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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

20,364 documents

Quant Q&A

The document poses a problem about finding a high quantile for the sum of two dependent random variables. Each variable is modeled with an extreme value distribution, and their dependence is represented by a Gumbel copula. The question highlights a common…

StatisticsRisk management
Quant Q&A

The document outlines a multi-curve approach to building the euro three-month forward curve alongside the OIS discount curve. It identifies short-dated futures, swaps at longer maturities, and LIBOR–OIS basis swaps as market instruments used in the process.…

Fixed incomeFuturesDerivatives pricing
Quant Q&A

The document addresses whether the time integral of squared Brownian motion is a random variable and how to find its first two moments. It computes the expectation by interchanging expectation and integration, then using the fact that standard Brownian…

Statistics
Quant Q&A

The document shows how to calculate a cohort-level single monthly mortality rate (SMM) from loan-level data. The cohort SMM is the total prepaid monthly balance divided by the total scheduled balance across the loans in that cohort. Equivalently, it is a…

Fixed incomeStatistics
Quant Q&A

The document explains the difference between credit default swaps and recovery swaps in the context of loan default risk. A CDS provides protection when a defined credit event occurs, such as a borrower’s failure to make a coupon payment. The protection…

Fixed incomeDerivatives pricingRisk management
Quant Q&A

The document distinguishes returns from cumulative returns and explains how the definition of the cumulative window affects stationarity. A cumulative return measured from a fixed starting date accumulates variation over time, so even when the underlying…

StatisticsEquitiesFixed incomeRisk management
Quant Q&A

Fast stochastic %K and Williams %R both measure a closing price’s position within the high-low range over a chosen lookback window. The document defines %K as the close’s distance above the low, scaled to a 0–100 range, and %R as the distance below the high,…

Technical indicatorsStatistics
Quant Q&A

The document frames numerical solution of a nonlinear pricing PDE for a derivative under the uncertain volatility model. Volatility is selected from a lower or upper bound according to the sign of the solution’s second spatial derivative, so the PDE is…

Derivatives pricingVolatilityStatistics
Quant Q&A

The discussion explains why estimating returns and covariances from historical prices for Modern Portfolio Theory (MPT) does not, by itself, contradict the weak Efficient Market Hypothesis (EMH). MPT prescribes how to allocate assets given expectations about…

Portfolio constructionStatisticsEquitiesRisk management
Quant Q&A

The discussion presents stochastic portfolio theory (SPT) as a framework for portfolio growth that accounts for asset variances and co-movements. It contrasts arithmetic expected returns with long-run logarithmic growth, explaining that volatility can reduce…

Portfolio constructionStatisticsPosition sizingEquities
Quant Q&A

The discussion distinguishes a derivative’s contractual cash flows from the funding burden a dealer may face while holding and hedging an uncollateralized position. A forward entered at zero value can later become an asset or liability without requiring the…

Derivatives pricingRisk managementFixed income
Quant Q&A

The note examines whether Rebonato’s swaption volatility approximation works only near the money. The questioner compares Black prices using the approximation with Monte Carlo prices, reporting larger discrepancies for strikes farther from at-the-money in…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The document connects the elliptic form of the Heston stochastic volatility operator to a boundary value problem with Dirichlet conditions. It explains that solutions to obstacle problems for this operator can represent value functions for perpetual…

OptionsDerivatives pricingVolatilityFixed income
Quant Q&A

The discussion considers software requirements for currency arbitrage, including fast market-data updates, processing many combinations, exchange connectivity, account support, and algorithm scripting. The replies caution that FX markets are not generally…

ForexArbitrageExecutionMarket microstructure
Quant Q&A

The note derives a Monte Carlo estimator for a European log-contract whose terminal payoff is the logarithm of the asset price relative to its initial price. Under risk-neutral Black–Scholes dynamics, Itô’s lemma gives a lognormal terminal asset price, which…

OptionsDerivatives pricingStatisticsBacktesting
Quant Q&A

The document considers why the Black–Scholes stock-price expression includes volatility while its expected price growth appears independent of volatility. It asks whether this means investors should accept a risky asset whose expected return equals the…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The document raises a measurement concern for historical tests of covered interest rate parity (CIP). It notes that interbank forward quotes are commonly expressed against the US dollar, with a stated exception, and asks whether inconsistencies between…

ForexArbitrageCarryMarket microstructure
Quant Q&A

The document asks whether the Poisson-mixture formula used to price European options under Merton’s jump-diffusion model can also price American options. For European contracts, the cited formula weights Black–Scholes prices adjusted for the number of jumps…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The document explains how to interpret a table reporting the risk of a dynamically delta-hedged option position. It describes simulating stock-price paths at the stated weekly frequency, computing the option delta at each step, and adjusting the share…

OptionsDerivatives pricingVolatilityRisk management
Quant Q&A

The document considers whether historical VaR for a single instrument can be extended to a one-year horizon. The main answer warns that long-horizon estimates are highly sensitive to the assumed or estimated mean return. Since drift is difficult to estimate…

Risk managementStatisticsBacktesting
Quant Q&A

The document collects suggested reading for an internship in model risk management focused on counterparty credit risk, especially exposure from derivatives. Its references cover modeling, pricing, and hedging counterparty exposure; credit valuation…

Derivatives pricingRisk managementOptionsFixed income
Quant Q&A

The document asks which unsupervised learning methods can help discover patterns in manually selected financial time-series features, with the aim of making broad predictions about future price behavior. It gives examples ranging from predicting the next…

Machine learningStatisticsEquities
Quant Q&A

The document discusses how to compare the VIX with realized volatility calculated from daily S&P 500 index prices. The questioner computes rolling volatility from log returns, annualizes it using the square root of 252, and asks whether a 30-day or roughly…

VolatilityOptionsUS marketsStatistics
Quant Q&A

The document proposes modeling a positive process by making its logarithm an Ornstein–Uhlenbeck process. In this construction, the log value mean-reverts toward a constant level, so the corresponding process is positive and its long-run central level is the…

StatisticsMean reversionVolatility