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The document reviews research on whether crowded mutual fund ownership predicts subsequent stock returns. It defines stock-level crowding as active mutual fund ownership divided by average share turnover, using lagged holdings and turnover data. Stocks are…
This document describes the first BigQuant strategy development interest group, scheduled for May through July 2024. Its purpose is to help learners build strategy templates and demonstrations, reproduce common methods, and improve their implementation…
This page presents a beginner-oriented quantitative trading curriculum for readers who may lack finance background. It points to introductory material on Python, Pandas, historical market data, financial data handling, and data visualization. It also lists…
This research summary tests whether industry returns left after removing common style exposures can help rank sectors for rotation. It reports a strong difference between markets: an alpha based on the Fama–French three-factor model works for US industries,…
This tutorial explains how convolutional neural networks can improve on a simpler model for classifying MNIST handwritten digits. It reports accuracy of about 99.2% for the CNN, compared with 91% for the earlier model, but gives no experimental setup or…
The article describes a staged approach for managing a losing stock position. It advises against repeatedly buying during a decline and suggests waiting for a pause in falling prices alongside an expansion in volume before adding. After a rebound, it…
The document proposes a cross-sectional trading factor that combines price location, order book imbalance, trading activity, and volatility. Its price component is the percentage difference between VWAP and the midpoint, intended to reflect whether…
This semiautomatic Expert Advisor opens a trade when price crosses a chart line. It supports trendlines, angle-based trendlines, vertical lines, and horizontal lines, with options to follow the line’s stated direction or restrict entries to buys or sells.…
This research describes a systematic trend-following strategy for commodity futures. It first uses MACD crossings, filtered by cumulative distance relative to ATR, to classify rising and falling trends. It then revises those classifications when price…
The article proposes a discretionary method for managing a losing stock position. It cautions against adding shares during an ongoing decline and instead waits for apparent stabilization near a prior low, including sideways action and increased volume. After…
This overview introduces artificial intelligence as the design of systems that make decisions or predictions, including methods such as machine learning and probabilistic reasoning. It explains why these methods can help with complex tasks involving many…
This research summary reports tests of alternative and growth factors for selecting Hong Kong stocks. Of 17 alternative factors examined, most showed relatively weak selection effects; realized return kurtosis over 240 days and the ratio of recent to…
This research summary adapts the Merrill investment clock to Chinese A-shares by grouping sectors according to their economic-cycle behavior and proposed roles across recovery, overheating, stagflation, and recession. It describes two index families:…
This 2018 research summary assesses the outlook for Chinese equities in 2019 using earnings, credit growth, producer prices, retail sales, and valuation. It argues that slower growth and gradual valuation improvement limit broad market upside, and advises…
This article recounts episodes in China’s A-share market where stock prices were said to react to unrelated public events, celebrity news, wordplay, ticker numbers, entertainment, or product associations. Examples include stocks linked by similar names to…
The document defines a price-based momentum factor as the 44-day average closing price divided by the current close, minus one. It compares two ways to analyze the factor: using a platform expression engine and supplying custom data. The stated test covers…
The report summary describes a two-stage stock selection process intended to enhance a fundamental factor model. First, the fundamental model creates an initial stock universe; a deep learning model then screens that set again. The stated rationale is to…
This post raises questions about the usefulness and timing of Chinese stock opening-auction data. It proposes deriving unmatched order volume by subtracting cancellation and matched-trade volume from total submitted volume, then estimating an unmatched…
This support note explains an apparent mismatch between the number of shares bought and sold in a stock backtest. A user observed buying 300 shares of China Yangtze Power and later selling 310, and asked how to correct the discrepancy. The reply attributes…
The discussion addresses a question about why a strategy cannot retrieve current factor values. The reply suggests the issue may be that some stocks or dates produce missing values, rather than that factor retrieval has failed altogether. It recommends…
This forum post investigates a simulated-trading run that fails while rendering performance charts with BigTrader v9. The author suspects that the newer module cannot draw results when the run covers only one day, unlike an earlier backtesting module.…
This page summarizes a research paper on using intraday price and volume data to build enhancement signals. Its outline moves from converting high-frequency observations into lower-frequency features, to using genetic programming to discover price-volume…
This introductory guide describes a supervised machine-learning workflow for quantitative equity strategies. It begins by defining the stock universe and target, such as a future return, volatility, or ranking, then splitting historical observations by time…
This forum question compares portfolio holding ratios under two execution schedules: buying with one TWAP setting and selling with another, versus buying at the open and selling at the close. It provides a trading function for a ranked stock strategy that…