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

20,364 documents

Quant Q&A

The document asks how to derive an approximation that maps at-the-money implied volatility to volatility at another strike under a lognormal distribution. The relationship is framed as a strike-dependent scaling rule, using the ratio of the at-the-money…

OptionsVolatilityDerivatives pricing
Quant Q&A

The document asks why two stated formulas for option vanna appear to differ by a factor of one hundred. It gives expressions involving spot price, volatility, time to maturity, the Black-Scholes variables, and vega, then reports that substituting a…

OptionsDerivatives pricingVolatility
Quant Q&A

The document questions whether a bank’s coded option pricing function can sensibly be used to calculate the profit and loss of a short option position. It presents a one-period P&L expression that combines the change in the option’s model price, interest on…

OptionsDerivatives pricingRisk management
Quant Q&A

The document explains that an incomplete market generally has multiple equivalent martingale measures rather than a unique risk-neutral measure. For a nonreplicable claim, expected discounted payoffs under these measures yield a range of arbitrage-free…

Derivatives pricingOptionsRisk managementStatistics
Quant Q&A

The document clarifies that “heat-map method” may refer to a way of assessing VaR model performance rather than a procedure for calculating VaR. In the answer provided, historical VaR breaches are counted over a review window and mapped to colored zones: a…

Risk managementBacktestingStatistics
Quant Q&A

The document explains why investors searching SEC filings may not find loan-level ABS-EE data for residential mortgage-backed securities. The key distinction is between publicly registered offerings, for which loan-level disclosures are required, and Rule…

Fixed incomeUS marketsStatistics
Quant Q&A

The document explains how counterparties manage exposure on OTC variance swaps when initial or maintenance margin is not posted. It says that many OTC swaps are governed by a credit support annex, but arrangements vary; without margin, counterparties rely…

OptionsVolatilityDerivatives pricingRisk management
Quant Q&A

The document challenges the idea that PCA and machine learning are rarely used in trading. It notes that machine learning covers many methods, from linear regression to random forests, and that firms have had varying degrees of success with them. It then…

Machine learningStatisticsFixed incomeEquities
Quant Q&A

The document considers how a leveraged exchange-traded fund affects the Sharpe ratio. Its answer applies a simple proportional scaling argument: if returns are doubled, their expected value doubles and their variance quadruples, so their standard deviation…

EquitiesStatisticsRisk management
Quant Q&A

The document explains why an uncollateralized OTC derivative can still impose funding costs on a bank, even though the trade itself requires no initial cash. One source is the hedge: the bank may hedge through collateralized interbank trades, and adverse…

Derivatives pricingRisk managementFixed income
Quant Q&A

The explanation frames interest-rate hedging as offsetting the sensitivity of a portfolio’s market value to movements in the risk-free curve. It values a debt instrument by discounting its dated cash flows using factors that reflect rates and other…

Fixed incomeDerivatives pricingRisk managementPortfolio construction
Quant Q&A

The document asks how to calculate the remaining principal on a loan when interest accrues using Actual/360 or Actual/Actual day-count conventions. It contrasts these methods with a 30/360 amortization, for which a level-payment annuity formula can express…

Fixed incomeStatistics
Quant Q&A

The discussion diagnoses an implausibly large cumulative return in a simple moving-average crossover backtest. The strategy uses closing prices and a 10-day simple moving average to set a long or short position, then applies that position to daily asset…

BacktestingTechnical indicatorsTrend followingExecution
Quant Q&A

The discussion examines whether corporate bond OAS should be adjusted for price or coupon differences when comparing bonds from the same issuer. It distinguishes a bond’s long-term value relative to a credit curve from its short-term trading value, which may…

Fixed incomeDerivatives pricingStatisticsRisk management
Quant Q&A

The document addresses why nearby option strikes can show sharply different open interest. Its proposed explanation is a preference for round-number strikes: traders may concentrate positions at salient levels such as 7,400, leaving a nearby strike like…

OptionsMarket microstructureSentiment
Quant Q&A

The note explains why the effect of implied volatility on a vertical credit spread depends on the net Greeks of its two option legs. A credit spread sells one option and buys another with the same expiration but a different strike, receiving a net premium at…

OptionsVolatilityDerivatives pricingRisk management
Quant Q&A

The document considers why a stock price can move repeatedly even when company news does not arrive at the same pace. It presents price as a noisy proxy for underlying value: after news, market participants may take time to interpret its implications and…

EquitiesMarket microstructureHigh-frequency tradingStatistics
Quant Q&A

The note distinguishes quoted bond prices from bond yields. It explains that a U.S. bond price such as 99 1/8 represents 99.125 per 100 of par, while a yield is the discount rate that equates the present value of coupon and principal cash flows with the…

Fixed incomeUS marketsDerivatives pricing
Quant Q&A

The document compares two ways to forecast daily variance with a GARCH(1,1) model when five-minute returns are available. In the first approach, the model is fit or applied at the daily horizon, using a realized-variance estimate and a prior daily variance…

VolatilityStatisticsBacktesting
Quant Q&A

The document discusses the purpose of meta-labeling in machine-learning trading systems. It responds to the concern that a secondary model merely filters trades and might overfit historical examples. The proposed framing is to start with a primary strategy…

Machine learningBacktestingTechnical indicators
Quant Q&A

The document poses a terminal-value PDE in two variables with second derivatives in each variable, a mixed derivative, and a source term proportional to one state variable. It asks how to solve the equation when reducing the two-dimensional dependence…

StatisticsDerivatives pricing
Quant Q&A

The document explains how to simulate a geometric Brownian motion after changing from the physical measure, where the drift is the expected return, to the risk-neutral measure, where the drift is the risk-free rate. It derives the change in Brownian motion…

StatisticsDerivatives pricingBacktesting
Quant Q&A

The document considers how to simulate correlated asset returns for Monte Carlo mean-variance portfolio optimization. A common approach starts with independent standardized draws and multiplies them by a matrix factor of the target covariance matrix,…

Portfolio constructionStatisticsBacktesting
Quant Q&A

The document distinguishes utility representations under certainty from expected utility under uncertainty. Under certainty, any increasing transformation of a utility function preserves the ranking of preferences. With lotteries, preferences are represented…

Statistics