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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
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

12,303 documents

Quant Q&A

The document explains why the intercept in a standard GARCH(1,1) conditional variance equation is usually constrained to be strictly positive, even when the lagged squared return and lagged variance terms are nonnegative. The accepted answer distinguishes…

StatisticsVolatilityRisk management
Quant Q&A

The document asks how to express time-sensitive inputs when using a closed-form option pricing approximation, specifically the Bjerksund–Stensland model. The question lists the risk-free rate, volatility, time to expiration, and cost of carry, and asks…

OptionsDerivatives pricingVolatility
Quant Q&A

The document describes a workflow for pricing interest-rate caps at multiple valuation dates with QuantLib. It builds yield curves from dated market data, interpolates cap volatilities across maturities, then creates Euribor-linked cap instruments and prices…

Fixed incomeDerivatives pricingVolatility
Quant Q&A

The document develops a definition of realized gamma from the second-order term in an option’s price change. It defines gamma dollars as one half of spot squared times option gamma, then uses the diffusion component of spot’s change to show that the option’s…

OptionsVolatilityDerivatives pricing
Quant Q&A

The document examines whether non-Gaussian stock returns can be transformed into a Gaussian series. It warns that forcing observations to match a normal quantile distribution can produce a normal-looking sample while distorting the data and removing…

EquitiesStatisticsVolatilityFactor investing
Quant Q&A

The document explores an empirical approach to estimating fair implied volatilities for SPX options. It proposes using a proxy for future volatility, such as VIX, to select similar historical observations, fitting a distribution to those observations, and…

OptionsVolatilityStatistics
Quant Q&A

The document explains why applying Itô’s lemma to a function of a diffusion requires a second-order term. For a process with drift and Brownian volatility, a Taylor expansion includes the squared process increment. Ordinary time increments have negligible…

StatisticsDerivatives pricingVolatility
Quant Q&A

The document explains how to turn a Dupire local volatility model into a European option price. Its example starts with an implied volatility surface, constructs a Black–Scholes–Merton process that uses the surface, and sets up a vanilla call. It then…

OptionsDerivatives pricingVolatilityBacktesting
Quant Q&A

The document considers how to derive a pricing PDE for a derivative under the Heston–Hull–White model, where the stock, stochastic variance, and short rate are correlated. It identifies a problem with applying the usual discounting transformation directly:…

Derivatives pricingOptionsVolatilityRisk management
Quant Q&A

The document examines how to calculate vega sensitivity under the Basel Fundamental Review of the Trading Book standardized approach. The question contrasts multiplying Black–Scholes vega by implied volatility expressed as a decimal with an example using a…

Risk managementVolatilityDerivatives pricingFixed income
Quant Q&A

The document discusses two interest-rate products that pay based on absolute changes in future rate volatility. One references successive six-month LIBOR changes; the other references changes in a long-dated forward-start swap rate and includes capital…

Fixed incomeVolatilityDerivatives pricingRisk management
Quant Q&A

The document raises a preprocessing question: how to transform a time series with changing volatility before using it as a predictor in a regression model. The author reports trying several scaling methods without obtaining satisfactory stationarity, and…

StatisticsMachine learningVolatility
Quant Q&A

The document asks how to implement Rebonato’s approximation for swaption volatility in C++. The formula combines swap weights, forward rates, correlations, and the integrated products of forward-rate volatilities. With piecewise-constant volatilities, each…

Fixed incomeOptionsVolatilityDerivatives pricing
Quant Q&A

The document extends the Ornstein–Uhlenbeck process by allowing its mean-reversion level to vary over time. Applying an integrating factor gives a closed-form stochastic solution: the initial value decays exponentially, the drift function contributes through…

StatisticsMean reversionVolatility
Quant Q&A

The discussion asks whether a binary option always has a Black–Scholes implied volatility and presents several competing answers. A binary call price can rise and then fall as volatility increases, so an observed price may correspond to two volatility…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The document asks how to sample a Wiener process with specified starting and ending values as well as a prescribed maximum and minimum, extending the usual Brownian bridge setup. A response outlines an approximate construction for a fixed maximum: simulate a…

StatisticsVolatility
Quant Q&A

The document examines valuing a corridor variance swap partway through its life, when realized variance is known for the elapsed period but future variance remains uncertain. It explains that a standard variance swap can be replicated in theory by weighting…

OptionsVolatilityDerivatives pricingMarket microstructure
Quant Q&A

The discussion examines what “vega” should mean when an exotic option is priced with a model beyond Black–Scholes. It raises several possible bump conventions: shifting local volatility, changing stochastic volatility parameters, or bumping an implied…

OptionsVolatilityDerivatives pricingRisk management
Quant Q&A

The document reviews evidence on whether high-frequency trading changes market quality in ordinary conditions and during stress. It points to research concluding that greater low-latency activity improves short-term volatility, spreads, and displayed…

High-frequency tradingMarket microstructureVolatility
Quant Q&A

The document raises a modeling question about applying the heterogeneous autoregressive realized volatility model to forecast one-minute realized volatility in an active Bitcoin market. The proposed regressors are realized volatility measured over recent…

CryptoVolatilityMarket microstructureBacktesting
Quant Q&A

The question asks why residuals from a GARCH model might appear less Gaussian than raw returns, despite the expectation that volatility adjustment should remove some non-normality. The answer argues that seeing this outcome challenges the assumed model, then…

EquitiesVolatilityStatisticsRisk management
Quant Q&A

The document discusses how traders construct implied-volatility smiles and surfaces from option quotes. Its central guidance is to use liquid market instruments across strikes rather than building a curve from calls alone or puts alone. For many European…

OptionsVolatilityDerivatives pricingMarket microstructure
Quant Q&A

The document considers whether combining a strategy with 20% annualized volatility and one with 15% volatility, when their returns have zero correlation, can produce a portfolio with volatility below 15%. The accepted response uses the portfolio variance…

Portfolio constructionVolatilityRisk managementStatistics
Quant Q&A

The document asks what calibration of an option volatility surface means, how often it should occur, and what determines its timing. It starts from the familiar implied volatility smile for one expiry and asks whether a spline with anchor points can…

OptionsVolatilityDerivatives pricing