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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

20,364 documents

Quant Q&A

DGTW adjusted returns measure a stock’s performance relative to a characteristic-matched benchmark. For each stock, the method subtracts the return of a portfolio of firms assigned to matching groups based on market equity, market-to-book ratio, and prior…

EquitiesFactor investingBacktestingStatistics
Quant Q&A

The document explains how to calculate an option’s volatility-related P&L when a pricing model maps observed market implied volatilities into calibrated parameters and then into the volatility used for the instrument. By the chain rule, sensitivity to each…

OptionsVolatilityDerivatives pricingRisk management
Quant Q&A

The document asks whether one scalar can summarize an implied volatility smile for a single maturity. It considers ATM curvature, estimated from nearby implied volatilities, and proposes SABR’s volatility-of-volatility parameter alpha as a possible…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The document explains that a J message in a Nasdaq TotalView ITCH sample file is not necessarily corrupt or undocumented. The response identifies it as a Limit Up–Limit Down auction collar message, which reports the price thresholds within which a paused…

EquitiesMarket microstructureExecution
Quant Q&A

The document considers how to rank live bonds using predicted default probabilities from a classifier trained with matured bond outcomes. It questions an individual-bond score that takes the logarithm of a return-to-default-probability ratio. The response…

Fixed incomeMachine learningRisk managementStatistics
Quant Q&A

The document distinguishes the return between two prices from the total return generated by a sequence of long positions. It asks whether adding percentage returns from separate trades gives the correct overall result and whether a charted calculation method…

StatisticsBacktesting
Quant Q&A

The document addresses event studies where many companies experience events on different calendar dates. Its central method is to align each event as time zero, estimate the normal-return model in a pre-event window, and express returns on surrounding days…

EquitiesEvent-drivenStatisticsBacktesting
Quant Q&A

The document asks how to differentiate Value-at-Risk and Conditional Value-at-Risk for a capped random variable, formed by taking the minimum of a random variable and a threshold. It does not derive the sensitivity or specify assumptions about the…

StatisticsRisk managementDerivatives pricing
Quant Q&A

The document discusses ways to estimate corporate probability of default from company financial statements or internal credit ratings. It mentions structural approaches such as the Merton model, accounting-based scores such as Altman’s Z and Ohlson’s O, and…

Fixed incomeStatisticsRisk managementMachine learning
Quant Q&A

The document examines why highly profitable firms might earn higher average returns despite appearing safer. It reviews several ways to measure profitability and presents two real-options explanations. One links profitable firms to a greater share of assets…

EquitiesFactor investingStatisticsRisk management
Quant Q&A

The document describes a thesis problem: fitting conditional volatility models to financial returns so their standardized residuals are approximately independent and identically distributed with zero mean and unit variance. GARCH(1,1) and GJR-GARCH(1,1)…

StatisticsVolatilityEquitiesTechnical indicators
Quant Q&A

The document asks how to choose individual stocks to short when the broad market appears to be topping. The author describes using relative strength versus an index, following Stan Weinstein’s approach, and says this method has been profitable but may miss…

EquitiesTechnical indicatorsRisk management
Quant Q&A

The document explains how to infer expected policy-rate changes from an overnight indexed swap curve when a market lacks meeting-date futures. The proposed approach builds a curve whose nodes fall on policy decision effective dates, with overnight forward…

Fixed incomeStatisticsDerivatives pricing
Quant Q&A

The document asks how to represent two-month and three-month maturities from a broker’s implied FX volatility delta-tenor table when using the Black–Scholes formula. It contrasts converting the tenor to a simple year fraction, such as months over twelve,…

ForexOptionsVolatility
Quant Q&A

The document asks how to reduce Monte Carlo pricing variance for a European call by using a second call with a nearby strike as a control variate. It specifies a stock model with normally distributed log returns and a call payoff, and notes that the second…

OptionsDerivatives pricingStatistics
Quant Q&A

The document explains how IMM dates are applied to interest-rate swap periods and related fixing dates. For swaps, periods begin and end on the third Wednesday of each quarter, adjusted to the following business day when necessary. These adjusted dates…

Fixed incomeDerivatives pricingStatistics
Quant Q&A

The document considers whether a covered-call seller should close a short out-of-the-money call after it has lost much of its value, then sell a call closer to the money. The proposed benefit is to collect additional premium as the short option’s delta and…

OptionsEquitiesRisk managementVolatility
Quant Q&A

The document surveys ways to capture, store and later analyze market tick data. It describes collecting Interactive Brokers market updates through an R interface and writing records to files or in-memory time-series objects. For longer-term analysis,…

High-frequency tradingMarket microstructureExecutionBacktesting
Quant Q&A

The document compares annualized standard deviation of returns with realized volatility computed as the square root of summed squared high-frequency returns. It says the realized variance approach can make better use of intraday observations than a standard…

VolatilityStatisticsHigh-frequency trading
Quant Q&A

The discussion clarifies a comparison between two short call spreads that share a short lower-strike call but use different long-call strikes. Moving the protective long call farther out of the money can increase the credit received, but it also widens the…

OptionsRisk managementDerivatives pricing
Quant Q&A

The question asks whether Markowitz portfolio construction should use closing prices or dividend-adjusted prices when estimating a minimum-variance portfolio, an optimal risky portfolio, and an efficient frontier. The answer frames the choice around how the…

Portfolio constructionEquitiesBacktesting
Quant Q&A

The discussion distinguishes stochastic volatility from the leverage-like relationship that creates an asymmetric implied volatility skew. Random volatility by itself can make returns heavier-tailed than a single normal distribution and produce a roughly…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The discussion explains why a fair die’s physical outcome probabilities do not, by themselves, determine the risk-neutral probabilities. Risk-neutral probabilities are inferred from traded prices and payoffs, so a price process and the available securities…

StatisticsDerivatives pricingArbitrage
Quant Q&A

The document asks how to use five years of weekly returns for three asset classes to find a minimum-variance portfolio and plot an efficient frontier in Excel. It notes that a simple two-asset weight table becomes cumbersome as the number of assets grows,…

Portfolio constructionStatisticsRisk management