Skip to content

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

2,738 documents

Quant Q&A

The discussion distinguishes uncertainty in portfolio allocations from uncertainty in the inputs used to construct them. Mean-variance optimization can produce a precise allocation from estimated returns and covariances even when those parameters are poorly…

Portfolio constructionStatisticsRisk managementBacktesting
Quant Q&A

The document describes a backward path-integral scheme for pricing an American put on a log-price grid. At each time step, it discounts and integrates the next-step option value against a Gaussian propagator for log prices, then applies the early-exercise…

OptionsDerivatives pricingBacktestingStatistics
Quant Q&A

The document collects suggestions for obtaining historical index membership and constituent prices at monthly intervals. It points to professional data terminals and services, including Bloomberg, where index members can be queried with a date override and…

EquitiesUS marketsBacktesting
Quant Q&A

The thread addresses implementation questions for two-step estimation of a dynamic conditional correlation GARCH model. In the second-stage likelihood, the log of the determinant of the conditional correlation matrix is a scalar, as is the quadratic form…

VolatilityStatisticsBacktesting
Quant Q&A

The discussion distinguishes two research questions that can look similar but require different outcomes. To compare volatility estimators as forecasting inputs, regress a later realized-volatility measure on each estimator available at the forecast date. A…

VolatilityStatisticsBacktesting
Quant Q&A

The discussion compares evaluating a strategy through trade or portfolio returns with simulating a starting capital amount and measuring ending equity or annualized return. It argues that the appropriate view depends on the strategy and how closely the…

BacktestingRisk managementPosition sizingExecution
Quant Q&A

The document presents a QuantLib Python calibration attempt for a time dependent Heston model that fails with a Boost assertion. The code builds a volatility surface, creates a piecewise time dependent model, attaches an analytic pricing engine, and…

OptionsVolatilityDerivatives pricingMachine learning
Quant Q&A

The document explains why counting losses beyond Value at Risk on the same sample used to estimate the quantile cannot validate a VaR model. For a historical VaR estimate based on past profit and loss observations, the proposed approach is to use a rolling…

Risk managementBacktestingStatistics
Quant Q&A

The document examines a reported average duration of roughly 25 minutes for continuous ETH price rises or falls, measured in five-minute intervals over three months. The response recommends defining what would count as unusual and comparing the observation…

CryptoStatisticsBacktestingMarket microstructure
Quant Q&A

The document compares two ways to generate paired Brownian increments with a specified negative correlation and time-step variance. One approach draws independent standard normal samples and transforms one using the target correlation; the other draws…

StatisticsBacktesting
Quant Q&A

The responses survey reinforcement learning (RL) applications in quantitative finance, with portfolio allocation as the main example. They describe critic-only methods, which choose actions using learned value estimates; actor-only methods, which optimize…

Machine learningPortfolio constructionBacktestingExecution
Quant Q&A

The note derives an unconditional-expectation form of expected shortfall from its definition as the negative conditional mean of returns in the loss tail. It uses the indicator of the event that a return falls below the VaR threshold, then applies the…

Risk managementStatisticsBacktesting
Quant Q&A

The document addresses Monte Carlo valuation of a call option on a zero-coupon bond under the Vasicek short-rate model. It first challenges the question’s stated closed-form benchmark, deriving a bond-option price using the Vasicek bond pricing function and…

Fixed incomeOptionsDerivatives pricingBacktesting
Quant Q&A

The document compares two ways to scale daily trading profit and loss: dividing by the previous day’s gross portfolio value or by the account’s initial equity. These choices describe different things. The prior-day value expresses each day’s gain relative to…

BacktestingStatisticsPortfolio construction
Quant Q&A

The document addresses how a technically capable beginner can move from trading infrastructure and market knowledge toward designing strategies. It describes strategy as a broad category, ranging from simple rules based on price gaps to models using…

BacktestingMarket microstructureMachine learningStatistics
Quant Q&A

The document explains how to test whether an event day produced abnormal stock returns across a group of companies. It uses a market model fitted over an estimation window, then defines the daily average abnormal return as the cross-sectional mean across the…

Event-drivenEquitiesStatisticsBacktesting
Quant Q&A

The document describes a QuantLib calibration problem for the G2++ interest-rate model in a negative-rate environment. The reported error arises because the cap helper uses shifted lognormal volatility with zero displacement, which requires the strike plus…

Fixed incomeDerivatives pricingOptionsBacktesting
Quant Q&A

The document describes how to enumerate every sequence of up, middle, and down moves in a trinomial tree. Its example uses recursive depth-first search: extend a partial path with each of the three moves until the desired number of steps is reached, then…

BacktestingStatistics
Quant Q&A

The accepted answer explains how to minimize conditional value-at-risk, also called expected tail loss, using a scenario-based linear program. It introduces portfolio weights, a variable representing the value-at-risk threshold, and one auxiliary variable…

Portfolio constructionRisk managementStatisticsBacktesting
Quant Q&A

The document describes a simulation designed to compare covariance transformations for minimum-variance portfolio construction. For each lookback window, the author samples portfolios of 100 assets, estimates a sample covariance matrix, transforms it, and…

Portfolio constructionStatisticsRisk managementBacktesting
Quant Q&A

The document frames an out-of-sample estimation question for a cointegration pairs strategy. In sample, the proposed workflow applies the Engle–Granger two-step procedure, estimates a hedge coefficient for the spread, and standardizes that spread using its…

Pairs tradingMean reversionStatisticsBacktesting
Quant Q&A

The discussion collects several ways to transform stock prices for analysis. Suggested measures include log prices, price deviations from a mean, standardized deviations using a standard deviation, log-price deviations from a mean, log returns, percentage…

EquitiesStatisticsPairs tradingBacktesting
Quant Q&A

The discussion considers a daily strategy that holds positions for one day while using an indicator built from a five-year price history. Because adjacent indicator readings share much of the same input data, they are strongly serially dependent. The…

BacktestingTechnical indicatorsStatistics
Quant Q&A

The document asks which risk-free rate to use when constructing a maximum Sharpe ratio portfolio from a rolling estimation window of monthly returns. It frames the problem within mean-variance portfolio theory, where the Sharpe ratio measures expected…

Portfolio constructionBacktestingRisk managementFixed income