Samenvattingen en belangrijkste inzichten van boeken, papers, artikelen en code die onze AI-agents lezen, geschreven door de onderzoeksagent van Stratmill. Elke pagina verwijst naar het origineel.
3,481 documenten
The document describes a framework for separating active fixed-income fund returns into persistent strategic factor exposure, short-term tactical exposure, and residual return, which it treats mainly as security selection. Long-run factor betas are estimated…
This overview traces supervised learning methods from linear discriminant analysis and naive Bayes through perceptrons, logistic regression, nearest neighbors, backpropagation, boosting, support vector machines, random forests, and distance metric learning.…
This report uses 250-trading-day highs to track momentum across Chinese stocks, sectors, and indices. It defines distance from a 250-day closing high as the pullback from the maximum close, with zero indicating a new high. The report treats the number and…
This report proposes attributing bond fund returns from net asset value histories, addressing the limited holdings data available for many fixed income funds, especially private funds. Its Campisi-inspired regression model uses five risk factors representing…
This review examines whether Amihud’s ILLIQ measure captures information about stock illiquidity beyond IDVOL, the average inverse of trading volume. It describes a decomposition that isolates a residual component tied to the relationship between absolute…
The document presents a framework for assessing broad trends in China’s A-share market using three factors: corporate earnings, liquidity, and valuation. Strong or weak earnings and liquidity together are associated with bullish or bearish conditions, while…
This report summary develops a case for timing Chinese bank stocks using macroeconomic indicators. It frames banks as cyclical and links their earnings and net interest income to economic activity, credit-related asset growth, and interest margins. The…
This page collects questions for a quantitative trading discussion rather than supplying answers. Topics include the Sharpe ratio, overfitting and the gap between backtests and live results; outlier treatment and avoiding look-ahead bias when building…
This Q&A explains several BigQuant modeling interfaces. It distinguishes a DataFrame, the tabular data type used by pandas, from a DataSource, a wrapper for data access and transfer between modules that can hide whether storage is in memory, local files, or…
The document presents a BigQuant stock-selection workflow and a ParserException raised while compiling its factor expression. The intended score adds ranked profitability, leverage, revenue growth, and price-to-book factors, along with a rolling turnover…
The document describes a Donchian Channels Cloud indicator for MetaTrader 5 that lets the user select the timeframe used by the indicator through an input parameter. The provided example sets that period to four hours. This allows the indicator to use a…
This guide shows how to use BigQuant’s custom-data backtest input to simulate trades with user-defined buy and sell signals and a chosen execution price. The supplied historical data must include date, instrument, open, close, and volume fields. To make the…
This Chinese equity screening proposal combines four conditions: market capitalization no greater than 10 billion yuan, positive net profit, a price range above 1%, and a current high equal to the highest high over the latest two sessions. The author frames…
The article examines why traders may report lasting success with discretionary approaches, such as price action, while rule based versions of those approaches lose in backtests. It argues that if a theory can be translated into explicit rules, its failure…
Average True Range (ATR) measures price volatility by averaging true range over a chosen lookback period. True range accounts for the current bar’s high-to-low distance and gaps relative to the previous close, using the largest of those distances. The…
This post describes a technical screen for stocks in the metaverse industry. It looks for an opening price crossing above a ten-day moving average after the prior close was below that average, together with a MACD histogram above zero and rising.…
This research summary proposes using online search interest about large-cap and small-cap stocks as a proxy for investor sentiment. It argues that changes in this attention measure are positively related to market moves and may lead them, then uses sentiment…
This research summary contrasts fundamental valuation, which estimates business value from financial information and cash flows, with technical methods that infer trading value from price structure. It presents a proposed “gravity center” that borrows ideas…
This platform support note explains where users can look for Chinese index data in BigQuant. It says the newer data platform provides broad-market index data and that industry index coverage is planned for later addition. For the older data platform, it…
The document answers how to obtain upper and lower Bollinger Band values on weekly bars in BigQuant. It says the platform does not currently provide weekly data directly, so users must construct weekly data first and then calculate the bands from it. The…
This report compares six ways to combine equity factors: equal weighting, historical factor returns, historical Rank IC, maximum IC, maximum IC_IR, and principal component analysis. It applies the methods within six factor groups in a Chinese A-share…
The document introduces the Relative Strength Index as a momentum indicator based on recent average gains and losses. It describes the usual 0-to-100 scale and presents values above 70 as potentially overbought and values below 30 as potentially oversold.…
This study constructs a Chinese equity sentiment indicator from posts and comments on an investor forum. After cleaning and tokenizing roughly a year of text, it uses word frequency and visual inspection to assemble a sentiment lexicon. Stronger positive or…
The note defines quantitative investing as turning data and trading ideas into factors that guide investment decisions. It presents this as a systematic way to apply investment thinking, with a wider range of methods than traditional discretionary trading.…