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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 document asks whether investors can buy a derivative that offsets an asset’s uncertainty while retaining its expected value. It contrasts two hypothetical assets with similar average outcomes but different ranges, then suggests that an investor might pay…

OptionsVolatilityDerivatives pricing
Quant Q&A

The answer explains how to set the time units when calculating Yang–Zhang volatility from hourly foreign-exchange prices. It distinguishes the basic time unit, which is the spacing between observed closing prices, from the result time unit, which is the…

ForexVolatilityStatistics
Quant Q&A

The document asks whether Parkinson, Rogers–Satchell, and Yang–Zhang estimators are used to forecast future volatility. The response says they can be used, and highlights Yang–Zhang’s reported comparison with close-to-close volatility estimation: it is…

VolatilityStatisticsBacktesting
Quant Q&A

The main explanation treats gamma as the rate at which delta changes with the underlying price. For a call, delta moves from near zero to near one across underlying prices, so the total area under gamma across prices is one under the simplified assumptions…

OptionsVolatilityDerivatives pricing
Quant Q&A

The document raises a proposed way to handle negative variance values when simulating the Heston stochastic volatility process: discard a draw that produces a negative variance and sample again. The author argues that independent, identically distributed…

VolatilityDerivatives pricingStatisticsOptions
Quant Q&A

The document proposes borrowing concepts from physics to describe market fluctuations. It suggests measuring price movement as velocity, with possible measures based on accumulated absolute changes, net changes, or the difference between two observations. It…

VolatilityTechnical indicatorsStatisticsDerivatives pricing
Quant Q&A

The document asks what price process can represent a perceived pattern in which prices rise gradually but fall abruptly. It contrasts this intuition with geometric Brownian motion, whose continuous paths do not contain sudden jumps, and points to Lévy…

StatisticsVolatility
Quant Q&A

The document proposes a master’s thesis studying how short-selling restrictions affected financial firms during the 2008 crisis. The suggested design compares similar banks or financial companies in countries that banned short sales with counterparts in…

EquitiesVolatilityEvent-drivenStatistics
Quant Q&A

The document clarifies how market implied volatility surfaces for caps and European swaptions relate to pricing more complex interest rate derivatives. A quoted surface represents vanilla option prices under a chosen convention or simple model, such as…

Fixed incomeOptionsVolatilityDerivatives pricing
Quant Q&A

The document asks why the Heston model’s call-price boundary at zero instantaneous variance equals the discounted intrinsic value of the option. It defines the option value as a function of time, spot price, and variance, then presents the boundary condition…

OptionsVolatilityDerivatives pricing
Quant Q&A

The document presents Black-Scholes as a foundational model and starting point, while explaining that its simplifying assumptions do not capture several observed features of market returns. It names fat tails and skew, changing volatility over time, and…

OptionsDerivatives pricingVolatilityStatistics
Quant Q&A

The document distinguishes put-call gamma imbalance from a mismatch between the gamma of a put and a call at the same strike, noting that put-call parity remains intact. It discusses how dealers’ or traders’ hedging behavior may affect the underlying market…

OptionsVolatilityDerivatives pricingMarket microstructure
Quant Q&A

The document asks how to bound the price of an out-of-the-money vanilla put when the only readily observable option price is an at-the-money call. It notes two basic constraints: call-put parity and the fact that call prices decline as strike increases. The…

OptionsDerivatives pricingVolatility
Quant Q&A

The document asks whether normal (Bachelier) implied volatilities can be derived directly for caps or swaptions under a Libor market model, rather than first deriving Black-76 volatility and converting it. The proposed forward-rate dynamics use a…

Fixed incomeDerivatives pricingVolatility
Quant Q&A

The document discusses how to choose volatility inputs and calibrate a Ho-Lee style short-rate model for bond pricing. Its central point is that a model with only a small number of free parameters cannot generally reproduce prices across a large set of…

Fixed incomeVolatilityDerivatives pricing
Quant Q&A

The document explains why an option’s price response to upward and downward implied-volatility shocks may be similar in magnitude without being exact opposites. A Taylor expansion expresses the price change through vega, the first derivative with respect to…

OptionsVolatilityDerivatives pricingRisk management
Quant Q&A

The document explains why VIX futures can trade in contango or backwardation and asks whether either shape represents a normal baseline. It relates the curve to expectations about future volatility: distant contracts may reflect the chance of future…

FuturesVolatilityMean reversionMachine learning
Quant Q&A

The document explains how to represent a time series with a nonlinear autoregressive mean, a GARCH(1,1) conditional variance, and an ARCH-in-mean effect. The mean equation includes the previous observation, its square, and the conditional variance; the…

StatisticsVolatilityMachine learning
Quant Q&A

The document considers whether a closed-form basket option formula could return each constituent asset’s terminal price, allowing users to evaluate many exotic payoffs from one expression. The response distinguishes that goal from pricing a single basket…

OptionsArbitrageVolatility
Quant Q&A

The document derives the value of a European claim paying the logarithm of the terminal asset price relative to its strike, assuming a non-dividend-paying asset under the Black–Scholes framework. It proposes a price that is affine in the log of the current…

Derivatives pricingOptionsVolatility
Quant Q&A

The document presents a fitting issue in a GARCH(1,1) model estimated on return data: some datasets yield an ARCH coefficient alpha of zero and a GARCH coefficient beta near 0.999. It includes a model specification with a constant mean, no autoregressive…

StatisticsVolatilityBacktesting
Quant Q&A

The document explains how to calibrate a stochastic volatility model such as Heston to option data and interpret its parameters. It recommends fitting model prices in implied volatility space by minimizing squared differences between model-implied and…

OptionsVolatilityDerivatives pricingStatistics
Quant Q&A

The document asks why exchange option chains can show different implied volatilities for European puts and calls with the same underlying and expiry, even though put-call parity links their prices. Its answer points to market supply and demand: stronger…

OptionsVolatilityDerivatives pricingSentiment
Quant Q&A

The document considers position sizing for a hypothetical signal that predicts an index’s direction with a 60% win probability against 40% losses. One answer applies the Kelly criterion to maximize long-run capital growth and gives a fraction of capital to…

Position sizingRisk managementVolatilityStatistics