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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
QuantRocket
7 documents
Lumibot strategies
7 documents
Awesome Quant
1 documents

Search the library

12,303 documents

Quant Q&A

The document concerns noisy, incomplete one-minute price and volume data for several stocks, with the goal of forecasting volatility for the month after the sample ends and investigating whether volatility is related to liquidity. The response recommends an…

High-frequency tradingVolatilityEquitiesStatistics
Quant Q&A

The document distinguishes implied volatility from historical volatility. Implied volatility is the volatility parameter that makes an option pricing model reproduce the market price of an option; it is inferred from current prices rather than calculated…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The explanation links negative correlation between an asset’s spot price and its instantaneous volatility to a downward implied-volatility skew. It contrasts the result with the standard Black–Scholes assumption of constant volatility, under which…

OptionsVolatilityDerivatives pricing
Quant Q&A

The document presents several uses of the term microstructure noise. In high-frequency econometrics, it can mean disturbances that make estimates such as realized volatility unstable. In market microstructure, it can refer to observed price deviations from…

Market microstructureHigh-frequency tradingVolatilityStatistics
Quant Q&A

The document recommends a practical scenario approach for pricing down-and-in puts when vanilla option prices and implied volatilities are available. Apply a spot shift and test a small set of implied volatility shifts, using the existing volatility surface…

OptionsVolatilityDerivatives pricingRisk management
Quant Q&A

The document discusses estimating a Heston stochastic volatility model with an Unscented Kalman Filter. The response recommends expressing the system in log prices or returns rather than raw prices, then discretizing the price and variance dynamics as a…

OptionsVolatilityStatisticsDerivatives pricing
Quant Q&A

The document considers how to price options with maturities as long as several decades when market quotes for those maturities are unavailable or illiquid. It raises three possible inputs: calibrating to the liquid portion of the volatility surface, using…

OptionsVolatilityDerivatives pricing
Quant Q&A

The document asks why derivatives traders use stochastic-volatility models when pricing and hedging products such as digital options. It frames the issue through approximate static replication: a digital payoff can be approximated with a narrow put spread,…

OptionsVolatilityDerivatives pricingRisk management
Quant Q&A

The discussion explains how Fourier transforms and characteristic functions can price European options when the underlying log-price density is difficult to obtain directly. It starts from the discounted expected payoff, expresses that expectation as an…

OptionsDerivatives pricingVolatilityBacktesting
Quant Q&A

The document asks whether persistent realized volatility reflects slowly changing latent volatility or whether observed volatility shocks cause future latent volatility to rise. It presents the latter as closer to the usual market consensus, then describes…

VolatilityStatistics
Quant Q&A

The document asks how to model stock returns whose estimated left and right tail exponents differ, noting that a skewed Student-t distribution uses a shared tail exponent. It describes an analysis of daily prices for 250 stocks beginning in 1972, with tail…

EquitiesStatisticsRisk managementVolatility
Quant Q&A

The document uses a delta-hedged option approximation to estimate the underlying price move needed to offset one day of time decay. Setting the approximate gamma gain equal to theta decay gives a break-even move proportional to the square root of twice the…

OptionsFuturesCommoditiesVolatility
Quant Q&A

The document challenges the claim that demand is generally greater for out-of-the-money and in-the-money options than for at-the-money options. The responses distinguish trading volume from volatility-smile explanations: near-the-money options are described…

OptionsVolatilityMarket microstructure
Quant Q&A

The document examines whether gross exposure divided by capital is a useful definition of portfolio leverage. It points out that this measure omits cash and can overstate risk when offsetting holdings are highly correlated, then derives a risk-based…

Risk managementPortfolio constructionVolatilityStatistics
Quant Q&A

The document examines a common first-order approximation for the premium of an FX risk reversal: multiply at-the-money option vega by the risk reversal in volatility points. It explains that the approximation should compare the market-priced call and put…

ForexOptionsVolatilityDerivatives pricing
Quant Q&A

The document considers how to infer a second asset’s covariance structure from that of a first asset and a relationship between their returns. One answer models the second asset’s return as a constant beta times the first asset’s return plus a small…

StatisticsVolatilityEquities
Quant Q&A

The document considers whether ordinary least squares can estimate the parameters of a GARCH model, which are commonly fitted using quasi-maximum likelihood. It summarizes a reference describing explicit OLS estimation for ARCH models through an…

StatisticsVolatilityMachine learning
Quant Q&A

The document explains why barrier-option Greeks cannot be represented by one general curve. Their shapes depend on spot, strike, barrier placement, volatility, and time to expiry. In a knock-out call, a rising spot can increase the call’s payoff potential…

OptionsDerivatives pricingVolatility
Quant Q&A

The document shows how to estimate the real-world probability that a stock will finish within a specified price interval using a geometric Brownian motion model. It treats the stated required annual return as the drift and, for illustration, uses implied…

EquitiesOptionsVolatilityStatistics
Quant Q&A

Vega and gamma often have the same sign for a single option under standard assumptions, but a calendar spread can produce opposite exposures. The example pairs a long near-dated at-the-money option with a short longer-dated at-the-money option, yielding long…

OptionsVolatilityDerivatives pricing
Quant Q&A

The document considers pricing a European option when the underlying asset’s daily return is constrained to a fixed interval. It proposes replacing the usual return density with a two-sided truncated normal risk-neutral density, then valuing the option by…

OptionsDerivatives pricingVolatility
Quant Q&A

This question asks how SABR is used in practical option workflows. It contrasts fitting or interpolating a volatility surface with SABR to obtain Black implied volatilities, then managing positions with Black Greeks, against measuring and managing risk…

OptionsVolatilityDerivatives pricingRisk management
Quant Q&A

This troubleshooting note concerns an error raised by the rugarch package while running a rolling GARCH forecast with ugarchroll. The reported setup estimates an sGARCH model with an ARMA mean specification, then forecasts one step ahead over a rolling…

StatisticsVolatilityBacktesting
Quant Q&A

The document asks for a more accessible derivation of the semi-closed-form price of a European call under the Heston stochastic-volatility model. It does not derive the pricing formula itself; instead, the replies point readers toward a paper with a…

OptionsDerivatives pricingVolatilityStatistics