Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
3,481 documents
The article presents a short-term stock screening pattern built around three sequential signals. First, it looks for at least five generally rising sessions, allowing one modest down day while the overall movement stays upward. Next, it requires a session…
This tutorial outlines a TensorFlow LSTM workflow for forecasting stock or index returns. It recommends transforming closing prices into returns, then arranging recent observations into sequential input groups with the next observations as prediction labels.…
This research summary compares ordinary cross-validation with time-series cross-validation for model tuning on sequential data. Randomly assigning observations to folds can train a model on future observations and validate it on the past, violating the…
This competition update reports diagnostics for the ten highest-ranked anonymous factors and tracks a CSI 1000 enhancement portfolio. Factor profiles include average Barra style exposures and sector-level average Rank ICs, calculated from cross-sectional…
The document introduces Moving Average Envelopes (MAE), a technical indicator built from a moving average of closing prices. It describes two bands: a lower envelope set below the average by a specified percentage and an upper envelope set above it by…
This study proposes adding daily risk monitoring to a traditional monthly rebalancing schedule for multi-factor equity portfolios. It tracks the portfolio’s exposure to the size factor during the month and rebalances on the next trading day when that…
The article summarizes research on whether forced mutual fund selling affects the prices and liquidity of related stocks. It describes a learning mechanism: investors may initially interpret a peer stock’s decline as evidence of negative information about…
The report describes an expanded Black-Litterman asset allocation model that combines subjective views with quantitative portfolio construction. It uses behavioral finance and historical trend information, including momentum and reversal effects, to form…
This example tests whether market capitalization is associated with next-day stock returns using repeated cross-sectional ordinary least squares regressions. It joins Chinese stock factor data to daily bars, calculates forward returns from the next close,…
This research summary examines the validity of mainland China Stock Connect ownership data and describes portfolio strategies built from investor holdings, sector allocations, style exposures, leading trades, and net capital flows. It reports persistent…
This study builds a monthly Chinese equity factor from stock returns and volatility around intraday volume surges. It defines a surge as a minute when the increase in trading volume exceeds that stock’s daily average increase by more than one standard…
This Chinese market note proposes that China’s credit impulse may be turning upward and argues that it tends to lead A-share earnings growth. On that assumption, it recommends overweighting Chinese equities, favoring growth over value, and ranking…
This reference catalogues TA-Lib functions and their input fields for calculating common trading indicators. It groups examples into overlapping price studies, volatility and volume-price measures, cycle functions, price transformations, momentum indicators,…
This stock selection method turns a discretionary approach into a set of technical and liquidity filters, followed by periodic rebalancing. It looks for prices mostly above long-term moving averages, favorable short-term moving-average conditions, and moving…
This Chinese-language article discusses the proposed introduction of market making on China’s STAR Market. It explains that market makers continuously quote prices, sell inventory to buyers, and use their own capital to buy from sellers. They bear price risk…
This BigQuant forum post presents a daily-bar backtest example that submits several limit buy orders during a bar callback. Before each order, the callback reads available cash and frozen cash from the trading account. The reported output shows those values…
The article summarizes research on whether active mutual fund managers adjust trading to anticipated stock-picking opportunities. It defines stock-selection timing as trading more when opportunities to hold positive-alpha stocks are greater and less when…
This overview surveys unsupervised learning through two groups of methods: dimensionality reduction and clustering. For dimensionality reduction, it describes PCA as a way to project high-dimensional features into fewer dimensions using covariance-matrix…
This study compares conventional return labels with alternative labels based on information ratio and Calmar ratio for AI stock selection models. It argues that labels incorporating returns, volatility, and drawdowns across a period may convey more…
This document outlines a simple KDJ-based trading strategy for the Chinese stock 605588, with the CSI 300 index named as a reference benchmark. It describes selling when K, D, and J are all in high territory, and buying when all three are in low territory.…
This article presents three MACD-based warnings for managing long equity positions: the indicator moving below its zero line, bearish divergence as price makes higher highs while MACD peaks decline, and a bearish crossover above zero. It interprets these…
This article outlines a research approach for Chinese equities during sharp market-wide declines. It proposes identifying historical selloff days and studying two groups: stocks that hit their upper price limit despite the market drop, and stocks that hit…
This note summarizes the AlexNet deep convolutional neural network described in the ImageNet classification paper. It outlines the model’s use of multiple convolutional channels, five convolutional stages and three fully connected layers, and explains how…
This strategy applies a stock-ranking model to a filtered pool of Chinese A-shares, aiming to find stocks that may rebound from depressed levels and hold up better when the broader market weakens. The target universe is the small and medium-sized board. Its…