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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
Quantpedia
86 documents
TqSdk
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 note asks whether an American put’s time derivative, or theta, is continuous where the optimal exercise boundary meets the continuation region. It distinguishes this question from the familiar smooth-pasting condition, which says the option’s delta…

OptionsDerivatives pricingVolatility
Quant Q&A

The note explains the intuition behind Hull’s statement that a U-shaped implied volatility smile corresponds to a distribution with more probability in both small and extreme moves than a lognormal distribution. Higher implied volatility away from…

OptionsVolatilityDerivatives pricing
Quant Q&A

The document examines negative variance-equation intercept estimates in a GJR-GARCH model with a pandemic indicator as an exogenous input. The response writes a simplified model with a regime indicator and an asymmetric term for negative shocks, then argues…

VolatilityStatisticsRisk managementEquities
Quant Q&A

The document asks how to generate observations from a four-factor t-copula when the dependence matrix changes over time. Its setting is a series of 2,000 correlation matrices from a copula-DCC model, with one matrix intended for each time step. The accepted…

Multi-assetStatisticsVolatilityBacktesting
Quant Q&A

Structured products can embed options that transfer specific risks between investors and issuing banks. For example, autocallable notes may pay higher coupons in exchange for investors selling downside protection, often through barrier puts. This can leave…

OptionsVolatilityDerivatives pricingRisk management
Quant Q&A

The document compares two discrete approximations for estimating the volatility parameter in a mean-reverting stochastic differential equation with a deterministic time-varying mean. One approach begins from the continuous-time solution and approximates the…

StatisticsVolatility
Quant Q&A

The document raises a practical question about estimating European option vega under the Heston stochastic volatility model. The author reports computing delta and gamma by bumping spot and recalculating prices or delta, with results matching Bloomberg…

OptionsVolatilityDerivatives pricingRisk management
Quant Q&A

The document explains why a standard variance swap payoff uses a scaled sum of squared log returns without subtracting the sample mean squared. One answer distinguishes realized variance in the market convention from the statistical sample variance: the…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The document poses a pricing puzzle under Black–Scholes assumptions: with spot at 100, zero interest, and volatility chosen so the one-year one-standard-deviation price range is 50 to 200, the call at 200 costs more than the put at 50. It asks why strikes…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The document questions whether daily leveraged ETFs can serve as long-term investments and whether switching between bullish and bearish funds could improve returns. It describes a simple simulation that compounds daily leveraged returns generated from…

EquitiesVolatilityBacktestingRisk management
Quant Q&A

The document asks whether the negative half-variance term in the logarithm of a geometric Brownian motion price process is related to volatility drag when converting arithmetic returns into geometric returns. It supplies the Black–Scholes stochastic…

StatisticsVolatility
Quant Q&A

The note explains that a product term in a regression represents an interaction: the association between one predictor and the outcome changes with the value of another predictor. For two predictors, including their product allows the fitted slope for one…

StatisticsVolatility
Quant Q&A

The note explains that delta 10 or delta 30 implied volatility quotes identify points on an options volatility surface, rather than a different definition of option delta. Each delta quote maps to a particular strike or moneyness under the relevant market…

OptionsFixed incomeVolatilityDerivatives pricing
Quant Q&A

The document derives the probability density of the minimum of a standard Wiener process over a fixed time interval using the reflection principle. It first observes that the minimum has the same distribution as the negative of the process maximum, since…

StatisticsVolatility
Quant Q&A

The document asks why the characteristic function used in Heston stochastic-volatility option pricing satisfies the same partial differential equation as the probability terms in a call-price representation. It presents the governing PDE for those terms,…

OptionsDerivatives pricingVolatility
Quant Q&A

The position buys two deep out-of-the-money calls and puts, while selling one nearer out-of-the-money call and put. One response describes it as a ratioed strangle switch; another views it as an iron condor combined with a long strangle. These are…

OptionsVolatilityDerivatives pricing
Quant Q&A

The document compares a normal model for a forward swap rate, where volatility measures absolute rate changes, with a lognormal model, where volatility measures proportional changes or log returns. It derives the relationship by considering the lognormal…

Fixed incomeDerivatives pricingVolatility
Quant Q&A

The document presents QuantLib code that constructs a Black–Scholes process and an American-style vanilla option with specified dividend dates and amounts. The pricing function accepts a valuation date, expiry, call or put designation, strike, time-step…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The document surveys ways to express a view on how volatility itself changes. It distinguishes realized volatility of volatility from implied volatility of volatility and describes several candidate exposures: options on volatility products, variance-swap…

OptionsVolatilityDerivatives pricingMarket microstructure
Quant Q&A

The document raises a modeling question about combining GARCH volatility filtering with peaks-over-threshold extreme value theory (POT) and Markov chain Monte Carlo estimation. The researcher fits several conditional volatility specifications, including…

Risk managementVolatilityStatistics
Quant Q&A

The document explains why a stochastic local volatility model can combine features of local volatility and stochastic volatility. It writes the asset’s instantaneous volatility as a stochastic factor multiplied by a state- and time-dependent local factor.…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The document addresses whether CME options on one-month SOFR futures continue trading during the month when the underlying SOFR rate is measured. This matters for implied volatility modeling: if trading overlaps the averaging period, remaining uncertainty…

OptionsFuturesFixed incomeVolatility
Quant Q&A

The document compares a rolling historical-volatility calculation with an ARCH(q) specification and asks how to describe each. It distinguishes a direct calculation from a model that assumes a return process and requires parameter fitting. It also mentions…

VolatilityStatistics
Quant Q&A

The document addresses how to simulate the variance process in a Heston-style model when its correlated Brownian driver includes a drift adjustment linked to the wealth process. The proposed approach is to substitute the adjusted Brownian differential into…

VolatilityDerivatives pricingStatistics