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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

21,023 documents

Quant Q&A

The document asks why volatility models used to estimate Value at Risk may pass a backtest at a 5% confidence tail while failing at a 1% tail. The response says this pattern is common: as the tail becomes more extreme, the assumed return distribution matters…

Risk managementBacktestingVolatilityStatistics
Quant Q&A

The document presents an implementation question about fitting a Heston–Nandi GARCH model by maximum likelihood. The author scales log returns, recursively computes conditional variance and standardized residuals, and uses differential evolution to search…

EquitiesVolatilityStatisticsMachine learning
Quant Q&A

The document asks how Markit defines spot dates for several currencies and how intermediate discount factors enter swap-curve bootstrapping. Its central example is a two-year EUR swap: the coupon schedule needs an 18-month discount factor, while quoted LIBOR…

Fixed incomeDerivatives pricingStatistics
Quant Q&A

The document discusses how to estimate continuous-time credit rating migration dynamics from observed rating histories. It contrasts discrete-time cohort transition matrices, formed from beginning-to-end rating changes over a period, with a continuous-time…

Fixed incomeStatisticsRisk management
Quant Q&A

The document asks how to price two claims under Black–Scholes: one paying the time integral of the asset price and another paying the squared log of its terminal price. For the integral claim, the answers use discounted risk-neutral expectation and…

Derivatives pricingOptionsStatistics
Quant Q&A

The note addresses where to find historical corporate bond yield series, especially across credit ratings and maturities. It identifies FRED as a free source for some ICE Bank of America credit index data, including a high-yield index series. Such index data…

Fixed incomeStatisticsUS markets
Quant Q&A

The note examines why the Fama–French model is often described as a risk model and how that differs from an alpha model. It presents a factor-pricing view in which expected excess returns are related to assets’ factor exposures and the premia associated with…

EquitiesFactor investingStatisticsRisk management
Quant Q&A

The document examines whether a European call butterfly, formed from three strikes, must have a strictly positive price under no arbitrage. It warns that subtracting separate lower-bound inequalities does not establish the desired result. One argument uses…

OptionsDerivatives pricingArbitrageStatistics
Quant Q&A

The document asks how to interpret the Black–Scholes price of a European equity call. It focuses on the formula’s two normal cumulative distribution terms, the stock price, and the discounted strike, and proposes reading the distribution terms as…

OptionsDerivatives pricingStatistics
Quant Q&A

The document considers principal component analysis for estimating risk in an international equity portfolio whose stocks are denominated in several local currencies. It asks whether PCA can be applied to local stock returns separately from exchange-rate…

EquitiesForexStatisticsRisk management
Quant Q&A

The document derives the stochastic dynamics of a reverse exchange rate. If the quoted rate gives domestic currency per unit of foreign currency and follows geometric Brownian motion, the reverse rate is its reciprocal, representing foreign currency per unit…

ForexStatistics
Quant Q&A

The document considers storage for real-time stock ticks and aggregation into volume clusters by time period, price, and trade side. The desired output groups buy and sell volume at each price over intervals ranging from minutes to hours. It asks which…

EquitiesMarket microstructureExecutionStatistics
Quant Q&A

This note outlines a transformation of the Black-Scholes option-pricing equation into a diffusion equation, then asks how to simulate the transformed PDE numerically. It gives the variable substitutions, the resulting heat-equation form, and a transformed…

OptionsDerivatives pricingBacktestingStatistics
Quant Q&A

The note addresses how to find aggregate market-size figures for structured products across product categories. The answerer says they do not know of a global estimate and points instead to European industry data compiled from national market figures. This…

Multi-assetStatistics
Quant Q&A

The discussion considers what risk information can be inferred from a portfolio snapshot containing prices, position values, share counts, and stock betas, but no time series. One response suggests using beta to approximate a stock’s volatility if market…

EquitiesStatisticsVolatilityPortfolio construction
Quant Q&A

The document identifies a stochastic volatility specification in which the asset follows geometric Brownian motion while volatility is the exponential of an Ornstein–Uhlenbeck process. The log volatility mean-reverts, and its driving Brownian motion may be…

VolatilityStatisticsDerivatives pricing
Quant Q&A

The document addresses whether a Student’s t distribution can represent the fat tails often observed in stock returns and how that choice relates to risk measures. It concludes that the distribution’s degrees of freedom can be used to model heavier tails…

StatisticsRisk managementEquities
Quant Q&A

The document explains why annualized volatility is commonly calculated by multiplying monthly return volatility by the square root of twelve. Under independent, identically distributed monthly log returns with finite variance, annual log return is the sum of…

VolatilityStatistics
Quant Q&A

The document explains why an attempted R calculation returned zeros: it subtracts the logarithm of each price from the logarithm of that same price. Log returns instead compare prices at different observations, commonly by taking the difference between…

EquitiesStatistics
Quant Q&A

The document describes a direct way to interpret a finite set of equally probable portfolio-return scenarios for Value at Risk. With 250 scenarios, sorting the outcomes from worst to best makes the 25th-worst outcome the 10% VaR under the convention…

Risk managementStatisticsPortfolio construction
Quant Q&A

The document explains how the stochastic discount factor prices a payoff through its expected value and its covariance with that payoff. When covariance is zero, the payoff has no exposure to the priced source of risk represented by the discount factor. Its…

StatisticsDerivatives pricingRisk management
Quant Q&A

The document explains why changing interest-rate or dividend-yield inputs can make an option implied-volatility smile appear unusual, especially for in-the-money options. Under Black–Scholes intuition, a higher interest rate raises a call’s modeled value; if…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The question investigates why a USD zero-coupon inflation swap’s fixed-leg payment can remain unchanged when its maturity date moves by a few days, then jump to the next annual compounding amount. The answer proposes interpreting the 1/1 convention through a…

Fixed incomeStatistics