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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
Quantopian lectures
45 documents
Binance API docs
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

12,303 documents

Quant Q&A

The discussion describes what makes options strategy backtesting more demanding than testing a simple underlying asset. A credible simulation needs historical option chains, including contract terms and market prices, alongside underlying prices used to…

OptionsBacktestingDerivatives pricingVolatility
Quant Q&A

The document compares Monte Carlo prices for a European call under constant volatility and under volatility sampled once per path from a lognormal distribution. It raises the question of why the random-volatility estimate is slightly below the…

OptionsVolatilityDerivatives pricing
Quant Q&A

The document asks how to estimate a stochastic volatility model from historical daily returns without using an implied volatility surface or computationally intensive MCMC. It specifies a latent log variance that follows an autoregressive process with…

VolatilityStatisticsRisk managementBacktesting
Quant Q&A

The document raises a model-validation question: whether stochastic-volatility option models produce more accurate market-price estimates than the standard Black–Scholes–Merton model. It highlights two practical costs of more complex models: greater…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The note raises two implementation questions about the time-series momentum method described by Moskowitz, Ooi, and Pedersen. First, it asks how to interpret the paper’s ex-ante volatility estimate when the strategy uses a twelve-month return signal: whether…

FuturesTrend followingVolatilityPosition sizing
Quant Q&A

The document asks whether Black–Scholes call valuation should use a point estimate of variance from historical returns or average option values across the sampling distribution of that estimate. It describes treating the sample variance as uncertain, mapping…

OptionsVolatilityStatisticsDerivatives pricing
Quant Q&A

The document considers how to estimate and plot volatility from a long history of FTSE closing prices. Its code calculates log returns, but the questioner’s approach appears to treat the deviation of each individual return from the overall mean as a…

VolatilityStatisticsTechnical indicators
Quant Q&A

The document considers whether measuring geometric Brownian motion at weekly rather than daily intervals lowers annualized volatility. Under the stated assumption of independent, normally distributed returns, variance grows linearly with time. Scaling the…

StatisticsVolatilityBacktesting
Quant Q&A

The document raises a pricing question about bonds with constant-maturity swap (CMS) coupons in euros, where interest rates may be negative. It asks whether the convexity adjustment from Hull, which assumes lognormal rates, can be adapted to a normal-rate…

Fixed incomeDerivatives pricingVolatility
Quant Q&A

The document examines how a rise in implied volatility for a short-expiry option may affect a longer-expiry option with the same strike, assuming the event driving the move occurs within the shorter option’s life. The answer models volatility across time…

OptionsVolatilityDerivatives pricing
Quant Q&A

The document explores estimating daily volatility for government bonds from open, high, low, and close prices. It notes that a daily high-low range can distinguish sessions with larger and smaller intraday fluctuations, then identifies the Garman-Klass…

Fixed incomeVolatilityStatisticsTechnical indicators
Quant Q&A

The note distinguishes fitting an implied volatility surface at the current time from specifying how that surface changes as time passes and the underlying price moves. A cross section indexed by maturity and strike can be fitted to current market data, but…

OptionsVolatilityDerivatives pricing
Quant Q&A

The document asks what it means to say Black–Scholes overprices or underprices options when the option’s true value is not directly known. It presents research claims that the model can price deep in-the-money calls too high and deep out-of-the-money calls…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The document asks why Ljung–Box tests on squared returns can report very high p-values even when their autocorrelation plots appear to show dependence. It presents test results for returns and squared returns at several lag lengths, then asks whether the…

StatisticsVolatilityRisk management
Quant Q&A

The document explains why converting an FX option delta into a strike requires the market’s quoting conventions and the correct definition of at-the-money. In the example, the pair uses unadjusted deltas, and the at-the-money strike is delta-neutral: the…

ForexOptionsDerivatives pricingVolatility
Quant Q&A

The document explains how to obtain one-step-ahead conditional mean and volatility forecasts across a time series after fitting an sGARCH model with rugarch. A standard call to the forecast function returns a forecast at the end of the fitted sample, which…

VolatilityStatisticsBacktesting
Quant Q&A

The note asks how power-law distributed pauses would change the first-passage-time density of Brownian motion with drift. It frames the process as motion that advances according to a drifted Brownian model but intermittently waits, and proposes convolving…

StatisticsVolatility
Quant Q&A

The document considers an exchange-traded product combining a butterfly payoff with a double no-touch condition: the payoff follows a butterfly profile if the underlying stays between two barriers until maturity, and becomes zero if either barrier is…

OptionsDerivatives pricingVolatility
Quant Q&A

The discussion examines option price changes near expiration that appear larger than delta and theta alone would suggest. It explains that gamma exposure is most pronounced near at-the-money strikes and presents gamma scalping as a way to manage a long gamma…

OptionsVolatilityDerivatives pricingRisk management
Quant Q&A

The document clarifies what a mid-curve swaption measures and how it can help infer forward volatility. A mid-curve swaption measures volatility of a forward-starting swap rate, but that is not itself the forward volatility of an option whose strike will be…

Fixed incomeOptionsVolatility
Quant Q&A

The document addresses how to value American options on SPY when dividends are present. It challenges the proposed shortcut of taking the maximum of two European option values, one tied to the ex-dividend date and one to final expiry. An American option can…

OptionsDerivatives pricingVolatility
Quant Q&A

The document considers how to assess long-volatility strategies with frequent small losses and occasional large gains, where mean return, Sharpe ratio, and hit rate may not capture the payoff pattern. The response recommends beginning with a long history of…

VolatilityBacktestingRisk managementPortfolio construction
Quant Q&A

The document poses an implied-volatility problem for an American call when a known dividend is paid before expiration and interest rates are zero. Because early exercise may be optimal immediately before the ex-dividend date, the contract can be viewed as…

OptionsDerivatives pricingVolatility