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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

5,867 documents

Quant Q&A

The document derives an approximate single implied volatility for a portfolio of options whose components have different implied volatilities. It begins with the condition that the portfolio’s modeled value at the common volatility should equal the sum of…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The document compares two martingale derivations of the Black–Scholes partial differential equation. With the bank account as numeraire, requiring the discounted option price to have zero drift yields the familiar PDE. The attempted stock-numeraire…

OptionsDerivatives pricingStatistics
Quant Q&A

The document offers historical volatility and correlation estimates as starting points for a foreign currency option model with domestic equities, foreign equities, and an exchange rate. Using weekly observations over five years for the DAX, S&P, and EUR…

ForexEquitiesOptionsVolatility
Quant Q&A

The document explains why the delta of a binary call becomes sharply concentrated around its strike as expiry approaches. Under Black–Scholes, the option value is expressed using the normal cumulative distribution function, and differentiating gives a delta…

OptionsDerivatives pricingVolatility
Quant Q&A

The document derives a European call pricing representation for an asset whose returns combine continuous Brownian movement with independent Poisson jumps. When jump sizes are lognormally distributed, conditioning on the number of jumps makes the terminal…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

An implied volatility surface reflects option prices that vary by strike and maturity, unlike the constant volatility assumption in the basic Black–Scholes model. Looking at one maturity at a time, a steep downside wing means out-of-the-money puts are…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The discussion asks whether manipulation of SPX options or equity and volatility futures caused the February 2018 VIX spike, and what data could help investigate. The response points to volatility-linked exchange-traded products as a possible source of…

VolatilityFuturesOptionsMarket microstructure
Quant Q&A

The document asks why an American put can have a different value from a European put when both are considered under the Black–Scholes framework. It contrasts the pricing inequality and payoff constraint for an American option with the familiar result that,…

OptionsDerivatives pricingRisk management
Quant Q&A

The document derives a closed-form price for a European payoff based on the positive part of one minus the strike divided by the terminal stock price, assuming the stock follows geometric Brownian motion under the money-market measure. Its key observation is…

OptionsDerivatives pricingStatistics
Quant Q&A

The document explains how to simulate terminal prices for several assets whose returns are correlated, in order to value a multi-asset option by Monte Carlo. In a geometric Brownian motion model, dependence is specified through correlations among Brownian…

Multi-assetOptionsStatisticsDerivatives pricing
Quant Q&A

The document describes a backward path-integral scheme for pricing an American put on a log-price grid. At each time step, it discounts and integrates the next-step option value against a Gaussian propagator for log prices, then applies the early-exercise…

OptionsDerivatives pricingBacktestingStatistics
Quant Q&A

The document frames a model-selection problem for reinforcement-learning-based dynamic hedging of long-dated swaptions. The proposed application uses 2y2y and 4y2y swaptions, requiring simulated paths that update both a forward swap curve and an implied…

Fixed incomeOptionsVolatilityMachine learning
Quant Q&A

The document shows how to rewrite a European put’s discounted expected payoff as an integral of the underlying asset’s cumulative distribution function. Starting from the payoff integral over nonnegative asset prices, it extends the density’s support to the…

OptionsDerivatives pricingStatistics
Quant Q&A

The document frames a research question about abrupt changes in index option prices near major expiration dates. It proposes systematic rebalancing by structured products as a possible source of price pressure and asks what other forces might contribute. The…

OptionsMarket microstructureEvent-drivenDerivatives pricing
Quant Q&A

The document explains why a European put’s Black–Scholes–Merton value can fall below its immediate exercise payoff. A European option cannot be exercised before expiration, so when the underlying price is far below the strike, the eventual payoff is…

OptionsDerivatives pricingRisk management
Quant Q&A

The document considers a weather-linked call whose daily payout depends on maximum temperature mapping to a quantity and a price index average exceeding a strike. The payoff also has daily and contract-wide payout limits, making a direct closed-form…

OptionsCommoditiesDerivatives pricingRisk management
Quant Q&A

The document outlines a derivation of the Black–Scholes equation from the Capital Asset Pricing Model rather than from a risk-free portfolio formed by delta hedging. It starts from CAPM’s relation between expected return and covariance-based risk…

OptionsDerivatives pricingStatistics
Quant Q&A

The document examines an option whose payoff and premium are expressed in the underlying asset, using an ETH example to compare conversion from a conventional Black–Scholes value with a direct simulation. The key issue is the payoff definition: converting…

OptionsCryptoDerivatives pricingStatistics
Quant Q&A

The document describes why a digital option’s stock hedge changes sharply as the underlying approaches and passes its strike. A digital option pays a fixed amount when it finishes in the money and nothing otherwise, so its payoff does not rise gradually with…

OptionsDerivatives pricingRisk management
Quant Q&A

Energy retailers that promise customers fixed prices while buying power or gas at floating wholesale prices face a mismatch between sales revenue and procurement cost. The risk can grow when demand and prices move together, as during cold weather. The…

CommoditiesRisk managementVolatilityDerivatives pricing
Quant Q&A

A question reports unstable or negative option values when the step count in an FFT-based binomial calculation becomes large. The accepted response suspects numerical precision loss in the terminal stock-price calculation, which raises up and down factors to…

OptionsDerivatives pricingStatistics
Quant Q&A

The document asks how to apply antithetic sampling when simulating the Heston stochastic volatility model with a discretized process. The central issue is whether to reverse the random draws only for the stock price or for both the price and variance…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The document presents a QuantLib Python calibration attempt for a time dependent Heston model that fails with a Boost assertion. The code builds a volatility surface, creates a piecewise time dependent model, attaches an analytic pricing engine, and…

OptionsVolatilityDerivatives pricingMachine learning
Quant Q&A

The document discusses why an online broker may cap the number of legs in a single options spread order. Its main explanation is that brokers submit orders using structures recognized by options exchanges, and the permitted order formats and applicable rules…

OptionsExecutionMarket microstructureDerivatives pricing