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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

57 documents

QuantInsti blog

This article introduces five technical indicators for assessing price trends, momentum, and volatility: moving averages, the Average Directional Index, Moving Average Convergence Divergence, the Relative Strength Index, and Bollinger Bands. It distinguishes…

Technical indicatorsTrend followingMomentumVolatility
QuantInsti blog

The article introduces delta as option price sensitivity and gamma as the rate at which delta changes with the underlying price. It describes gamma scalping as repeatedly adjusting an options portfolio to manage its Greek exposures while seeking to benefit…

OptionsVolatilityRisk managementDerivatives pricing
QuantInsti blog

This overview introduces multi-leg options strategies, including straddles, strangles, iron condors, and iron butterflies. It explains Delta, Gamma, Theta, Vega, and Rho as measures of how option values and portfolio exposures respond to changes in the…

OptionsVolatilityDerivatives pricingRisk management
QuantInsti blog

The document introduces volatility as a measure of return dispersion and distinguishes historical volatility, calculated from past prices, from implied volatility inferred from option prices. Its historical-volatility example uses logarithmic returns and a…

VolatilityRisk managementOptionsStatistics
QuantInsti blog

This interview describes David U. Ordiz’s progression from discretionary Bund futures trading to systematic research and portfolio management. His approach focuses on intraday algorithms seeking short-term trend or counter-trend moves across index futures,…

FuturesVolatilityRisk managementBacktesting
QuantInsti blog

The article introduces the Kalman filter as a recursive method for estimating a changing, partly unobserved state by combining model predictions with noisy measurements and their uncertainty. It explains concepts including normal distributions, variance,…

StatisticsPairs tradingVolatilityPortfolio construction
QuantInsti blog

This overview explains high-frequency trading as automated order placement that depends on rapid market data, fast decision systems, and low-latency execution. It describes co-location, tick-by-tick feeds, and market making, where firms quote both sides and…

High-frequency tradingMarket makingMarket microstructureVolatility
QuantInsti blog

A retail trader describes moving from options volatility trading toward a broader systematic approach after the 2018 bear market exposed limits in relying on one strategy. He is refining his earlier short volatility system and exploring a floor-and-ceiling…

OptionsVolatilityStatisticsRisk management
QuantInsti blog

The article explains proprietary trading as a firm’s use of its own capital, then surveys strategies including merger arbitrage, index arbitrage, global macro trading, and volatility arbitrage. Its index example illustrates buying an ETF while shorting its…

ArbitrageVolatilityOptionsRisk management
QuantInsti blog

This introductory guide explains descriptive statistics and probability concepts using daily Apple stock data. It defines mean, mode, and median, then introduces range and standard deviation as ways to describe price levels and dispersion. It distinguishes…

StatisticsVolatilityTechnical indicatorsEquities
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

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

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

The document discusses SEBI’s approval for Indian exchanges to set equity derivatives trading hours between 9 a.m. and 11:55 p.m., subject to suitable risk systems and infrastructure. Approval alone does not ensure the exchanges will extend their sessions.…

EquitiesFuturesExecutionRisk management
QuantInsti blog

The document explains kurtosis as a measure of how heavy or light a return distribution’s tails are relative to a normal distribution. It distinguishes ordinary kurtosis from excess kurtosis, for which the normal distribution is the zero baseline, and…

StatisticsRisk managementVolatility
QuantInsti blog

The document introduces the Heston model as an option-pricing framework that allows both the underlying asset price and its variance to evolve stochastically. Unlike constant-volatility Black–Scholes, it models variance as mean reverting, with random…

OptionsVolatilityDerivatives pricingStatistics
QuantInsti blog

The article examines market effects associated with the early COVID-19 outbreak and the Russia–Saudi Arabia oil price dispute. It describes calculating average forward returns after historical drawdowns: compute cumulative returns and running peaks, identify…

EquitiesOptionsBreakoutVolatility
QuantInsti blog

This article introduces exotic options as contracts whose payoff, exercise conditions, or underlying can differ from standard calls and puts. It describes barrier options, which activate or expire when a price threshold is reached; binary options, which pay…

OptionsDerivatives pricingVolatilityStatistics
QuantInsti blog

This overview explains index options as contracts whose value depends on a market index, and describes how they can be used to speculate on index moves or hedge exposure. It distinguishes index options from options on individual stocks and surveys broad…

OptionsEquitiesVolatilityRisk management
QuantInsti blog

The article describes three sentiment measures and proposes contrarian trades based on them. VIX is presented as an options-derived estimate of expected S&P 500 volatility; high readings are associated with fear and falling prices, while low readings are…

SentimentVolatilityOptionsFutures
QuantInsti blog

This article explains why index volatility depends on both the volatility of constituent stocks and the correlation among them. When stocks move more independently, their individual volatility can rise without a comparable increase in index volatility; when…

OptionsVolatilityArbitrageEquities
QuantInsti blog

This interview traces Vijayakumar’s progression from early stock investments and repeated losses to options trading and work on algorithmic strategies. He describes learning through books and practice, then studying derivatives, Python, and quantitative…

OptionsRisk managementVolatilityMachine learning
QuantInsti blog

This project tests an options dispersion strategy that compares BANKNIFTY implied volatility with the weight-adjusted implied volatility of its constituent stocks. The author estimates average implied volatility from first out-of-the-money calls and puts,…

OptionsVolatilityArbitrageBacktesting
QuantInsti blog

The document explains the Black–Scholes model for pricing European call options. It introduces the main inputs—underlying price, strike, time to expiry, risk-free rate, and volatility—and describes how the formula combines the discounted strike with the…

OptionsDerivatives pricingVolatilityStatistics