Kokkuvõtted ja põhiideed raamatutest, teadustöödest, artiklitest ja koodist, mida meie AI-agendid loevad. Need on koostanud Stratmilli uurimisagent. Igal lehel on link originaalile.
3,481 dokumenti
This report summary explains diffusion indicators as measures of how broadly index constituents participate in an advance or decline. Using the CSI 300 and its constituents, it compares moving-average and rate-of-change versions, equal weighting with…
The report proposes using Benford’s law, the uneven distribution of leading digits found in many datasets, to study stock minute-volume data. From those statistics, it constructs an “institutional footprint” measure: higher values are interpreted as stronger…
This study examines how Chinese and US equity markets move together, with a focus on whether movements in one market help explain later movements in the other. It uses Granger causality tests on market returns and volatility, reporting evidence of two-way…
This Chinese A-share example builds a daily stock-ranking strategy using LightGBM regression. Its features combine market capitalization, recent price and turnover averages, dividend yield and price-to-earnings ranks, plus two custom factors. The target is a…
This discussion raises a data-reconciliation question: why historical prices retrieved from a Chinese equity data platform still differ from observed market prices after dividing open, high, low, and close by an adjustment factor. The example queries daily…
This research note describes two revisions to AlphaNet, a neural model that learns stock selection factors from raw price and volume data. Version two adds ratio features, replaces pooling and dense layers with an LSTM to capture temporal patterns, and gives…
This meetup page collects questions about quantitative trading on the BigQuant platform. Topics include searching for holding-period parameters in a default stock-ranking template, defining reusable Python modules, and building a workflow for developing…
This article proposes a defensive equity strategy that seeks oversold rebounds or bounces after a pullback. It draws inspiration from research on money-flow factors, including inflow, outflow, net institutional flow, and opening net flow, and proposes…
This research summary describes factors derived from operating financial statements and reports selected long-short results. It identifies changes in operating current liabilities as a notable factor, with a reported Sharpe ratio of 2.62 and annualized…
This research summary proposes stock-selection factors built from daily highs, lows, opens, and average traded prices, arguing that closing-price indicators alone miss information in price movement. It evaluates opening-price spikes, rebounds from intraday…
This support exchange concerns warnings from BigQuant’s feature extractor that it cannot find the open, high, low, close, and volume fields in its field mapping. The logs show the warnings recurring across multiple years while basic feature extraction still…
The document describes a method for testing factor effectiveness dynamically and selecting stocks within industries. It examines whether differences in style-factor exposure relate to differences in stock returns, then uses the results to form industry-based…
This guide describes how a BigAlpha competition participant can build equity factors using BigQuant’s DAI data engine. The specified universe is the historical membership of the CSI 1000, and the listed inputs include one-minute bars and order-book…
This overview explains the main stages of a machine-learning workflow for quantitative investing, using a fruit-selection analogy to introduce training data, labels, features, prediction, and validation. It recommends defining the market and stock universe,…
This research summary examines stock selection factors derived from operating financial statement items, especially changes in operating current liabilities. It reports that these factors showed selection ability, with the strongest cited result for a…
This research overview examines risk parity within the broader development of portfolio allocation methods. It describes several risk measures and risk-allocation principles, emphasizing Euler allocation to define each asset’s contribution to portfolio risk.…
This short forum post gives a data access pattern for retrieving historical benchmark or stock data from a trade module. The example requests closing prices and volume for a benchmark symbol over a specified lookback, using daily frequency, and assigns the…
The report describes a stock-selection strategy that predicts the future usefulness of seven style factors and adjusts their portfolio weights over time. It uses historical factor information coefficients (ICs), macroeconomic variables, and market variables…
This Chinese-language support exchange addresses a quantitative research notebook that restarts automatically after two features are added and feature extraction begins. The user reports that the visible CPU and memory figures have not reached their…
This article collects learning materials for applying machine learning to algorithmic trading, grouped into books, blogs, research papers, videos, and podcasts. The topics span neural networks, structured data, regression, clustering, nearest-neighbor…
This study considers whether a company’s decision to capitalize research and development spending conveys information about future project profitability. Because accounting rules allow judgment in deciding whether development costs should be capitalized, the…
This study turns unusual intraday stock behavior into a measurable event signal. It describes days when a stock repeatedly moves against the direction of the broader index, then uses correlation to screen for these cases. The resulting event samples are…
This study examines whether managers of equity-focused and mixed equity funds can anticipate shifts between market styles defined by company size, and whether any apparent skill persists. It identifies funds that ranked near the top around past style…
The document presents a SQL approach to estimating annualized variance for Chinese stocks. It first calculates daily close-to-close returns for each instrument, then applies a rolling 20-observation standard deviation, squares that value, and multiplies by…