নলেজ লাইব্রেরি

This BigQuant assignment asks learners to modify a classic quantitative strategy by adding an indicator, then search across hyperparameters. It directs them to collect the historical performance curve for every parameter configuration into a data frame, plot…

This weekly market review summarizes Chinese economic and financial developments for August 8–12, 2022. Its macro section reports trade growth and foreign-exchange reserve changes, alongside selected global policy and company news. The market section…

This brief strategy note outlines an equity selection approach using a random forest regression model. It adds features such as beta and technical indicators, including MACD and Williams %R, and predicts a return-style target calculated from the difference…

This research summary proposes measuring whether trading in a stock is unusually one-sided. It classifies five-minute candles by their bodies as consensus or non-consensus activity, then measures consensus volume as a share of daily volume. Splitting that…

This literature summary describes research on the historical characteristics and investment relevance of sentiment signals drawn from news and social media over a 20-year period. The underlying study compares the information in the two source types, examines…

This document is a brief summary of an A-share quantitative market-timing report titled around the possibility that a lower open can offer an opportunity. It reports contemporaneous observations on sector strength, index valuations, sector relative…

This research summary surveys dynamic allocation methods for equity style factors. It organizes the approaches by whether they use return information, risk information, or both, adapting methods originally used in broad asset allocation. The study first…

This document explains how to combine absolute, time-series momentum with relative, cross-sectional momentum in a multi-asset portfolio. It describes a unified portfolio framework that treats each pair of instruments as a relative-momentum opportunity,…

This report compares several Chinese language models as assistants for discovering equity liquidity factors over a specified historical period. Each model proposes data inputs and a formula built from trading volume, turnover, amount, or price. The author…

This weekly report assesses the Chinese equity market after a rebound in early June 2022. Its timing framework compares 20-day and 120-day moving averages for a broad market index and combines their distance with a market breadth or “profit effect” measure.…

This article proposes six checks to run before deploying a strategy that has performed well in historical backtests. It recommends perturbing data with delays, missing observations, and outliers; scanning core parameters for broad regions of acceptable…

This brief Chinese-language forum answer addresses how to use the data parameter in a quantitative trading workflow. It notes that scheduled functions receive both data and context as inputs, pointing readers toward the platform documentation for details.…

This document summarizes a Chinese research report on measuring listed companies’ technological innovation capacity. It identifies factor construction and testing by industry as a central topic, alongside building an enhanced portfolio for a strategic…

This study proposes a stock-selection factor derived from intraday volume patterns. It defines a daily volume “tide” by smoothing minute-level volume with a nine-minute neighborhood, locating its peak, and identifying low-volume points before and after that…

This report examines industry exposures in an existing multi-factor stock-selection portfolio, tests how the model performs across sectors, and constructs industry-neutral long and short portfolios. It identifies sectors where the model appeared unsuitable…

This note offers informal Sharpe-ratio thresholds for screening strategies under a stated evaluation context: a single strategy rotating half-capital among at least three stocks, with million-level capacity and a backtest covering roughly three years. It…

This commentary describes six measures it says would reshape quantitative trading in China’s A-share market: adding network latency, sharing or relocating trading ports, capping order activity, requiring strategy registration and disclosure, imposing…

The document reviews three quantitative allocation approaches: rotating between equities and bonds, flexible multi-asset allocation, and multi-asset trend following. Each approach is offered at conservative, moderate, and aggressive risk levels, allowing…

This support note explains a model-training failure attributed to problems in the input data. The specific diagnosis is that an erroneous expression caused the buy-condition factor to fail extraction. It also notes that the dataset had only two factors, both…

This BigQuant example builds a binary classifier from rolling windows of daily open, high, low, close, and volume changes. Its network applies a convolutional layer to each 50-step, five-feature input, passes the result through an LSTM and dense layers with…

This review evaluates an RSRS market timing strategy out of sample across several Chinese broad-market indices from March 2017 to November 2019. It reports generally positive timing results, strongest on the CSI 300, while the CSI 500 results were more…

This short assignment defines quantitative investing in practical terms as using computers to carry out investment work. It identifies consistency within a fixed framework and the ability to hedge or run multiple trading styles as potential advantages. These…

This research note outlines a framework for evaluating funds and constructing allocations across active equity funds, enhanced index funds, and active bond funds. For equity funds, it discusses classification and performance attribution using both holdings…

The document describes a low-lag trend line (LLT) designed to reduce the delay that comes with smoothing price data. It frames moving averages as a trade-off: longer windows smooth noise more effectively but respond more slowly, while exponential averages…