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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
Quantopian lectures
45 documents
Binance API docs
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 compares White’s Reality Check with the Benjamini-Hochberg-Yekutieli procedure for screening a large set of backtested strategies against a benchmark. It describes the Reality Check as accounting for dependence among test statistics through…

BacktestingStatisticsMachine learning
Quant Q&A

The document presents the Gordon growth model for valuing a dividend-paying stock, with growth derived from return on equity and the retention ratio. It asks how to update intrinsic value day by day when the last annual dividend was paid the previous day.…

EquitiesStatistics
Quant Q&A

The document describes diagnosing weak convergence in Monte Carlo simulations of an option-pricing model with state-dependent volatility. The original comparison estimates expectations at two time-step sizes, but uses independent simulated paths. Sampling…

OptionsDerivatives pricingStatisticsBacktesting
Quant Q&A

The document asks how to scale returns calculated from hourly bars to create volatility-adjusted features at longer horizons, including several-hour and calendar-period returns. It specifically considers whether a multi-hour return should be divided by…

VolatilityTechnical indicatorsStatistics
Quant Q&A

The document asks how to test whether a proposed predictor forecasts weekly stock market returns when observations are formed from overlapping daily windows. Because adjacent weekly returns share days, their errors may be correlated, so a test designed for a…

EquitiesStatistics
Quant Q&A

The document explains that standard open, high, low, and close bars are constructed from transaction prices. Best bid and ask quotes, quote sizes, and aggregate volume or book-size fields do not provide the actual sequence of trades needed for true OHLC…

Market microstructureExecutionStatistics
Quant Q&A

The document examines whether compounding normally distributed values can produce leptokurtic outcomes. It models the compounded result as a product of independent, identically distributed normal variables and gives expressions for its mean, variance, and…

StatisticsEquities
Quant Q&A

The document asks how to show that a portfolio’s percentage contribution to loss equals its percentage contribution to risk, in the setting of a quantitative equity portfolio management exercise. It defines percentage contribution to risk for asset i as that…

EquitiesPortfolio constructionRisk managementStatistics
Quant Q&A

The document presents Gyöngy’s theorem as a way to construct a local volatility process whose one-time probability distributions match those of a given stochastic process. In the stated result, the local drift and variance are conditional expectations of the…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

A moving average period counts observations in the data series being used. For a five-period EMA calculated from 15-minute candles, the period refers to five candles, not five calendar days. The candle interval therefore determines the time span represented…

Technical indicatorsStatistics
Quant Q&A

The document asks whether the process driven by an imaginary multiple of real Brownian motion has negative variance. The answer distinguishes the process’s imaginary-valued increments from its variance: at time t, the process is an imaginary multiple of…

StatisticsVolatility
Quant Q&A

The document explains how to represent a Wiener process on a uniform time grid for simulation. Divide the interval into equal steps, then construct each sampled value by adding the next increment to the previous value, starting from zero. The increment over…

StatisticsMachine learningBacktesting
Quant Q&A

The document asks how the return of a function of an asset changes when the underlying asset’s drift rises by a constant, assuming the asset follows geometric Brownian motion. It applies Itô’s lemma to the function, separating the stochastic term from the…

StatisticsVolatilityDerivatives pricing
Quant Q&A

The document explains that 3-month Euribor is generally quoted as an annualized rate for clarity and comparison with other interest rates. ACT/360 specifies the day-count convention used to calculate interest: the actual number of days in the accrual period…

Fixed incomeStatistics
Quant Q&A

The answer distinguishes preferences for return skewness according to an investor’s objective. It says that maximizing expected log wealth can favor higher odd moments, such as mean return and skewness, while favoring lower even moments, such as volatility…

StatisticsRisk managementVolatility
Quant Q&A

The document asks which machine learning and statistical methods candidates should prepare for quantitative researcher and developer interviews at hedge funds and proprietary trading firms. It situates the question alongside probability puzzles and coding…

Machine learningStatistics
Quant Q&A

The document asks which partial differential equation is satisfied by an expectation of a function evaluated at a Wiener-driven, exponentially transformed variable. The proposed derivation differentiates the expectation and applies Itô’s rule to the…

StatisticsDerivatives pricing
Quant Q&A

The document compares two ways to measure a relationship between two stocks for pairs trading. A price ratio represents a dollar-neutral position: the trader invests equal dollar amounts in each asset. A regression-based spread instead uses a hedge ratio,…

EquitiesPairs tradingStatisticsRisk management
Quant Q&A

The document gives an intuitive argument for uniqueness of the risk-neutral probability measure in the Black–Scholes setting. It invokes Arrow–Debreu claims, each paying one unit in a particular state. If the same claim must have the same arbitrage-free…

OptionsStatisticsRisk management
Quant Q&A

The document explains why a bond forward’s impliedYield method does not directly return the yield of a forward-starting bond. In the example, applying it to the spot bond price and clean forward price produces a curve zero rate, because the forward price…

Fixed incomeDerivatives pricingStatistics
Quant Q&A

The document examines a potential domain issue in a paper on utility maximization in finite probability spaces. It describes a conjugate function defined for strictly positive arguments, then questions a later expression that evaluates the function at a…

Derivatives pricingStatisticsRisk management
Quant Q&A

The document derives an expression for the expected log return over a time interval under a local volatility stochastic differential equation. Applying Itô’s lemma gives a drift contribution and a negative half-variance contribution; the expected stochastic…

VolatilityStatisticsDerivatives pricing
Quant Q&A

The document asks how to calculate the variance of a discounted European call or put payoff under the Black–Scholes assumptions. Its answer outlines a second-moment approach: variance equals the expected squared payoff minus the square of its expected value.…

OptionsDerivatives pricingStatistics