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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 presents a question about applying the Johansen procedure to oil and corn price series after unit-root tests suggest both are nonstationary. It shows example eigenvalue and trace test statistics alongside critical values, then asks how to…

CommoditiesStatisticsPairs trading
Quant Q&A

The document considers fitting an implied-volatility curve to intraday option observations, where data become sparse and variable at extreme out-of-the-money strikes. The question is whether unusual observations can be discounted and whether option-market…

OptionsVolatilityStatistics
Quant Q&A

The discussion covers two linked choices when calculating a Sharpe ratio from monthly returns: selecting a risk-free return that matches the measurement horizon and converting the ratio to an annual scale. One answer suggests using a short-term Treasury rate…

StatisticsRisk managementFixed income
Quant Q&A

The document considers how much historical data to use when monthly portfolio rebalancing is based on Sharpe-ratio or mean-variance optimization. It does not identify a universally suitable window. One response emphasizes that expected returns and volatility…

Portfolio constructionStatisticsRisk managementBacktesting
Quant Q&A

The document explains why the Black–Scholes call price contains two different probabilities: N(d2), the risk-neutral probability that the option finishes in the money, and N(d1), the corresponding probability under a measure associated with using the stock…

OptionsDerivatives pricingStatistics
Quant Q&A

The document asks why a pairs-trading spread is formed from stock price levels when the hedge ratio is estimated using price changes. The answer identifies cointegration: two nonstationary price series may share a long-run relationship such that a particular…

EquitiesPairs tradingMean reversionStatistics
Quant Q&A

The document explains how to simulate two correlated Hull–White short-rate processes, such as rates for two currencies, using QuantLib. Its suggested construction combines separate Hull–White processes in a stochastic process array and supplies a correlation…

Fixed incomeMulti-assetStatisticsBacktesting
Quant Q&A

The document compares covariance estimates from a pandas returns table and a NumPy array, where the results differ in shape and contain missing values. The core issue is that the two interfaces interpret the data layout differently: pandas computes…

StatisticsPortfolio constructionEquities
Quant Q&A

The post considers whether regime-switching models can detect market shifts and support trading. It outlines a common approach: model market behavior using states with specified distributions, then estimate a Markov transition matrix. The intended benefit is…

Machine learningStatisticsBacktestingPortfolio construction
Quant Q&A

The document explains how an annual effective discount rate can be related to an effective interest rate when interest received in advance is repeatedly reinvested. Starting with capital C, each advance interest payment is reinvested under the same terms,…

StatisticsFixed income
Quant Q&A

The document raises questions about whether quantitative managers use the Black–Litterman framework, which combines a starting portfolio with investor views to form portfolio inputs. It observes that the method is often presented as a way for portfolio…

Portfolio constructionStatisticsFactor investing
Quant Q&A

The document asks whether estimating factor exposures for 25 portfolios requires seemingly unrelated regression (SURE), especially when the estimated intercepts feed into a GRS test. The response says there is no universally correct regression method: the…

EquitiesFactor investingStatistics
Quant Q&A

The document introduces autoregressive conditional duration models in the context of high-frequency financial data. The questioner has implemented an ACD model in R using trade-only observations and asks how modeling elapsed time between trades, or volume…

High-frequency tradingMarket microstructureStatistics
Quant Q&A

The document weighs whether a candidate can prepare for a quant interview on stochastic processes, stochastic calculus, and financial instrument pricing in a very short time. It argues that this is unrealistic for most learners unless they already have…

StatisticsDerivatives pricing
Quant Q&A

The document asks how to discount a cash flow at a tenor beyond the longest available LIBOR tenor, and whether interpolating quoted rates is appropriate when those rates reflect different payment periods. The answer frames the problem through the distinction…

Fixed incomeDerivatives pricingStatistics
Quant Q&A

The document addresses numerical breakdown in SABR normal implied volatility calculations when the forward price equals the strike. In the cited formulation, the intermediate function chi depends on zeta, which becomes zero at this at-the-money point; direct…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The document presents an attempted Kalman filter implementation for estimating a changing hedge ratio between two cointegrated stock log-price series. The proposed state is a single beta that evolves over time, with the second stock’s log price treated as…

EquitiesPairs tradingStatisticsMachine learning
Quant Q&A

The document examines whether the arithmetic average of two independent standard Brownian motions is itself a standard Brownian motion. The average starts at zero, has continuous paths, and its increments remain Gaussian with mean zero. Independence makes…

StatisticsRisk management
Quant Q&A

The document explains the intuition behind the claim that riskier assets offer higher expected returns. If investors view an asset as riskier, they require compensation for bearing that uncertainty. Their reluctance to buy it at the same price as a safer…

Risk managementStatistics
Quant Q&A

The document asks whether several historical volatility estimators can be ranked from largest to smallest: close-to-close variants and range-based estimators including Parkinson, Garman–Klass, Rogers–Satchell, and Yang–Zhang. The answer says a universal…

VolatilityStatisticsBacktesting
Quant Q&A

The document seeks an intuitive explanation of the integral definition of fractional Brownian motion and asks why its construction includes negative times. The responses describe the Hurst exponent as controlling how increments scale with time and how…

StatisticsMean reversionVolatility
Quant Q&A

The document explains why the intercept in a standard GARCH(1,1) conditional variance equation is usually constrained to be strictly positive, even when the lagged squared return and lagged variance terms are nonnegative. The accepted answer distinguishes…

StatisticsVolatilityRisk management
Quant Q&A

The question highlights a difficulty in analyzing foreign exchange rates: bilateral pairs that share a currency can move together because of changes affecting that currency. It asks whether another asset, such as gold, could serve as a common reference for…

ForexStatistics