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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

21,023 documents

Quant Q&A

The document asks how the survival probability of a default time relates to its hazard rate, and why a conditional survival probability has a shifted hazard integral. It presents the standard unconditional relation as an exponential of the negative…

Fixed incomeStatisticsRisk management
Quant Q&A

The document explains how to convert fractional betting odds into implied probabilities. For odds stated as a to b, it gives the conversion b divided by the sum of a and b. Applying that formula to the quoted odds yields implied chances of 0.652 for remain…

StatisticsEvent-driven
Quant Q&A

The document asks when Monte Carlo discretization is needed in financial modeling, given that geometric Brownian motion can be simulated exactly at selected dates. The responses name the LIBOR market model, Heston stochastic volatility, and local volatility…

Derivatives pricingOptionsStatisticsBacktesting
Quant Q&A

The document explains why rates and discount factors derived from a QuantLib zero-coupon curve may differ from input deposit yields. The example builds a curve from deposit rate helpers and compares calculated forward rates and discount factors with quoted…

Fixed incomeDerivatives pricingStatistics
Quant Q&A

The document asks how to value a one-period payoff equal to the higher of two stock prices and whether stochastic calculus is necessary. For jointly normal prices, the accepted answer rewrites the maximum as one stock plus a call-like payoff on the…

EquitiesOptionsDerivatives pricingStatistics
Quant Q&A

The document considers ways to assemble a spreadsheet comparing selected public companies across fields such as headquarters, market capitalization, earnings, revenue, free cash flow, debt, and cash. It describes a programmatic route using a finance data API…

EquitiesFactor investingStatistics
Quant Q&A

The document discusses ways to reduce sampling noise when simulating a Cox–Ingersoll–Ross interest rate process for option valuation. It recommends quasi-random sequences, with Niederreiter sequences given as an example, and control variates, suggesting a…

Fixed incomeOptionsDerivatives pricingStatistics
Quant Q&A

The document explains how least-squares Monte Carlo (LSM) values an American option and identifies when exercising is preferable to continuing to hold it. Working backward from terminal payoff, the method estimates each time step’s discounted continuation…

OptionsDerivatives pricingMachine learningStatistics
Quant Q&A

The document discusses a worked example from research on price impact, where market sells consume bid depth and limit bid additions replenish it. The response interprets the sequence in ticks: selling 15 shares against depth of 5 removes three levels, while…

Market microstructureExecutionStatistics
Quant Q&A

For a stock modeled by geometric Brownian motion, the document explains how to find the chance that its future price falls below a specified threshold. The logarithm of the price is normally distributed, with a mean adjusted for the volatility term and…

StatisticsEquitiesVolatility
Quant Q&A

The document asks how to define a stock that fades through the day in a way that can be measured. An open-to-close decline alone may miss a rise earlier in the session followed by a sharp late drop. The questioner suggests requiring an early high of day that…

EquitiesStatisticsMomentumBacktesting
Quant Q&A

The document surveys approaches to estimating time-varying dependence with copulas in R. It points to vine copulas with evolving parameters as one research direction and mentions the CDVine package and related materials for statistical inference. It also…

StatisticsMachine learningPortfolio construction
Quant Q&A

The document raises two questions about reproducing a hidden Markov model study of factor investing across market regimes. First, it asks whether the reported regime labels and their return and volatility summaries use future observations to classify past…

Machine learningStatisticsBacktestingUS markets
Quant Q&A

The document explains why differentiating a Black–Scholes option price with respect to time does not include a derivative of the stock price. The option pricing formula treats time and spot price as separate arguments: a partial derivative with respect to…

OptionsDerivatives pricingStatistics
Quant Q&A

The document asks how to connect risk-neutral moments of simple gross returns to moments of log returns under a Black–Scholes model. It applies Itô’s lemma to obtain the stock’s terminal value, then uses the moment-generating function of a normal variable to…

Derivatives pricingOptionsVolatilityStatistics
Quant Q&A

The document resolves an apparent paradox: an American put’s value is at least its immediate exercise payoff, yet early exercise can still be optimal. The option value is the greater of immediate exercise value and the best expected discounted payoff from…

OptionsDerivatives pricingRisk managementStatistics
Quant Q&A

The document discusses reducing Monte Carlo pricing variance for a European put under a stochastic volatility simulation. The questioner uses the terminal stock price as a control variate and reports a modest reduction in the estimated standard error after…

OptionsStatisticsBacktestingDerivatives pricing
Quant Q&A

The document considers how to fill gaps in cumulative default probability data across maturities and credit ratings. One proposed framework is to choose a probability matrix that stays close to a target, such as a calibrated estimate or a smoothness…

Fixed incomeStatisticsRisk management
Quant Q&A

Yield curve PCA needs observations on a consistent tenor grid, but some markets have quotes at only a few maturities. The note compares running PCA on the sparse observed points with interpolating or extrapolating rates to create a fuller grid. The choice of…

Fixed incomeStatisticsRisk management
Quant Q&A

The document asks how to obtain a conditional variance-covariance matrix from an estimated MGARCH-BEKK model fitted to spot and futures price series. It presents a two-asset BEKK-style recursion built from a constant matrix, lagged return residuals, and the…

FuturesCommoditiesStatisticsRisk management
Quant Q&A

The document addresses how traders might identify market conditions associated with changes in algorithmic strategy performance. It proposes structural-break analysis as one avenue, naming CUSUM tests on recursive residuals or levels and explosiveness tests…

Machine learningStatisticsRisk managementPosition sizing
Quant Q&A

The answer considers whether market betas and rolling-window beta estimates should be normally distributed when asset and market returns are assumed normal. It distinguishes beta estimated from raw returns from beta estimated using log returns, and argues…

EquitiesStatisticsRisk management
Quant Q&A

The document asks how a biased random walk converges to a process with nonzero drift as its time step shrinks. It starts from the expected displacement after repeated up-or-down steps, then considers steps of size proportional to the square root of the time…

StatisticsVolatility
Quant Q&A

The document explains the origin and limits of a simple approximation that relates implied variance at a strike and maturity to local variance near the initial spot and the strike at expiry. It places this shortcut alongside short-maturity results: the…

OptionsVolatilityDerivatives pricingStatistics