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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
WonderTrader
14 documents
Alphalens
14 documents
backtesting.py
11 documents
Technical Analysis
9 documents
QTPyLib
8 documents
Lumibot strategies
7 documents
QuantRocket
7 documents
Awesome Quant
1 documents

Search the library

511 documents

QuantInsti blog

This introductory tutorial explains how Python represents and manipulates collections of values. It covers zero-based indexing, slicing with exclusive end positions, and negative indices, then introduces arrays, tuples, lists, dictionaries, and sets.…

Equities
QuantInsti blog

The guide distinguishes statistical independence, correlation, and cointegration, concepts that are often confused when assessing diversification and trading relationships. Independence means observing one variable does not change the probability…

StatisticsMean reversionPairs tradingPortfolio construction
QuantInsti blog

The document is a brief event report about a talk on quantitative news trading at a Princeton–UChicago quantitative trading conference. The speaker’s topic was how news articles can be quantified and whether trading strategies based on news analytics can be…

SentimentEvent-drivenStatistics
QuantInsti blog

This interview profile describes a trader and strategy researcher’s plan to help establish a high-frequency trading desk within a broader systematic trading fund. The intended focus is short holding periods in Asian and European markets, drawing on machine…

High-frequency tradingMachine learningStatisticsMean reversion
QuantInsti blog

This interview presents a quantitative analyst’s path from engineering and statistics studies into quantitative finance, including work in high-frequency trading, banking, and strategy research. Its central lesson is to research markets carefully before…

High-frequency tradingMachine learningBacktestingMarket microstructure
QuantInsti blog

The document explains the Aroon indicator’s two components, Aroon Up and Aroon Down, which track how recently a period’s highest high and lowest low occurred. It gives a lookback-based calculation and shows that the resulting values are expressed as…

CryptoTechnical indicatorsTrend followingRisk management
QuantInsti blog

This project describes an intraday Nifty strategy using five-minute data, a 200-period simple moving average, and a 50-period exponential moving average. It takes long or short positions when the index closes beyond both averages, with no position when the…

FuturesOptionsTrend followingTechnical indicators
QuantInsti blog

This guide explains the long-short equity approach: buying stocks expected to outperform and shorting those expected to underperform. It distinguishes general long-short portfolios from market-neutral funds, which seek to offset broad market exposure, and…

EquitiesPortfolio constructionRisk managementBacktesting
QuantInsti blog

This project describes a daily trend-following strategy for liquid Nifty 50 stocks, taking both long and short positions. MACD and SuperTrend generate directional signals: MACD crossovers can provide quicker entries, while SuperTrend helps identify the…

EquitiesTrend followingTechnical indicatorsBacktesting
QuantInsti blog

The document introduces Bayesian classification and applies a Bernoulli Naive Bayes model to a long-only stock trading example. The features are binary signals derived from RSI and the stochastic oscillator; the target labels whether the following day's…

Machine learningStatisticsTechnical indicatorsBacktesting
QuantInsti blog

The document outlines a framework for deciding whether to expand algorithmic trading into another country or exchange. It groups the assessment into four considerations: market access and regulation, the technical requirements for connectivity, traded…

Multi-assetMarket microstructureExecution
QuantInsti blog

The article explains latency as the time required for data and orders to move through a trading system, distinguishing it from bandwidth or capacity. It compares a traditional workflow, where market data passes through a broker to a trader’s tools before…

ExecutionMarket microstructureHigh-frequency tradingRisk management
QuantInsti blog

The article introduces several ways to allocate weights in a multi-asset portfolio: equal weighting, risk parity, minimum variance, and Markowitz mean-variance optimization. It describes the intuition behind each method, including equal risk contributions in…

Portfolio constructionStatisticsRisk managementEquities
QuantInsti blog

The project backtests a mechanical strategy of selling an at-the-money SPY straddle each week, using options with roughly 45–60 days to expiry and holding each position until expiration. It describes sourcing option prices, matching entry dates with expiries…

OptionsVolatilityBacktestingRisk management
QuantInsti blog

The article introduces probability as a way to reason about uncertain market outcomes. It explains event probabilities using analyst forecasts, distinguishes subjective judgments from estimates based on historical observation, and gives the rules that…

StatisticsEquitiesRisk management
QuantInsti blog

The article argues that a backtest should approximate live trading conditions rather than maximize the appearance of historical returns. It recommends including commissions and slippage, with estimates adjusted to the instrument and checked against actual…

BacktestingExecutionRisk managementFutures
QuantInsti blog

The article introduces Ethereum as a blockchain platform for running smart contracts and decentralized applications. It explains Ether and gas, the Ethereum Virtual Machine, and examples of applications in decentralized finance and autonomous organizations.…

CryptoTechnical indicatorsMomentumDeFi
QuantInsti blog

The article outlines a process for turning a market hypothesis into a live systematic strategy. It starts with a rule, such as buying when price is above an N day moving average, then uses backtesting to choose parameters such as the lookback period, stop…

BacktestingRisk managementStatisticsTechnical indicators
QuantInsti blog

The article describes a one day seasonal trade in the S&P 500: enter at the close on the US federal tax deadline and exit at the following day’s close. It cites research reporting an average annual return of about 0.5% since 1980, with less attractive…

EquitiesUS marketsEvent-driven
QuantInsti blog

This project describes a cloud based automated system for WTI futures that uses machine learning to classify market conditions as trending or ranging. Several models vote within separate trend and range groups; when the groups disagree, their confidence…

FuturesMachine learningTrend followingMean reversion
QuantInsti blog

The article explains how to adapt Zipline’s CSV directory bundle to ingest daily Yahoo Finance files for a chosen market. It presents the bundle as an ETL pipeline: read files, normalize fields and dates, align records with a trading calendar, then write the…

EquitiesBacktesting
QuantInsti blog

This interview follows Xavier, an Australian IT architect with engineering and computer science training, as he moves from market research and investing to day trading and an interest in building an algorithmic trading desk. He describes exploring company…

BacktestingRisk managementCommoditiesEquities
QuantInsti blog

This roundup introduces a range of options topics through summaries of ten articles and several additional strategy guides. It describes options as tools for transferring risk and outlines strategies such as butterflies, spreads, straddles, and calendar…

OptionsVolatilityDerivatives pricingRisk management
QuantInsti blog

This article describes India’s securities regulator, SEBI, considering new algorithmic trading rules. The proposed measures discussed include reducing high order-to-trade ratios, discouraging orders submitted without intent to execute, and potentially…

Market microstructureRisk managementExecution