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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

16,761 documents

Quant Q&A

The document describes a practitioner’s attempt to extend univariate GARCH risk analysis to a three-series DCC-GARCH model. The workflow fits separate conditional volatility models, combines them through a dynamic conditional correlation specification, and…

StatisticsRisk managementBacktestingPortfolio construction
Quant Q&A

The document describes a one-minute foreign-exchange backtest that chooses a trade direction using the next bar’s close. Because that information is unavailable at entry time, the apparent advantage is a look-ahead bias rather than a usable signal. It also…

ForexBacktestingRisk managementExecution
Quant Q&A

The document discusses ways to backtest a daily signal that indicates buying, selling, or holding an equity position. It names R packages for strategy testing and portfolio analysis, while noting that a simple strategy can also be modeled with return and…

BacktestingEquitiesExecutionRisk management
Quant Q&A

The discussion considers a trading research process that clusters historical intraday observations, such as daily vectors of hourly exchange rates, and then measures the next day's average return for each cluster. A researcher might use the return associated…

Machine learningStatisticsBacktestingForex
Quant Q&A

The document discusses timestamp reliability in free historical data from Gain Capital, especially for combining non-FX instruments or multiple feeds in a backtest. One respondent reports that observations were correctly ordered within individual CSV files…

Market microstructureBacktestingExecutionForex
Quant Q&A

The post considers how to decide whether an implied volatility smile has unusually large skew. It defines local skew as the slope of implied volatility against log moneyness, while noting that the raw slope depends on the volatility level and may need…

OptionsVolatilityDerivatives pricingBacktesting
Quant Q&A

The discussion addresses building a daily indicator for which Eurozone government bond is cheapest to deliver against a bond futures contract, for use in a repo-rate panel regression. It gives a Bloomberg Excel history query for the futures CTD identifier…

Fixed incomeFuturesStatisticsBacktesting
Quant Q&A

The post considers lag-one sample autocorrelation in daily stock log returns and asks when its scaled estimator is asymptotically standard normal under a model with normally distributed returns. It seeks a reference for the assumptions that justify this…

EquitiesStatisticsBacktesting
Quant Q&A

The document discusses implementing the nine Piotroski F-score signals with company financial data. The response treats the questions as accounting and data-mapping issues, and recommends consulting the original research for precise definitions. It suggests…

EquitiesFactor investingStatisticsBacktesting
Quant Q&A

The document explains how to forecast a lower-tail, two-day Value at Risk (VaR) from a Gaussian GARCH(1,1) model and how to evaluate the forecasts. Rather than using the fitted, in-sample quantiles as the main backtest, the accepted answer recommends…

EquitiesVolatilityRisk managementBacktesting
Quant Q&A

The note derives a Monte Carlo estimator for a European log-contract whose terminal payoff is the logarithm of the asset price relative to its initial price. Under risk-neutral Black–Scholes dynamics, Itô’s lemma gives a lognormal terminal asset price, which…

OptionsDerivatives pricingStatisticsBacktesting
Quant Q&A

The document raises a measurement concern for historical tests of covered interest rate parity (CIP). It notes that interbank forward quotes are commonly expressed against the US dollar, with a stated exception, and asks whether inconsistencies between…

ForexArbitrageCarryMarket microstructure
Quant Q&A

The document explains how to interpret a table reporting the risk of a dynamically delta-hedged option position. It describes simulating stock-price paths at the stated weekly frequency, computing the option delta at each step, and adjusting the share…

OptionsDerivatives pricingVolatilityRisk management
Quant Q&A

The document considers whether historical VaR for a single instrument can be extended to a one-year horizon. The main answer warns that long-horizon estimates are highly sensitive to the assumed or estimated mean return. Since drift is difficult to estimate…

Risk managementStatisticsBacktesting
Quant Q&A

The document seeks practical examples of spreadsheet and database applications used on trading desks or in bank middle offices, particularly to prepare for interviews. Responses point to several kinds of learning material: QuantLib as a source of tested…

OptionsFixed incomeBacktesting
Quant Q&A

The document explains why dates supplied to QuantLib’s futures rate helpers may not appear as nodes in a bootstrapped yield curve. A futures contract provides a forward rate for an accrual period, so its start date is not the tenor date inferred by the…

FuturesFixed incomeBacktesting
Quant Q&A

The document examines why a single simulated geometric Brownian motion path with zero drift can produce a maximum likelihood drift estimate far from zero. It defines log returns and gives the likelihood setup used to estimate drift and volatility, then…

StatisticsEquitiesVolatilityBacktesting
Quant Q&A

The document explains why realized volatility used to evaluate forecasts is not directly observable. Researchers must choose a proxy, such as one derived from returns, and recognize that the resulting measure depends on assumptions about prices, sampling,…

VolatilityStatisticsBacktesting
Quant Q&A

The document asks how to represent bracket orders in a Python backtester. A bracket consists of an entry order linked to child exit orders, typically a stop loss and a take profit, with one exit canceling the other after execution. The response recommends…

BacktestingExecutionRisk management
Quant Q&A

The document considers estimating hedge ratios for spreads between Treasury futures when historical DV01 data are unavailable. It describes empirical DV01 as an estimate from a regression of futures price changes against changes in the yield of the…

FuturesFixed incomeStatisticsBacktesting
Quant Q&A

The document discusses how to organize historical equity data so backtests can handle delistings, acquisitions, ticker changes, dividends, and splits. One proposed design assigns stable internal identifiers to companies and separately tracks stock series and…

EquitiesBacktestingEvent-drivenStatistics
Quant Q&A

The document addresses a discrepancy between historical stock prices shown in a finance website interface and values in its downloadable data. The example shows an apparent inconsistency between daily high and low values and the displayed close, alongside…

EquitiesStatisticsBacktesting
Quant Q&A

The document describes a fixed-risk position-sizing approach used in spread betting. The trader chooses a fraction of account equity to risk, measures the distance between entry and stop in points or pips, and divides the cash risk allowance by that distance…

Position sizingRisk managementBacktesting
Quant Q&A

The document considers whether antithetic variates must produce a positive Monte Carlo estimate for a European call when every payoff from the original simulated draws is zero. The answer is no: pairing each draw with its antithetic counterpart does not…

OptionsDerivatives pricingBacktestingStatistics