OptionsDerivatives pricing
QuantStart

The document derives an explicit finite-difference scheme for the one-dimensional heat equation. It approximates the time derivative with a forward difference and the spatial second derivative with a centered difference, then advances the solution using…

Statistics
QuantStart

The article compares the typical financial engineering master’s curriculum with the capabilities quantitative funds seek when hiring. It lists common coursework such as derivatives pricing, numerical methods, portfolio optimization, risk, programming, and…

Machine learningStatisticsRisk managementHigh-frequency trading
QuantStart

The document explains how C++ function parameters behave when passed by value, by reference, or by const reference. Passing a large object such as a vector by value creates a copy, which can consume memory and processing time; passing by reference avoids…

Execution
QuantStart

This tutorial introduces the Ornstein-Uhlenbeck (OU) process as a continuous-time model for a variable that fluctuates around a long-term mean. It explains the roles of the mean, reversion speed, and volatility, and contrasts mean-reverting behavior with…

Mean reversionStatisticsPairs tradingDerivatives pricing
QuantStart

This career guide compares quantitative researcher and quantitative developer roles for engineers considering a move into quantitative finance. Researchers need evidence of rigorous analysis and stronger statistical skills, including time series methods and…

StatisticsMachine learning
QuantStart

This article explains why tactical asset allocation strategies can be difficult to evaluate over long periods: allocation signals are often monthly, leaving relatively few observations, and market regimes may persist for years. Retail investors may also lack…

Multi-assetPortfolio constructionBacktestingRisk management
QuantStart

This tutorial describes a MySQL and Python data store for daily equities history. Its proposed schema separates exchanges, data vendors, symbols, and daily prices, linking prices to both a security and its source. That structure supports multiple vendors,…

EquitiesUS marketsBacktesting
QuantStart

This article surveys five books on finite difference methods (FDM) and partial differential equations used in quantitative finance, especially for solving Black–Scholes pricing problems. It distinguishes texts that emphasize mathematical foundations,…

Derivatives pricingStatistics
QuantStart

The document extends binomial-tree option pricing from a small tree to a finite N-step model. It explains backward propagation from known terminal payoffs and presents risk-neutral valuation as an alternative: calculate the probabilities of ending at each…

OptionsDerivatives pricingStatistics
QuantStart

The article explains how to evaluate Python frameworks for systematic strategy research and how backtesting fits between strategy development and live deployment. It distinguishes historical performance testing from trade simulation and real-time order…

BacktestingPortfolio constructionRisk managementExecution
QuantStart

This tutorial explains the Thomas algorithm, also called the tridiagonal matrix algorithm, for solving the banded linear systems produced by an implicit finite-difference method. It frames the algorithm as Gaussian elimination adapted to a matrix with…

StatisticsDerivatives pricing
QuantStart

This tutorial presents the Crank-Nicolson method for numerically solving the one-dimensional heat equation. It motivates the method by noting that an explicit finite-difference scheme can impose a restrictive time step. Crank-Nicolson averages the spatial…

StatisticsDerivatives pricing
QuantStart

This development diary describes changes to a forex trading system: correcting how positions use bid and ask prices, adding historical tick data from CSV files, and building an initial event-driven backtester. The position update distinguishes trade…

ForexBacktestingExecutionHigh-frequency trading
QuantStart

This note extends the one-step binomial option model from zero interest rates to a positive continuously compounded risk-free rate. It bounds the stock’s possible up and down prices around risk-free growth, then chooses a risk-neutral probability that makes…

OptionsDerivatives pricingArbitrage
QuantStart

The article describes a long-only equity strategy that uses timestamped vendor sentiment scores as trading events in QSTrader. It enters a stock when its sentiment reaches the positive threshold of +6 and exits when the score falls to -1. Three versions…

SentimentEquitiesEvent-drivenBacktesting
QuantStart

The article explains why raw pointers and the legacy C++ auto_ptr create problems when stored in standard library containers. Raw pointers require explicit cleanup, so exceptions or early returns can leak allocated objects or leave dangling pointers.…

Statistics
QuantStart

The article walks through installing QSTrader on secondary Raspberry Pi nodes managed by SLURM, then checking the setup by running its 60/40 equity and bond example across the cluster. It describes installing Python build dependencies, creating a virtual…

BacktestingExecutionPortfolio construction
QuantStart

The document explains decision trees for regression and classification, describing them as models that divide feature space into rectangular regions using axis-aligned splits. For regression, each region predicts the mean response of its training…

Machine learningStatisticsBacktesting
QuantStart

This installment in an event-driven backtesting series describes a portfolio component that receives trading signals, creates orders, processes fills, and records positions and holdings over time. Its NaivePortfolio tracks per-symbol quantities alongside…

BacktestingPosition sizingRisk managementExecution