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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

This discussion explains why the constant-volatility assumption in Black-Scholes can be restrictive and outlines broader ways to extend the model. Stochastic-volatility models make volatility vary over time, with model dynamics linked to quantities such as…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

This exchange discusses whether Stata can estimate a BEKK-GARCH model and clarifies when that model is appropriate. One answer says it is not aware of a Stata implementation and points to R software that can fit a BEKK(1,1) model, while noting that…

StatisticsVolatilityBacktesting
Quant Q&A

The document raises a question about very negative AUDJPY market strangle volatility quotes at long maturities. It gives examples for 25-delta and 10-delta strangles, alongside an at-the-money-forward volatility level, and asks whether the resulting concave…

ForexOptionsVolatilityDerivatives pricing
Quant Q&A

The document addresses an apparent tension between the stationarity assumptions used in GARCH models and their purpose of modeling changing volatility. Its answer distinguishes unconditional stationarity from conditional volatility: a stationary process can…

StatisticsVolatility
Quant Q&A

The document asks whether Novikov’s condition holds for a proposed Girsanov kernel in the Heston model. The stock’s market price of risk leads to an exponential-integrability requirement involving the time integral of the reciprocal variance. The variance…

OptionsVolatilityStatistics
Quant Q&A

The note describes several reasons to trade a risk reversal, which combines a put and a call to express a view on implied-volatility skew. A delta-hedged risk reversal can be viewed as a wager on realized spot–volatility covariance relative to the covariance…

OptionsVolatilityDerivatives pricingRisk management
Quant Q&A

The document proposes extending the Heston stochastic volatility model by making its long-run variance level itself a mean-reverting stochastic process. The spot variance mean-reverts toward that changing level, while the asset price follows a diffusion with…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The document asks how to estimate an autoregressive model with a GARCH volatility process in R. Its practical suggestion uses the fGarch package, specifying an AR(1) mean structure and a GARCH(1,1) conditional variance structure, then reviewing the fitted…

StatisticsVolatility
Quant Q&A

This question compares a perpetual American put’s exercise threshold under Black–Scholes assumptions with the value of waiting for a finite period. The author argues that, from a price above the perpetual threshold, there should be some horizon for which…

OptionsDerivatives pricingVolatility
Quant Q&A

This note diagnoses a warning from a rolling GARCH estimation in R. The example computes returns from SPY closing prices with `ROC`, then passes them to `ugarchroll` for repeated model estimation. The key issue is that the first return is missing because a…

VolatilityBacktestingEquitiesStatistics
Quant Q&A

The document explains why a parallel shift in a forward curve does not necessarily produce the same change in a forward swap rate. The effect depends on how the shift is defined: the answer defines it through overnight rates implied by discount factors, then…

Fixed incomeDerivatives pricingVolatility
Quant Q&A

The document examines the limits of replicating variance exposure with a weighted strip of out-of-the-money calls and puts, a construction associated with log-contract replication and the VIX calculation. The cited explanation says the continuous static…

OptionsVolatilityDerivatives pricing
Quant Q&A

The document examines whether a calendar spread can provide a stable way to trade changes in implied volatility while remaining delta- and gamma-neutral. The responses explain that neutrality holds only under particular market conditions and can deteriorate…

OptionsVolatilityDerivatives pricingRisk management
Quant Q&A

The document asks how to estimate covariance when optimizing a minimum-variance portfolio over weekly or fortnightly cumulative returns. It contrasts calculating covariance directly from returns sampled at the target frequency with scaling a daily covariance…

StatisticsPortfolio constructionBacktestingOptions
Quant Q&A

The post compares statistics for log returns and simple returns, using normally distributed simulated log returns and converting them to simple returns. Its central clarification is that exponentiating the sample mean of log returns does not produce the mean…

StatisticsVolatility
Quant Q&A

The document discusses how to estimate risk inputs for an option on the difference between two futures. It presents three approaches to spread volatility: calculate it from historical spread returns, combine the futures’ implied volatilities with a…

FuturesOptionsVolatilityDerivatives pricing
Quant Q&A

This discussion explains how to retrieve estimated volatility summaries from an R model fit with the stochvol package. The questioner compares a plot produced by the package’s plotting function with median values computed from the latent simulation draws and…

VolatilityMachine learningStatistics
Quant Q&A

This note examines how Bloomberg’s swaption volatility quotes relate to Black-model prices, focusing on a EUR cash-settled payer swaption. It defines the normal cumulative distribution and Black formula, then distinguishes the physical-settlement annuity…

OptionsFixed incomeDerivatives pricingVolatility
Quant Q&A

The document describes difficulties in deriving pseudo-European implied volatility slices from American option prices through de-Americanization. The method may produce different European implied volatilities at the same strike when both calls and puts are…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The document asks why a stock-price diffusion changes when a model includes correlation between interest rates and stock returns. In the uncorrelated case, the stock diffusion coefficient is constant; in the correlated case described, it is scaled by the…

OptionsDerivatives pricingVolatilityFixed income
Quant Q&A

The document raises whether annualized volatility estimated from monthly returns is typically lower than estimates based on daily returns. The questioner considers using daily observations to increase the sample size, while worrying that the measured…

StatisticsVolatilityRisk managementPortfolio construction
Quant Q&A

The document distinguishes pricing models used for equity options from parametric fits used to store and retrieve implied volatility data. It identifies Black–Scholes, including time-dependent versions, and local volatility as common industry choices, with…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The document explains how a delta-neutral position with long call options can generate gains through repeated stock rebalancing. In its example, the trader starts long calls and short shares; a price decline reduces call delta, so buying shares to restore…

OptionsDerivatives pricingVolatility
Quant Q&A

This discussion considers whether VIX-style volatility indices could be created for individual S&P 500 industries. Since the VIX is derived from index option prices, the proposed analogue would likewise depend on an adequate market in options on the relevant…

OptionsVolatilityEquities