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

16,761 documents

Quant Q&A

The document discusses how to simulate trade prices that behave like a random walk. One answer recommends modeling the bid-ask spread and using Poisson or Hawkes point processes for order arrivals and cancellations. A price move can occur when the queue at…

Market microstructureHigh-frequency tradingStatisticsBacktesting
Quant Q&A

The discussion considers whether distribution fitting for financial returns should use simple percentage returns, log returns, or standardized observations. The accepted response favors percentage returns when fitting distributions such as Student’s t,…

StatisticsRisk managementBacktesting
Quant Q&A

The document asks how to obtain accurate intraday foreign-exchange data for analysis in R. One response points to an R package as a way to access FX data directly, potentially avoiding a separate Java download and CSV import workflow. Another response…

ForexStatisticsBacktesting
Quant Q&A

The document asks whether automated trading strategies tend to make or lose money and seeks comparative tests of simple strategies on historical data. It offers no such benchmark study or measured performance results. Instead, one response describes a…

BacktestingRisk management
Quant Q&A

The document describes building a six-month sterling Libor yield curve in QuantLib from a deposit quote and swap rates, then asks how to incorporate forward rate agreements. It shows the existing curve-building setup and its resulting spot-rate table,…

Fixed incomeForexDerivatives pricingBacktesting
Quant Q&A

The document asks how to check a Python implementation of delta hedging for a short European call, offset by a long position in the stock sized to the option’s delta. It proposes examining the distribution of the combined hedge-and-option profit and loss…

OptionsDerivatives pricingBacktestingRisk management
Quant Q&A

The document raises a calibration problem for classical short-rate models, including Black-Karasinski and Hull-White, during the sharp rate increases of 2022–2023. The author is concerned that model parameters may struggle to fit current rate levels while…

Fixed incomeDerivatives pricingBacktestingStatistics
Quant Q&A

The document explains why rows generated from a Sobol sequence repeat when the generator is reinitialized inside each asset loop. The generator should persist across draws, and its dimension should match the number of random coordinates needed for one…

StatisticsBacktestingMulti-asset
Quant Q&A

The document presents a Python implementation of Longstaff–Schwartz least-squares Monte Carlo for an American-style Asian call with exercise available at discrete dates. The payoff depends on the average underlying price observed up to exercise. The reported…

OptionsDerivatives pricingBacktestingStatistics
Quant Q&A

The document compares holding a cryptocurrency investment for a fixed period with periodically withdrawing gains while restoring the original principal. It describes a Monte Carlo example using simulated daily returns, then reports average outcomes for both…

CryptoMean reversionVolatilityBacktesting
Quant Q&A

The document collects suggestions for software and services that can be used to backtest investment strategies. The examples span a mobile portfolio tool, a Python framework, a form based service, standalone engines, and a brokerage platform with strategy…

BacktestingEquitiesFuturesForex
Quant Q&A

The note explains how to use trade-level profits with PerformanceAnalytics, whose functions generally expect return data rather than a raw list of trade P&L values. One suggested workflow is to record transactions in the blotter package, calculate cash P&L,…

BacktestingPortfolio constructionRisk management
Quant Q&A

The document distinguishes financial bars from statistical resampling methods. Time bars divide observations by clock intervals, while tick, volume, and dollar bars aggregate irregular transaction data until a chosen trade count, traded volume, or traded…

StatisticsBacktestingMarket microstructureHigh-frequency trading
Quant Q&A

The document considers how to model hedge fund returns for simulation, noting that return moments alone do not specify a distribution. One proposed approach represents returns as exposures to a broad set of tradable ETF factors. The cited research uses…

StatisticsPortfolio constructionMachine learningBacktesting
Quant Q&A

The document compares two numerical approaches for pricing European calls under the Heston–Nandi GARCH model using an inversion integral. One implementation evaluates the integrand through adaptive quadrature, while another precomputes…

OptionsVolatilityDerivatives pricingBacktesting
Quant Q&A

The post asks how to select conditional-mean terms when fitting GARCH-family models to South African, Australian, and US equity index returns. The author compares autocorrelation plots, information criteria, ARCH tests, and the significance of terms in…

EquitiesVolatilityStatisticsBacktesting
Quant Q&A

The document considers what is needed to simulate the year-end prices and portfolio variance of two stocks. It challenges the idea that historical means and standard deviations alone can be used to draw independent lognormal prices and average them. The…

EquitiesStatisticsBacktestingPortfolio construction
Quant Q&A

The document explains the standard portfolio drawdown workflow: convert periodic returns to growth factors, compound them into an equity curve, and measure losses from prior peaks. Maximum drawdown is the greatest decline from a previous high-water mark, so…

StatisticsRisk managementBacktesting
Quant Q&A

The document asks how to judge whether a derivative-pricing model improves on Black–Scholes when traders already use implied volatilities and interpolation to fit option prices. It distinguishes pricing from prediction: a pricing model can interpolate or…

OptionsDerivatives pricingBacktestingRisk management
Quant Q&A

The document addresses a data problem for researchers working with hedge fund performance: broker-provided daily index histories may begin later than the period they need. The question asks for free daily levels of HFRX or a comparable index with history…

Multi-assetStatisticsBacktesting
Quant Q&A

The document contrasts quantitative investing with quantitative trading, emphasizing the larger capital base and capacity requirements of asset management. Strategies that exploit small arbitrage opportunities may not scale to the size of investment…

Factor investingPortfolio constructionMachine learningBacktesting
Quant Q&A

The document frames how to measure performance for a strategy that opens long stock positions at different times and closes each after a short, fixed holding period. It asks how to make trade returns comparable, remove broad market movement, and combine the…

EquitiesStatisticsBacktestingPortfolio construction
Quant Q&A

The document asks how quickly the value of a Bermudan option approaches the value of an American option as exercise dates become more frequent. It describes the practical setting in which numerical methods use discrete exercise opportunities, and proposes…

OptionsDerivatives pricingBacktesting
Quant Q&A

The document compares ways to obtain historical company fundamentals for multiple stocks in machine-readable formats. The original request prioritizes several years of data, broad international coverage, index-level or identifier-based retrieval, and sample…

EquitiesMulti-assetStatisticsBacktesting