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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 solving for the risk-aversion parameter in a mean–variance portfolio setup. Portfolio weights are expressed as a function of the parameter, expected returns, the covariance matrix, and a budget constraint. Those weights are substituted…

Portfolio constructionStatisticsRisk managementBacktesting
Quant Q&A

The document considers how to validate a low-frequency crypto strategy with roughly 25 trades per year, focusing on the limits of statistical evidence from a small sample and the dependence introduced by adding correlated assets. One reply suggests extending…

CryptoStatisticsBacktestingMachine learning
Quant Q&A

The document explains how correlation between two stocks’ Brownian shocks relates to correlation between their simulated price levels under correlated geometric Brownian motion (GBM). It gives formulas for each asset’s mean and variance, their joint…

EquitiesStatisticsBacktesting
Quant Q&A

The document explains that the VIX Flip indicator can be approximated with Williams VIX Fix, a synthetic volatility measure calculated from the highest close over a recent window and the current period's low. The reported formula expresses the gap between…

Technical indicatorsVolatilitySentimentBacktesting
Quant Q&A

The document asks whether EGARCH volatility forecasts are biased and seeks a published academic source supporting that claim. It identifies a preprint on bias correction for EGARCH estimators and a published study of finite-sample properties of EGARCH…

VolatilityStatisticsBacktesting
Quant Q&A

The document describes an attempt to check the optimal consumption and investment policy in Merton’s portfolio problem by simulating wealth paths and comparing average terminal utility with an analytical value function. It gives a discretized wealth update,…

Portfolio constructionBacktestingStatisticsRisk management
Quant Q&A

The document outlines a proposed backtest of a receiver swaption under a one-factor Hull–White or Gaussian Markov HJM framework. It describes forward rates in Musiela form, relates continuously compounded yields to average forward rates, and gives an…

Fixed incomeOptionsDerivatives pricingBacktesting
Quant Q&A

The document presents an attempted trinomial-tree implementation of an uncertain volatility model for pricing European calls between minimum and maximum volatility bounds. It lays out a backward-induction scheme, defines a discrete second-difference term…

OptionsVolatilityDerivatives pricingBacktesting
Quant Q&A

The document clarifies that geometric Brownian motion can be simulated by multiplying lognormal price relatives across time steps. In the direct approach, each step’s gross price ratio should itself be lognormally distributed, with log-mean and…

StatisticsBacktestingDerivatives pricing
Quant Q&A

The document considers how much historical data is appropriate for evaluating a trading strategy, especially one that trades frequently. It argues that the useful horizon depends partly on average holding period and market structure: longer-horizon…

BacktestingStatisticsRisk management
Quant Q&A

The discussion shows that the number of stocks worldwide depends on how the universe is defined and which source is used. It cites MSCI global index factsheets as a way to estimate investable coverage: one broad index included large, mid, small, and micro…

EquitiesMulti-assetBacktesting
Quant Q&A

The document compares ways to choose the drift parameter in a recombining binomial tree. It describes the Cox–Ross–Rubinstein construction, which uses symmetric log-price steps, and an alternative that adds a fixed drift to the up and down multipliers. It…

OptionsDerivatives pricingBacktesting
Quant Q&A

The document asks why correlations, regression coefficients, and R-squared change when monthly observations are expanded into daily observations using linear interpolation. Its central point is that the computed correlation depends on the observations…

StatisticsBacktesting
Quant Q&A

The document describes a daily-rolled backtest of EURUSD options using one-month and two-month straddles, 25-delta risk reversals, and 25-delta butterflies. Each position is held for one trading day, with the next day's valuation using the full volatility…

ForexOptionsVolatilityBacktesting
Quant Q&A

The document proposes choosing a retail time-deposit term by calculating the net present value of deposits with different maturities and converting each value into an equivalent constant annual payment over its term. It selects the term with the highest…

Fixed incomeStatisticsBacktesting
Quant Q&A

The document describes a bond discount-curve fitting error in QuantLib. The questioner supplies bond maturities and coupon rates alongside market prices, then constructs fixed-rate bond helpers for a Nelson–Siegel fitted discount curve. The reported…

Fixed incomeBacktestingStatistics
Quant Q&A

The document frames the Jane Street market prediction task as choosing whether to take each opportunity using anonymized features, trade weights, and realized outcomes. Its objective combines aggregate profit with a clipped Sharpe-like factor, so a…

Machine learningStatisticsRisk managementBacktesting
Quant Q&A

The document proposes generating trading alpha by predicting index additions and deletions before an official announcement. The suggested approach is to build an algorithm that tracks stocks in a target market and applies the index provider's published…

EquitiesEvent-drivenBacktesting
Quant Q&A

The document asks whether Bloomberg Barclays monthly total and excess returns can be reproduced from constituent-level index data, including constituent returns, market values, and excess returns. The author reports a close but imperfect tie-out for a broad…

Fixed incomeBacktestingPortfolio construction
Quant Q&A

The document summarizes screening practices for using Thomson Reuters Datastream in empirical equity research, with particular relevance to momentum strategies. It highlights risks from rounded prices, erroneous extreme returns, nonlocal listings, depositary…

EquitiesMomentumStatisticsBacktesting
Quant Q&A

The document examines whether removing factor exposure from a trading signal or from forward returns produces the same performance estimate. With a projection matrix P and residual-maker M = I − P, it writes the strategy’s cross-sectional return as the inner…

StatisticsFactor investingBacktesting
Quant Q&A

The document explains roll-rate analysis as tracking accounts from one delinquency state to another over a fixed interval, then questions whether published portfolio projections follow from the reported transition percentages. It sets up equations to infer…

StatisticsRisk managementBacktestingFixed income
Quant Q&A

The document outlines a practitioner framework for evaluating alternative data signals when simple relationships may already be widely known. It recommends writing a specific hypothesis and pass/fail criteria before inspecting results, with the decision…

Machine learningStatisticsBacktestingRisk management
Quant Q&A

The document asks whether a quantitative strategy’s useful life can be forecast as returns decay. It distinguishes two possible causes: other traders may discover and arbitrage a strategy, or market conditions may change. The main answer is skeptical:…

StatisticsBacktestingRisk management