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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
WonderTrader
14 documents
Alphalens
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 measure volatility over a past sample when the goal is explanatory analysis rather than forecasting. The motivating study relates monthly mutual fund alphas to market volatility measured over the same historical period. It contrasts…

VolatilityStatisticsFactor investing
Quant Q&A

The document derives covered interest rate parity by equating the present value of a foreign currency cash flow when converted at today’s spot rate with its value when converted at the forward rate and discounted in the other currency. It then explains why a…

ForexFixed incomeArbitrage
Quant Q&A

The document asks how the survival probability of a default time relates to its hazard rate, and why a conditional survival probability has a shifted hazard integral. It presents the standard unconditional relation as an exponential of the negative…

Fixed incomeStatisticsRisk management
Quant Q&A

The document explains how to convert fractional betting odds into implied probabilities. For odds stated as a to b, it gives the conversion b divided by the sum of a and b. Applying that formula to the quoted odds yields implied chances of 0.652 for remain…

StatisticsEvent-driven
Quant Q&A

The document asks when Monte Carlo discretization is needed in financial modeling, given that geometric Brownian motion can be simulated exactly at selected dates. The responses name the LIBOR market model, Heston stochastic volatility, and local volatility…

Derivatives pricingOptionsStatisticsBacktesting
Quant Q&A

The document explains why rates and discount factors derived from a QuantLib zero-coupon curve may differ from input deposit yields. The example builds a curve from deposit rate helpers and compares calculated forward rates and discount factors with quoted…

Fixed incomeDerivatives pricingStatistics
Quant Q&A

The document presents a long-short portfolio with a small long position, a much larger short position, and cash held alongside the positions. Its reported portfolio contribution is sharply negative even though the portfolio’s net market value is positive.…

Portfolio constructionRisk managementPosition sizing
Quant Q&A

The document asks how to value a one-period payoff equal to the higher of two stock prices and whether stochastic calculus is necessary. For jointly normal prices, the accepted answer rewrites the maximum as one stock plus a call-like payoff on the…

EquitiesOptionsDerivatives pricingStatistics
Quant Q&A

The document considers ways to assemble a spreadsheet comparing selected public companies across fields such as headquarters, market capitalization, earnings, revenue, free cash flow, debt, and cash. It describes a programmatic route using a finance data API…

EquitiesFactor investingStatistics
Quant Q&A

The document asks how an issuer can value a callable bond before knowing its option-adjusted spread. It explains the market practice described in the answer: construct a credit curve from noncallable bonds when possible, then use an interest-rate lattice or…

Fixed incomeOptionsDerivatives pricingVolatility
Quant Q&A

The document discusses ways to reduce sampling noise when simulating a Cox–Ingersoll–Ross interest rate process for option valuation. It recommends quasi-random sequences, with Niederreiter sequences given as an example, and control variates, suggesting a…

Fixed incomeOptionsDerivatives pricingStatistics
Quant Q&A

The document considers what to call the attribute that distinguishes a call option from a put option. Suggested terms include “CallPut,” “PutCall,” and “OptionType”; some systems also use “Right,” though one response cautions that this can be misleading…

OptionsDerivatives pricing
Quant Q&A

The document explains how least-squares Monte Carlo (LSM) values an American option and identifies when exercising is preferable to continuing to hold it. Working backward from terminal payoff, the method estimates each time step’s discounted continuation…

OptionsDerivatives pricingMachine learningStatistics
Quant Q&A

The document discusses a worked example from research on price impact, where market sells consume bid depth and limit bid additions replenish it. The response interprets the sequence in ticks: selling 15 shares against depth of 5 removes three levels, while…

Market microstructureExecutionStatistics
Quant Q&A

For a stock modeled by geometric Brownian motion, the document explains how to find the chance that its future price falls below a specified threshold. The logarithm of the price is normally distributed, with a mean adjusted for the volatility term and…

StatisticsEquitiesVolatility
Quant Q&A

The document distinguishes a forward price from a forward interest rate. A forward price is the amount agreed today for purchasing an asset at a future date; a forward rate is an interest rate set today for borrowing over a future period. Although bond…

Fixed incomeDerivatives pricingArbitrage
Quant Q&A

The document asks how to define a stock that fades through the day in a way that can be measured. An open-to-close decline alone may miss a rise earlier in the session followed by a sharp late drop. The questioner suggests requiring an early high of day that…

EquitiesStatisticsMomentumBacktesting
Quant Q&A

The document considers how to use rapidly changing limit order book features, such as spread and bid-ask imbalance, to predict a future midprice. It suggests testing recent history or aggregation and emphasizes that useful inputs may include time of day,…

High-frequency tradingMarket microstructureMachine learningExecution
Quant Q&A

The document addresses a data-matching problem in 13F research: issuer names in filings may differ from the names used in SEC records, making name-based searches unreliable. It points to a repository that describes a process for building a CSV mapping CUSIPs…

EquitiesUS markets
Quant Q&A

The document surveys approaches to estimating time-varying dependence with copulas in R. It points to vine copulas with evolving parameters as one research direction and mentions the CDVine package and related materials for statistical inference. It also…

StatisticsMachine learningPortfolio construction
Quant Q&A

The document raises two questions about reproducing a hidden Markov model study of factor investing across market regimes. First, it asks whether the reported regime labels and their return and volatility summaries use future observations to classify past…

Machine learningStatisticsBacktestingUS markets
Quant Q&A

The document explains why differentiating a Black–Scholes option price with respect to time does not include a derivative of the stock price. The option pricing formula treats time and spot price as separate arguments: a partial derivative with respect to…

OptionsDerivatives pricingStatistics
Quant Q&A

The document asks how to connect risk-neutral moments of simple gross returns to moments of log returns under a Black–Scholes model. It applies Itô’s lemma to obtain the stock’s terminal value, then uses the moment-generating function of a normal variable to…

Derivatives pricingOptionsVolatilityStatistics
Quant Q&A

The document compares an equal-strike interest rate floor with a receiver swaption on the same underlying forward rate exposure and asks which is more valuable. Under single-curve pricing, the receiver swaption’s payoff is no greater than the sum of the…

Fixed incomeOptionsVolatilityDerivatives pricing