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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

1,414 documents

Quant Q&A

The document distinguishes futures open interest from the physical production of a commodity. Open interest counts outstanding contracts, with a long and a short side for each contract; it does not represent a quantity of grain that buyers can collectively…

FuturesCommoditiesMarket microstructure
Quant Q&A

The document asks for references and a maximum-likelihood method to estimate parameters for two correlated Ornstein–Uhlenbeck processes from observed data. The question raises using a multivariate likelihood and the mean and variance properties of an OU…

StatisticsCommoditiesMean reversion
Quant Q&A

The document describes an attempt to price a commodity forward under a Schwartz mean-reverting process by discretizing its pricing PDE with an explicit finite-difference scheme. It compares numerical results with a stated analytical solution and observes…

CommoditiesDerivatives pricingMean reversionBacktesting
Quant Q&A

The document compares methods for joining expiring futures contracts into continuous price histories, with attention to how roll adjustments affect chart interpretation and backtests. Difference, or Panama, adjustment adds or subtracts the price gap between…

FuturesBacktestingMarket microstructureCommodities
Quant Q&A

The discussion explains how to infer implied volatility for an American-style option by repeatedly pricing it with a binomial model and adjusting volatility until the model value matches the observed option price. Bisection can be used in the same way as for…

OptionsCommoditiesDerivatives pricingStatistics
Quant Q&A

The document raises questions about modeling oil spot price log returns with an ARMA mean equation and conditional volatility models such as GARCH, APARCH, and EGARCH. It asks whether a forecasting function returns expected returns, volatility, or both, and…

CommoditiesVolatilityStatistics
Quant Q&A

The document collects possible sources for historical end-of-day foreign exchange prices, including broker quote archives, a free exchange-rate service, central-bank publications, and a commercial historical-price provider. It emphasizes that a daily FX…

ForexCommoditiesStatistics
Quant Q&A

The document outlines how to investigate whether macroeconomic indicators such as growth, inflation, and monetary policy help forecast silver prices. It recommends first considering the economic relationships, timing, and functional form linking those…

CommoditiesStatisticsMachine learningBacktesting
Quant Q&A

The document asks where to obtain daily prices for commodity futures across contract maturities, including each contract’s time to expiration. The data is intended to calibrate a one-factor commodity model based on Schwartz (1997): log spot price follows a…

CommoditiesFuturesMean reversionStatistics
Quant Q&A

The document explores stochastic models for commodity spot prices and forward term structures in discounted cash flow and contingent-claim valuation. It identifies a challenge with geometric Brownian motion and exponential Ornstein-Uhlenbeck approaches:…

CommoditiesFuturesMean reversionDerivatives pricing
Quant Q&A

The document explains how to choose a futures contract to hedge a commodity exposure, using natural gas as an example. It distinguishes next-day spot from near-term delivery positions: a longer-dated future may be a poor hedge for immediate spot exposure,…

CommoditiesFuturesRisk managementPosition sizing
Quant Q&A

The document asks how to calibrate Monte Carlo simulations for value at risk on a portfolio of energy futures. The positions include gasoline and oil products such as ultra-low-sulfur diesel and West Texas Intermediate, across multiple delivery months. The…

FuturesCommoditiesRisk managementStatistics
Quant Q&A

The question considers forecasting energy costs far into the future from annual observations. It proposes estimating drift and volatility from log returns, then extending a geometric Brownian motion approach with recent observations weighted more heavily and…

CommoditiesStatisticsBacktesting
Quant Q&A

The document offers preparation advice for an upcoming quantitative trading internship involving research and backtesting across markets. It recommends finding out which programming language the firm uses and gaining familiarity with that environment, since…

BacktestingEquitiesForexCommodities
Quant Q&A

The document outlines an attempt to reproduce the Dow Jones-UBS Commodity Index from constituent futures prices. The described calculation applies annual commodity index multipliers to dollar-denominated prices, sums the products, and uses the change in that…

CommoditiesFuturesBacktesting
Quant Q&A

The document addresses how to estimate and forecast a seasonal ARIMA model with GARCH errors for daily electricity prices, including a holiday indicator in the mean equation. The question arises because the R base ARIMA function supports seasonal terms,…

CommoditiesStatisticsVolatilityBacktesting
Quant Q&A

The discussion asks for practical examples of Asian or other exotic options so that pricing approaches can be compared with actual market arrangements. It identifies exchange-traded Asian-style options linked to WTI crude oil futures as one example, and…

CommoditiesOptionsDerivatives pricing
Quant Q&A

The discussion distinguishes conventional Value at Risk from a forecast of how portfolio risk changes as contracts mature. Historical VaR is described as applying past market shocks to current exposures; it generally does not model trades rolling off or…

FuturesOptionsCommoditiesRisk management
Quant Q&A

This note explains why long put options can lose much of a recent gain even when the underlying futures price remains below an earlier level. It uses the option Greeks to separate the effects: delta reflects changes in the underlying, vega reflects changes…

OptionsCommoditiesVolatilityDerivatives pricing
Quant Q&A

The explanation clarifies that a negative gamma estimate does not automatically invalidate a GJR-GARCH(1,1) model. Under the stated parameterization, positivity can hold when the intercept, ARCH coefficient, and GARCH coefficient are positive and the sum of…

CommoditiesVolatilityStatisticsRisk management
Quant Q&A

The document concerns finding historical daily prices for a set of commodity futures, including metals, grains, rubber, coal, and iron ore. The answer says Bloomberg covers the listed commodities except tobacco, for which the respondent could not find a…

CommoditiesFutures
Bitget Academy

The document explains how CFD copy trading can expose followers to losses even when they mirror an experienced trader. It describes fixed-ratio and fixed-lot copying, noting that copied positions may differ in size and execution and that the leader’s limit…

Risk managementPosition sizingForexCommodities
Bitget Academy

The article links a reported gold rally to lower long-term U.S. Treasury yields and a weaker dollar. Its explanation is that falling yields reduce the opportunity cost of holding a non-yielding asset, while dollar depreciation can make dollar-priced gold…

CommoditiesTechnical indicatorsMomentum
Bitget Academy

This preview lays out how an upcoming U.S. Nonfarm Payrolls report could affect expectations for Federal Reserve policy and, in turn, Treasury yields, the dollar, gold, and U.S. equities. It presents three broad outcomes: moderate hiring that may support a…

EquitiesFixed incomeForexCommodities