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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 considers how to estimate a high percentile of absolute daily returns when the available sample is short and the desired tail quantile lies beyond what the observations can reliably show. Because the estimate requires extrapolation, it…

Risk managementStatisticsVolatility
Quant Q&A

The question compares a probability shown by the CME FedWatch Tool with a calculation based on a fed funds futures price, the effective rate for part of the month, and the number of days affected by a rate decision. The author finds that the calculation…

Fixed incomeFuturesUS marketsStatistics
Quant Q&A

The document asks how to derive the conditional expectation of the future integral of squared standard Brownian motion using Itô’s formula and a partial differential equation. The key correction is that this conditional expectation is not itself a…

StatisticsDerivatives pricing
Quant Q&A

The document asks how a long straddle could produce positive profit when the underlying finishes at the strike and the option expires worthless, despite high volatility during the holding period. One explanation is dynamic delta hedging: as the underlying…

OptionsVolatilityDerivatives pricing
Quant Q&A

The document considers how to construct dollar bars when only minute OHLCV data is available. One proposal estimates each minute’s traded value by multiplying volume by an average price, then combines minutes until their estimated dollar value reaches a…

StatisticsBacktestingMarket microstructure
Quant Q&A

The document poses a question about the relationship between instantaneous forward rates and the short rate in a zero-coupon bond framework. It writes the bond price in two forms: as the exponential of the negative integral of forward rates, and as the…

Fixed incomeStatistics
Quant Q&A

The document discusses errors encountered when using an R interface to Ox for fitting GARCH models, including FIGARCH and possibly FIEGARCH specifications. The reported failures include undeclared file input and output functions in an Ox source file and a…

VolatilityStatisticsEquities
Quant Q&A

The document considers how to evaluate a multi-agent stock market simulation against historical prices for large companies across several sectors. The question proposes matching the volatility of simulated prices to observed volatility while also checking…

EquitiesVolatilityTechnical indicatorsStatistics
Quant Q&A

The document recommends *Active Portfolio Management* by Richard Grinold and Ronald Kahn as a possible preparation for graduate study in asset pricing and portfolio choice. The original question asks for a book that is accessible to a beginner yet rigorous…

Portfolio construction
Quant Q&A

The document examines whether a discounted option value in the Black–Scholes model is a martingale. Applying Itô’s formula and the Black–Scholes partial differential equation leaves a stochastic integral whose integrand contains the stock price and the…

Derivatives pricingOptionsStatistics
Quant Q&A

The document asks how to calculate an ex-ante Sharpe ratio from simulated return paths: from compounded total returns across paths, or by calculating a Sharpe ratio along each path and averaging those ratios. The response emphasizes that the conventional…

StatisticsBacktesting
Quant Q&A

The document discusses whether option delta can approximate the probability of profit for multi-leg positions. Its example uses a call debit spread: the response treats the short strike’s delta as a rough chance of reaching the spread’s maximum gain and the…

OptionsDerivatives pricingRisk managementStatistics
Quant Q&A

The document considers how to hedge a leveraged liability whose value depends on a basket of two assets and U.S. dollars. It questions whether a hedge based on the assets’ historical correlation is reliable, especially if that relationship changes. The…

Risk managementPortfolio constructionStatistics
Quant Q&A

The document asks whether range-based estimators such as Garman–Klass can be combined with EWMA or ARCH-family models, and whether a dynamic volatility model is still needed when using such estimators. The response points to research that incorporates…

VolatilityStatisticsBacktesting
Quant Q&A

The document considers how to choose both a signal threshold and the future return horizon when evaluating a trading hypothesis. It warns that searching many combinations can amount to data mining and produce overfit results, while acknowledging that there…

BacktestingStatisticsMachine learning
Quant Q&A

The document derives the dynamics of EUR/USD from a model for USD/EUR. It assumes the USD/EUR exchange rate follows geometric Brownian motion with drift r and volatility σ, then represents the reciprocal exchange rate as 1/S. Applying Itô’s formula to that…

ForexStatistics
Quant Q&A

The document works through the moving-average representation of fractional Brownian motion and asks how to simplify the variance of an increment between two ordered times. It splits the stochastic integral into regions before the first time and between the…

Statistics
Quant Q&A

The document raises a research concern: option pricing models may be proposed without being tested against market data or compared with established alternatives. It notes that affordable options data can be difficult to obtain, while arguing that empirical…

OptionsDerivatives pricingBacktesting
Quant Q&A

The document asks whether stock returns can be rescaled to look more normally distributed and describes an attempt to reproduce a published method on S&P 500 returns. The approach clips each return at a multiple of an evolving volatility estimate, updates…

EquitiesStatisticsVolatilityTechnical indicators
Quant Q&A

This document describes a simulation problem: generating several correlated variables from a Sobol quasi-random sequence in MATLAB. The author contrasts a Cholesky-based transformation of standard normal draws with an attempted method that transforms Sobol…

StatisticsMachine learningBacktesting
Quant Q&A

This discussion addresses the difficulty of finding dependable Chinese economic indicators for strategy research. The question lists areas of interest including credit, materials, durable goods, GDP composition, government spending, housing, and personal…

China marketsStatisticsCommodities
Quant Q&A

This document presents a method for estimating the parameters of an Ornstein–Uhlenbeck process from an equally spaced historical time series. It defines the process through a mean-reverting rate, a long-run level, and a volatility term, then gives formulas…

StatisticsMean reversionForexFixed income
Quant Q&A

The document surveys software options for developing and evaluating trading strategies in C++. It names QuantLib, Boost, Armadillo, Intel MKL, Eigen, and TA-Lib for quantitative, numerical, and technical-analysis work. It also mentions TradeStation for…

BacktestingRisk managementTechnical indicators
Quant Q&A

The discussion concerns setting settlement days explicitly when constructing QuantLib swap rate helpers for a USD LIBOR curve. It notes that the default can come from the index’s fixing days, and the original question asks how to use zero settlement days…

Fixed incomeDerivatives pricing