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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

16,761 documents

Quant Q&A

The document considers how to measure the fit of a portfolio or linear combination of assets intended to reproduce another asset’s returns. It notes a weakness of relative error: dividing by the target return becomes unstable when that return is near zero.…

StatisticsPortfolio constructionBacktesting
Quant Q&A

The document poses a statistical question about annualized Sharpe ratios. It starts from the convention of multiplying a daily Sharpe ratio by the square root of the number of trading days in a year, under certain assumptions, and asks whether that scaling…

StatisticsRisk managementBacktesting
Quant Q&A

The document explains how the time increment in a simulated Heston model relates to the number of steps per year: once the time unit is fixed, choosing one determines the other. Its main example is the Euler–Maruyama discretization of the stock and variance…

OptionsVolatilityDerivatives pricingBacktesting
Quant Q&A

The document explains why stock positions remain open overnight even when some day traders prefer to close before the market shuts. Long-horizon investors and institutions—including mutual funds, private individuals, and bank portfolios—may hold shares as…

EquitiesExecutionStatisticsBacktesting
Quant Q&A

The document outlines a validation approach for a finite-difference implementation of the Libor Market Model. It recommends comparing European swaption prices across at-the-money and in-the-money or out-of-the-money strikes with prices from a zero-shift SABR…

Fixed incomeOptionsDerivatives pricingBacktesting
Quant Q&A

The Longstaff–Schwartz method prices American options by simulating possible asset paths and using regression to estimate continuation values. The discussion notes that simulation can accommodate options whose value depends on multiple factors, so the method…

OptionsDerivatives pricingStatisticsBacktesting
Quant Q&A

The document compares two ways of combining the annualized returns of a two-asset portfolio. Weighting each asset’s annualized return assumes the portfolio weights are maintained over time through rebalancing. Weighting the assets’ cumulative returns instead…

Portfolio constructionStatisticsBacktesting
Quant Q&A

The document surveys the trade-offs involved in selecting an optimizer for portfolio allocation problems with multiple assets or risk factors, varied objectives, constraints, and simulation noise. It describes numerical search as a balance between…

Portfolio constructionRisk managementStatisticsBacktesting
Quant Q&A

The document asks whether the profit or loss of a Black-Scholes delta-hedged option depends only on realized volatility at expiry or also on its path. It references a hedge-error expression involving gamma exposure and the difference between implied variance…

OptionsVolatilityRisk managementBacktesting
Quant Q&A

The document compares two ways to obtain future distributions and price claims from an Itô process. A Fokker–Planck equation evolves the probability density through a deterministic partial differential equation, while Monte Carlo estimates outcomes from…

OptionsDerivatives pricingStatisticsBacktesting
Quant Q&A

The document considers how to compare two optimized portfolios whose returns are positively skewed and have positive kurtosis. The author asks whether to estimate each portfolio's alpha against value-weighted and equally weighted benchmarks, or regress one…

StatisticsPortfolio constructionRisk managementBacktesting
Quant Q&A

The document surveys ways to obtain long-history intraday data for S&P 500 instruments, including index or ETF prices, futures, and options. The responses describe commercial vendors, exchange data shops, academic databases, and hosted research platforms.…

EquitiesFuturesOptionsMarket microstructure
Quant Q&A

The document considers how to track exit risk for a portfolio of direct investments in real estate, infrastructure, and private equity. It proposes treating the ability to sell as an option: the hypothetical premium for a right to exit could serve as a cost…

OptionsRisk managementFixed incomeBacktesting
Quant Q&A

The document discusses implicit equity trading costs and the market conditions that shape them. A central explanation is adverse selection: market makers facing informed traders risk trading at prices that will soon move against them. They may respond by…

EquitiesExecutionMarket microstructureVolatility
Quant Q&A

The document discusses how to interpret and empirically assess Geometric Brownian Motion (GBM). Under GBM, log returns follow a normal distribution whose mean combines the drift and a volatility adjustment. The drift is therefore a model parameter: its…

EquitiesStatisticsBacktestingMachine learning
Quant Q&A

The document discusses how to choose regression basis functions in the Longstaff–Schwartz least squares Monte Carlo method for valuing American-style options, including real options with multiple risk factors. The question is whether the few Laguerre…

OptionsDerivatives pricingStatisticsBacktesting
Quant Q&A

The document questions the assumption behind conventional strategy optimization: that the process generating historical market data remains stable. It notes that out-of-sample checks and periodic reruns can help address overfitting, but may not resolve the…

BacktestingStatisticsMachine learning
Quant Q&A

The document describes a comparison between intraday VIX-style implied volatility and realized variance computed from one-minute prices aggregated into hourly intervals. The implied measure is calculated from option prices for two maturities around 30 days,…

VolatilityStatisticsOptionsBacktesting
Quant Q&A

The discussion considers GPU selection for parallel quantitative finance workloads, with the original question asking whether hardware should favor multiplication over addition. One answer suggests that for MATLAB or Python on a single machine, the…

Multi-assetExecutionBacktesting
Quant Q&A

The document shows how to estimate geometric Brownian motion parameters from a series of daily prices. It first maps prices to log returns, which follow a normal process with drift equal to the price process’s drift minus half the variance rate. When the…

StatisticsVolatilityBacktesting
Quant Q&A

The document explains convertible bond arbitrage as buying a convertible bond while shorting the issuer’s stock to create a delta-hedged, long-volatility position. The strategy seeks to benefit when realized volatility exceeds the bond’s implied volatility,…

ArbitrageOptionsEquitiesVolatility
Quant Q&A

The document shows why subtracting calendar dates does not reliably locate observations in daily financial data. Weekends and market holidays create gaps, so the number of elapsed days can differ from the number of rows between two trading dates. The example…

EquitiesStatisticsBacktestingTechnical indicators
Quant Q&A

The document explains XIRR as the annualized rate that equates a dated series of irregular cash flows with an equivalent constant-rate investment. Its examples contrast a simple loan with interest paid at different dates and a short holding-period trade,…

StatisticsBacktestingRisk management
Quant Q&A

The document discusses reports of inconsistent Yahoo Finance downloads after changes to its data access method. The answer describes a retrieval process that relied on extracting a token from a webpage, along with reported irregularities in adjusted-close…

EquitiesBacktesting