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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

21,023 documents

Quant Q&A

The document asks how fractional Brownian motion (fBm) might be interpreted when it replaces ordinary Brownian motion in a Black–Scholes framework. Its answer focuses less on an economic interpretation and more on a mathematical consequence: fBm is generally…

Derivatives pricingStatisticsArbitrage
Quant Q&A

The document asks how to normalize eigenportfolio weights when some components are negative, in a setting based on statistical arbitrage in U.S. equities. It contrasts dividing each weight by the signed sum with dividing by the sum of absolute weights. The…

Portfolio constructionPairs tradingEquitiesStatistics
Quant Q&A

The document asks why historical volatility is commonly measured as the standard deviation of log returns. The author points out that a sequence of identical daily percentage changes would have constant log returns and thus zero standard deviation, even…

StatisticsVolatilityDerivatives pricing
Quant Q&A

The document examines how to estimate expiration probabilities and expected value for a two-leg put credit spread. It divides outcomes into three price regions: above the short strike, between the strikes, and below the long strike. The author asks whether…

OptionsDerivatives pricingStatistics
Quant Q&A

The document asks whether the Calmar ratio, defined as expected return divided by maximum drawdown, satisfies monotonicity as a performance measure. It contrasts Calmar with the gain-loss ratio, which the author describes as monotone, and the Sharpe ratio,…

Risk managementStatistics
Quant Q&A

The document poses a portfolio risk question about estimating value at risk when the portfolio value is a nonlinear cubic function of one normally distributed risk factor. At the stated current factor level, both the first and second derivatives are zero, so…

Risk managementDerivatives pricingStatistics
Quant Q&A

The document explores how to derive a variational or weak formulation for the Black–Scholes partial differential equation. Its central issue is integration by parts in the diffusion term, where the coefficient depends on the underlying price variable.…

OptionsDerivatives pricingStatistics
Quant Q&A

The document outlines a basic Monte Carlo procedure for estimating single-asset Value at Risk: choose a return distribution, estimate its parameters, simulate returns, revalue the position under each scenario, and read the loss quantile for the chosen…

Risk managementStatisticsBacktesting
Quant Q&A

The document considers how to turn over-the-counter FX option quotes, reported at different strikes and maturities, into a usable volatility surface. One proposed workflow starts from standard delta quotes, converts each maturity’s deltas to strikes using…

ForexOptionsVolatilityDerivatives pricing
Quant Q&A

The document derives an index’s single-period total return from security weights, price changes, and income. It rewrites each holding’s contribution as the sum of its price return and income return, then takes the weighted sum across holdings. This makes the…

Multi-assetPortfolio constructionStatistics
Quant Q&A

The document investigates a negative value appearing in a plotted down-and-out call price when the underlying starts below the barrier. It presents a Black–Scholes barrier pricing expression, defines its parameters, and compares that expression with an…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The question examines why a sinking fund example might assume that the fund earns a higher rate than the interest charged on a loan. The proposed setup keeps making interest payments to the lender while accumulating money separately to repay principal, which…

Fixed incomeRisk managementStatistics
Quant Q&A

The document explains duration times spread (DTS) as a way to express a bond’s sensitivity to relative spread changes. It decomposes each bond’s relative spread movement into a shared sector or market component and an idiosyncratic component. Multiplying…

Fixed incomeRisk managementStatistics
Quant Q&A

The document connects volatility drag to the compounding effect of a gain followed by a loss of equal magnitude. If daily returns alternate between positive and negative moves of size equal to daily volatility, their two-day compounded return is negative,…

StatisticsVolatility
Quant Q&A

The document explains that integrating Brownian motion over time is an ordinary pathwise Riemann integral, since Brownian paths are continuous. The resulting process is also an Itô process: applying Itô’s product rule to the product of time and Brownian…

Statistics
Quant Q&A

The question seeks a way to estimate the risk-neutral variance of an underlying stock’s gross return and considers option-implied distributions alongside estimates made from data under the physical measure. One reply assumes geometric Brownian motion under…

OptionsDerivatives pricingStatisticsVolatility
Quant Q&A

The document describes a Texas Instruments BA II Plus calculation of an effective annual rate from a nominal rate compounded daily. The user rounds the intermediate value shown on the calculator to five decimal places, then raises that displayed value to the…

Fixed incomeStatistics
Quant Q&A

The note addresses why implied volatilities calculated from option prices differ across strikes. It explains that Black–Scholes implied volatility is the volatility input that reproduces a given market price under the model; when the model assumes a single…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The note explains why a CDS premium leg includes an accrual-on-default term alongside scheduled spread payments. Because default can occur between coupon dates, the protection seller is owed premium for the portion of the period elapsed since the last…

Fixed incomeDerivatives pricingStatistics
Quant Q&A

The document asks why a strongly path-dependent option’s value is written as a function of the asset price, time, and an accumulated quantity. The payoff depends on a path summary defined as the time integral of a function of the asset price and time. Since…

OptionsDerivatives pricingStatistics
Quant Q&A

The discussion compares graphical machine-learning environments such as RapidMiner, KNIME, and WEKA with programming tools including R, Python, and scikit-learn. It frames the choice around platform familiarity, learning time, dataset size, need for…

Machine learningStatistics
Quant Q&A

The discussion examines how to tell whether a market-timing strategy adds skill beyond exposure to broad market returns. A strategy that is mostly long during a rising market may appear successful while offering little independent evidence of an edge. One…

BacktestingRisk managementStatisticsPosition sizing
Quant Q&A

The document asks how to compare a volatility arbitrage strategy’s Sharpe ratio with those of established funds. It describes volatility arbitrage broadly as positioning for the difference between implied and realized volatility, often by receiving implied…

VolatilityArbitrageRisk managementStatistics
Quant Q&A

The document distinguishes the CAPM’s theoretical expected-return relation from Jensen’s alpha as a measure of realized portfolio performance relative to an estimated benchmark. It presents the CAPM relation between expected excess return and beta, then…

EquitiesStatisticsFactor investing