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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

12,303 documents

Quant Q&A

The note examines a MATLAB implementation of the log-likelihood for a GARCH model whose standardized innovations follow Hansen’s skew-t distribution. It asks whether a normalization parameter should be square-rooted and why the likelihood includes a term…

VolatilityStatisticsMachine learning
Quant Q&A

The document raises a practical issue in constructing an implied volatility curve for ETF options during high-frequency intraday trading. Its premise is that option quotes may update faster than the ETF cash price, so calculating implied volatility from the…

OptionsVolatilityDerivatives pricingHigh-frequency trading
Quant Q&A

The document considers whether recent historical bond-price volatility is suitable as the volatility input for pricing a bond option with the Black 76 model. The preferred input, when available, is implied volatility: the value that reproduces the option’s…

Fixed incomeOptionsVolatility
Quant Q&A

For sparse, illiquid index option chains, the response recommends cleaning and fitting prices or total variance rather than removing implied-volatility outliers and interpolating the remaining points. In option-price space, useful restrictions are clearer:…

OptionsDerivatives pricingVolatilityRisk management
Quant Q&A

The document presents a replication problem involving the conversion of an implied volatility surface into a local volatility surface using Dupire’s formula. The author builds a smooth implied surface by parameterizing total implied variance against time and…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The document describes how option pricing is used in practice, emphasizing that model choice depends on the contract and market. It notes that standard Black-Scholes-Merton pricing can be sufficient for European-style listed index options, while…

OptionsDerivatives pricingMarket makingVolatility
Quant Q&A

The document asks whether an option model can represent volatility as a deterministic function of strike alone. It distinguishes this idea from local volatility, which the author understands as deriving a volatility surface from market prices with dependence…

OptionsDerivatives pricingVolatility
Quant Q&A

The document reviews a commonly cited set of empirical regularities in asset returns, including heavy tails, volatility clustering, slow decay in the autocorrelation of absolute returns, leverage effects, and relationships between volume and volatility. It…

StatisticsVolatilityDerivatives pricing
Quant Q&A

The document describes a problem generating Monte Carlo paths from a QuantLib Black variance surface. The surface is interpolated and extrapolated from market data, with checks for decreasing total variance across maturities and butterfly irregularities, yet…

OptionsVolatilityDerivatives pricing
Quant Q&A

The discussion considers estimating implied volatility at each intraday option tick when end-of-day implied volatility and Greeks are available alongside tick-level option and underlying prices. One shortcut estimates volatility changes from option price…

OptionsVolatilityDerivatives pricing
Quant Q&A

The document raises a modeling question about computing option theta from the Black-Scholes partial differential equation when market implied volatility varies across strikes. It compares three approaches: standard Black-Scholes Greeks with implied…

OptionsVolatilityDerivatives pricing
Quant Q&A

The discussion explains three common criticisms of the Heston stochastic volatility model: limited flexibility when fitting observed option volatility surfaces, unusual behavior in its forward volatility skews, and limited usefulness for some exotic pricing…

OptionsVolatilityDerivatives pricingEquities
Quant Q&A

Brownian motion with drift, X_t = μt + σW_t, is normally distributed with mean μt and variance σ²t. Exponentiating a normally distributed variable gives a lognormal distribution, so the stated exponential process has lognormal levels; its logarithm has mean…

StatisticsVolatility
Quant Q&A

The document asks whether American option prices must increase with volatility whenever European option prices do, and seeks intuition for proving the result in more general models. The answer states that a standard American option under Black–Scholes is…

OptionsVolatilityDerivatives pricing
Quant Q&A

The document presents a European call pricing comparison under the Heston stochastic volatility model. It gives a characteristic-function integral for the closed-form price and a Milstein-style discretization for simulating variance and log spot, then…

OptionsDerivatives pricingVolatilityBacktesting
Quant Q&A

The document considers how to estimate annualized daily volatility from portfolio balances recorded after trades at uneven intervals. It presents a simple estimator under a zero-drift Wiener model: square each log return, divide by the elapsed time between…

VolatilityStatisticsRisk management
Quant Q&A

The document raises a model-calibration question: whether parameters in the Heston–Nandi GARCH option-pricing model can be estimated by fitting model prices to observed option prices instead of using maximum likelihood. The proposed approach is to choose a…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The document compares using historical index returns directly in Monte Carlo option pricing with fitting a return distribution or a dynamic stochastic-volatility model. Its central pricing point is that option values are expectations under a risk-neutral…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

A Lévy process is characterized by a drift vector, a diffusion matrix, and a Lévy measure, which together describe its continuous variation and jumps. The document distinguishes finite-dimensional estimation of drift and diffusion parameters from the…

StatisticsVolatility
Quant Q&A

The discussion explains why the magnitude of a short-interval return can serve as a rough estimate of volatility over that interval. Under a diffusion model for proportional price changes, the return contains both drift and a random shock. At daily…

VolatilityStatisticsEquities
Quant Q&A

This discussion considers how a shift from a currency peg toward a freer float could affect over-the-counter FX option prices, using the Moroccan dirham’s EUR and USD relationships as context. It notes that a regime change may bring a spot jump together with…

ForexOptionsVolatilityDerivatives pricing
Quant Q&A

The question asks why a butterfly spread’s value might be expressed using the probability that the terminal asset price falls between the outer strikes, multiplied by half the spacing between adjacent strikes. The answer frames pricing under the risk-neutral…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The document distinguishes GARCH forecasts from realized volatility (RV) measures. GARCH models use discrete observations, commonly squared returns, to estimate conditional variance and forecast volatility. RV instead aggregates high-frequency observations…

VolatilityStatisticsBacktesting
Quant Q&A

The document discusses using ARCH or GARCH models to describe time-varying volatility in a financial series, prompted by a researcher studying copper prices. These models relate current conditional variance to past observations or shocks. The proposed…

CommoditiesVolatilityStatistics