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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

20,364 documents

Quant Q&A

The document explains how to recover the standard deviation of a normally distributed forecast from two interval quantiles. It uses an 80% interval and a point forecast as context, then presents the general quantile relationship: a normal quantile equals the…

StatisticsRisk management
Quant Q&A

The discussion considers how to estimate covariance when historical relationships may shift with observable conditions. Although framed around selecting a portfolio of athletes, its examples—player matchups and racing-track characteristics—illustrate why…

StatisticsPortfolio constructionRisk managementMachine learning
Quant Q&A

The document introduces Emanuel Derman’s volatility approximation for a volatility swap under an assumed linear volatility skew. It describes the approximation as assuming a linear put skew and a flat call skew, with applicability focused on strikes…

OptionsVolatilityDerivatives pricing
Quant Q&A

The document asks how to interpret dollar neutrality when a portfolio shorts an index ETF and uses the resulting proceeds to finance long-only holdings. The investor describes gross positions whose long exposure exceeds the short exposure, despite the short…

EquitiesPortfolio constructionRisk management
Quant Q&A

The document explores whether an option-implied risk-neutral distribution can be checked by integrating its density twice and comparing the reconstructed call prices with observed option prices. It grounds the idea in the Breeden-Litzenberger relationship,…

OptionsDerivatives pricingStatistics
Quant Q&A

The document explains that a volume dimension specifies the unit used to report traded amounts. For equities, volume is commonly stated as a count of shares. For bonds, the relevant unit may instead be nominal value, which refers to the bond's face or par…

Fixed incomeEquitiesMarket microstructure
Quant Q&A

The document discusses how to simulate the SFGK continuous double auction limit order book model when the theoretical tick size approaches zero. The original question concerns Poisson order arrivals over an unbounded price line and how discrete price levels…

Market microstructureExecutionStatistics
Quant Q&A

The document considers two different quantities involving the exponential of Brownian motion: a stochastic integral with respect to Brownian motion and an ordinary time integral. It applies Itō's isometry to the stochastic integral, reducing its second…

Statistics
Quant Q&A

The document surveys ways to study relationships among time series, including covariance, correlation, principal component analysis, factor analysis, cointegration, information measures, graph theory, and clustering. Its central question is whether these…

StatisticsFactor investing
Quant Q&A

The document raises a portfolio-theory question about whether including government bonds as an available asset makes the global minimum-variance portfolio an all-bond allocation. It connects this question to the efficient frontier: the global…

Portfolio constructionFixed incomeRisk managementStatistics
Quant Q&A

The document asks how Cover’s universal portfolio algorithm compares with two simple allocations: a constant-relative-proportions portfolio that rebalances to equal weights, and a buy-and-hold allocation that initially divides wealth equally across assets.…

Portfolio constructionBacktestingStatistics
Quant Q&A

The document explains why the relationship between the underlying price and future volatility can affect autocall valuation. Autocalls often include digital payoffs and barrier conditions, so their value depends on the chance that the underlying crosses a…

OptionsVolatilityDerivatives pricingRisk management
Quant Q&A

The document addresses a discrepancy between a calculated FX option delta and a market terminal’s value. The key clarification is that the instrument is a digital vanilla, or binary, option, so ordinary vanilla Black–Scholes delta inputs and intuition do not…

ForexOptionsDerivatives pricing
Quant Q&A

The document explains why the Ornstein–Uhlenbeck (OU) process is often used as an interest rate model. Its stationarity, Gaussian behavior, Markov property, and continuous-time formulation make analysis and pricing more tractable. These assumptions are…

Fixed incomeMean reversionStatistics
Quant Q&A

This discussion asks how to generalize covariance and correlation when modeling dependence in heavy-tailed settings and extreme events. One response proposes mutual information, which quantifies how much observing one variable reduces uncertainty about…

StatisticsRisk managementMachine learning
Quant Q&A

This exchange explains why SVXY, a daily inverse VIX futures exposure, did not lose all its value during the sharp volatility episode of February 2018. The key distinction is between the VIX index, which is calculated from options and is not directly…

VolatilityFuturesRisk managementUS markets
Quant Q&A

This exchange clarifies how to interpret labels in the triple barrier method. Each labeled instance corresponds to a specific trading circumstance and receives a single outcome according to which event closes it: an upper barrier win, a lower barrier loss,…

Machine learningEvent-drivenBacktesting
Quant Q&A

This exchange diagnoses why a Newton–Raphson routine for Black–Scholes implied volatility diverges. The original code divides the option-price error by gamma, but implied volatility is the quantity being solved for, so the derivative needed for the update is…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The document asks whether an option can have a strike that varies randomly over time and what such products are used for. The answers point to several related structures: an Asian option uses an average of the underlying over the contract period in its…

OptionsDerivatives pricingVolatility
Quant Q&A

The document investigates why a return computed from a vendor’s adjusted closing prices differs from a hand-calculated dividend-inclusive return. The explanation focuses on the ex-dividend date: the investor’s daily return includes both the new share price…

EquitiesStatisticsBacktesting
Quant Q&A

The document considers how to simulate a variance process that can turn negative under a discretized stochastic volatility model with a variance-gamma jump term. Rather than forcing the jump component itself to be nonnegative, the answer presents ways to…

VolatilityStatisticsBacktesting
Quant Q&A

The document asks how risk-neutral pricing relates to the risk aversion assumed in expected utility theory. Its answer connects risk-neutral valuation to an assumption of complete markets and no arbitrage: under those conditions, a risk-neutral measure can…

Derivatives pricingStatistics
Quant Q&A

The document explains how binary options differ from vanilla options. A binary contract pays a fixed amount or nothing at expiry, whereas a vanilla call’s payoff grows with the underlying above its strike. The binary’s capped payoff and risk profile can suit…

OptionsDerivatives pricingRisk management
Quant Q&A

The document addresses how changes in a short-term interest rate’s yield volatility relate to price volatility, while questioning whether a proposed bond formula applies to a short-term interest rate option. The response gives the standard first-order…

Fixed incomeVolatilityDerivatives pricing