Skip to content

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

12,303 documents

Quant Q&A

The discussion considers how to examine whether emerging markets change in volatility and in correlation with developed-market indices. Rather than prescribing a split into halves or thirds, one answer recommends plotting rolling estimates to locate periods…

VolatilityStatisticsEquities
Quant Q&A

The document presents an attempt to calibrate the raw SVI parameterization to market implied volatilities. The code converts quoted volatilities to total implied variance, computes model variance at log-moneyness values, and minimizes the root sum of squared…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The document asks how to evaluate the integral in the semi-closed Heston formula for call options. Its answer says Monte Carlo simulation can approximate the integrals, while Newton-Cotes formulas and Gaussian quadrature are also possible numerical methods.…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The document examines why sponsors may not issue an exchange-traded note alongside an inverse version. It explains that daily leveraged and inverse notes are path-dependent: their exposure resets as their values change, so the pair’s gains and losses do not…

Derivatives pricingRisk managementCommoditiesFutures
Quant Q&A

The document explains why at-the-money implied volatility can rise or fall across option maturities. It challenges the assumption that the term structure should be monotonic and identifies several possible drivers, including return autocorrelation,…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The document uses a Gram–Charlier approximation to connect the distribution of log returns with the implied volatility curve. It standardizes log returns, approximates their density using a normal density adjusted for skewness and kurtosis, prices a call…

OptionsVolatilityStatisticsDerivatives pricing
Quant Q&A

The document discusses a put backspread, described as selling a higher-strike put and buying a lower-strike put with the same maturity. At expiration, the payoff has a V-shaped profile. In the answer’s simplified description, the steepness of that payoff is…

OptionsVolatilityRisk management
Quant Q&A

A digital call pays when the underlying finishes above its strike, so its discounted price equals the risk-neutral probability of finishing in the money. The document connects this price to the strike derivative of an ordinary call price. When ordinary call…

OptionsVolatilityDerivatives pricing
Quant Q&A

This discussion explains why an option can have a curved value before expiration even though its payoff at expiration is piecewise linear. Before expiry, an out-of-the-money option retains value because the underlying may still move enough for the option to…

OptionsDerivatives pricingVolatility
Quant Q&A

This exchange clarifies what “autoregressive” means in an ARCH model. The model expresses current conditional variance as a function of past squared errors, rather than directly as an autoregression of past variances. The response explains that an error can…

VolatilityStatistics
Quant Q&A

The document examines Malliavin integration by parts for estimating option sensitivities in a diffusion model. It asks whether a Delta weight concentrated up to the first payoff observation loses relevant Brownian information, and how to extend the method to…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The document explains why the Hull–White short-rate model’s zero-coupon bond price at the initial time is independent of volatility and mean reversion. The model is calibrated to the observed market zero curve, so its initial bond prices reproduce that curve…

Fixed incomeVolatilityStatistics
Quant Q&A

The document presents an approximation for pricing a geometric Asian call under Heston stochastic volatility. It replaces the random variance path with its deterministic expected variance, then integrates that expected variance over the option horizon to…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The document addresses why a basic GARCH model may be outperformed by realized GARCH or other realized-volatility models, especially when modeling intraday volatility. Its explanation focuses on information inputs rather than a strict difference between…

VolatilityStatisticsMachine learning
Quant Q&A

The document explains how to obtain Black–Scholes implied volatility for a European option when the underlying follows a stochastic volatility jump model. Since the model lacks a closed-form log-return density and a direct analytical option price, the answer…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The note offers a possible historical explanation for a fixed fallback volatility used in a bond option-adjusted spread model. It says term-structure models from that period often relied on constant volatility assumptions, commonly calibrated to realized…

Fixed incomeVolatilityStatistics
Quant Q&A

The document asks how node values for an American option on a small stock-price tree translate into a hedge. The accepted reply identifies the commenter’s suggested stock position as delta hedging: adjusting exposure to the underlying so that small price…

OptionsDerivatives pricingVolatilityRisk management
Quant Q&A

The document addresses whether an option-implied distribution for a stock’s log return must be symmetric because Brownian motion is symmetric. The response clarifies that symmetry is not a general property of option-implied distributions and points to models…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The document examines numerical difficulties in pricing European calls under the stochastic volatility with correlated jumps (SVCJ) model. The original MATLAB implementation produces integration warnings and NaN values while attempting to recover prices from…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The document explains why delta neutrality does not guarantee a break-even result or a profit. Delta measures a portfolio’s local sensitivity to the underlying price; it does not eliminate all sources of return or risk. The response distinguishes the claim…

OptionsDerivatives pricingVolatilityRisk management
Quant Q&A

The document derives the instantaneous volatility of the product of two correlated assets modeled with lognormal diffusion dynamics. Applying Itô’s product rule produces a return shock with contributions from both assets, including their correlation.…

StatisticsVolatilityDerivatives pricing
Quant Q&A

The document examines whether probability belongs in explanations of option Greeks. Its response distinguishes uncertainty in future price paths, given fixed model parameters, from uncertainty in the parameters themselves. Under the fixed-parameter…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The document derives a condition for a vanilla option’s implied volatility curve to have zero slope with respect to strike at a given maturity. Differentiating the Black–Scholes call price, expressed using strike-dependent implied volatility, and comparing…

OptionsVolatilityDerivatives pricingStatistics