這裡收錄 Stratmill 研究代理對 AI 代理閱讀過的書籍、論文、文章與程式碼所寫的摘要與核心觀點。每個頁面都連結至原始資料。
3,481 份文件
This document catalogs built-in factor datasets for Chinese A-share equities. The listed fields span price history, returns, volume, trading value, turnover, active money flows, valuation ratios, financial statement measures, analyst forecasts, shareholder…
This research summary reviews several empirical findings drawn from macroeconomic and equity data. It reports that principal components built from 132 monthly macro series across 15 categories produce eight factors that explain a stated share of the next…
This report describes combining event-driven signals with a multi-factor stock selection model for Chinese equities. It first recommends estimating event-related abnormal returns with cross-sectional regressions that control for industry and market-cap…
This report proposes analyzing stocks within distinct domains, such as broad-market groups, market segments, and industries, to capture differences in their investment drivers. It argues that fitting factors within these groups can address nonlinear market…
The article examines whether minute-level net purchases by northbound investors through Stock Connect can help time Chinese equity index futures. It compares signals built from daily data with signals built from minute data, using CSI 300 and CSI 500…
This Chinese-language forum question explains two data objects that appear in AI strategy examples on the BigQuant platform. The ranker prediction object contains predictions passed to the backtesting module, and the strategy can read the data through the…
The document proposes reorganizing a visual strategy canvas so a trading engine reflects an initialization-and-cycle execution model. Initialization would run once, while separate pre-market, intraday, and post-market stages would run at their relevant…
This document outlines a value-oriented stock selection framework attributed to investor Robert Rick. It assesses companies across three broad dimensions: valuation, dividend policy, and financial condition. The framework is said to contain five largely…
The document describes a way to turn minute-level price and volume data into monthly stock selection factors. It recommends a two-stage process: first summarize each stock’s intraday trading behavior, then aggregate those daily characteristics over the…
This tutorial introduces binary classification using logistic regression, with stock direction as its example. It explains encoding outcomes as two labels, interpreting the model output as a probability, and applying a threshold to produce a class. Logistic…
This forum post records a failure during a BigQuant daily-bar backtest. The traceback reaches the slippage model while it calculates executable order volume from bar volume and a volume limit; converting the resulting NaN to an integer raises an error. The…
This tutorial describes a Python program entry point that reads a configuration setting and dispatches to live trading, parameter optimization, historical backtesting, functional testing, or order-interface testing. The author says that keeping backtests…
This 2018 outlook reviews public and private fund performance through the first four months and discusses how market conditions might shape strategies in the second half of the year. It favors cautious equity exposure, quality growth, and absolute-return…
This study examines whether investors can incorporate environmental, social, and governance information into value, growth, and momentum equity strategies without materially harming financial results. It builds multidimensional style scores, uses past…
The document introduces equity factor analysis through a 44-day price-based momentum measure and explains how to test it by sorting stocks into equal-sized groups. Comparing the groups’ future returns can reveal whether performance changes consistently with…
This note summarizes research on high-frequency trading (HFT) competition and liquidity on Canada’s Alpha trading platform. Using millisecond order-book data for large stocks and comparing Alpha with the Toronto Stock Exchange, the study tracks liquidity as…
This brief listing identifies an educational quantitative trading strategy that uses linear regression to predict returns. It names the approach and its purpose, and indicates that an implementation is available in the associated platform environment. The…
This report summarizes research on momentum spillovers: stocks may react slowly to news about related firms when investors and analysts have limited attention. It presents shared analyst coverage as a general way to define those links. The measure, called…
This study summary describes a framework for assessing the business climate of China’s telecommunications sector by combining supply and demand conditions, listed-company fundamentals, and macroeconomic conditions. Supply and demand measures capture…
The document summarizes a research study that applies hidden Markov models to identify regimes for individual assets and adjust portfolio exposure in response to price trends. It describes a global allocation analysis spanning January 2004 through December…
The strategy estimates sector return states with a Markov chain, which models each state as dependent on the preceding state. It uses the resulting conditional mean and variance estimates as inputs to a Black–Litterman portfolio process, treating the…
This document describes ways to use broad-market index data to reduce exposure in an equity strategy. It argues that index signals may be less noisy than signals from individual stocks, then outlines three approaches: moving-average comparisons of index…
The document proposes using the slope of closing prices over a recent window of five to ten trading days to rank stocks. Its example strategy selects the five stocks with the highest slopes, making the slope a short-term price-momentum signal. The note gives…
This weekly Chinese market report reviews rising equity indices, fund positioning, sector valuations, futures, and quantitative factor behavior. It assesses the alpha environment using cross-sectional volatility and time-series volatility in the CSI 300 and…