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.
2,456 documents
The article presents five principles for short-term stock trading: prominent stocks may attract liquidity despite looking expensive; near-term prices reflect the balance of buying and selling shaped by expectations and sentiment; traders should seek gaps…
The study measures a fund’s risk shifting by comparing the volatility implied by its latest disclosed holdings with the fund’s realized volatility over the same rolling period. Using quarterly holdings and return data for actively managed US domestic equity…
This research outline proposes allocating among equity industries by tracking the behavior of different market participants. It motivates industry rotation with the observation that returns can diverge substantially across sectors and styles, so broad asset…
This document outlines an event-driven study of how MSCI inclusion announcements affected the prices of Chinese A-shares. It describes estimating CAPM parameters from a historical period, using those parameters and subsequent market index returns to…
This tutorial compares three ways to train an XGBoost model for stock selection: ranking securities by a score, classifying outcomes into categories, and predicting a numeric target through regression. It frames these choices within a broader modeling…
This brief support note addresses a BigQuant workflow where a ranking strategy appears to backtest normally but produces no rebalance signals in simulated trading. It points to configuration and data-window checks: bind the code-list module’s end date to…
The document introduces a moving-average arrangement scoring model, or MASS, that assesses market direction and trend strength from the relative ordering of multiple moving averages. It aims to combine the smoothness of longer averages with the quicker…
This tutorial explains how to use Seaborn to explore financial data through matrix plots, plot grids, regression plots, and style settings. It uses stock financial statement data to demonstrate correlation heatmaps, including annotations and color maps, and…
This Chinese-language question and answer explains why a strategy’s apparently strong later years in a long backtest may not reproduce the same pattern when tested over those years alone. It identifies several possible causes rather than prescribing a single…
The page reports a user’s concern that the Chinese stock 600256 had incorrect values for the total-liabilities factor fs_total_liability_0 over a historical interval in 2021. The user says values for other periods agreed with Eastmoney data, while the…
This BigQuant user question concerns a feature expression that calculates how many days have elapsed since a limit-up event within a recent window. The example marks sessions where return exceeds a threshold and the close equals the high, then uses a rolling…
This report introduces a quantitative research approach that combines behavioral finance with trading indicators. It centers on George Soros’s theory of reflexivity and the author’s use of volume measures, with the stated aim of developing an indicator…
The article tests whether a convolutional neural network can classify stock direction from chart-like images generated from OHLC data. Each sample uses 32 time steps, normalized to a 128-by-128 binary image: groups of columns mark open, high-low range, and…
This article outlines a machine-learning stock selection strategy intended to find shares that may rebound after declines while limiting drawdowns during weak market conditions. It targets China’s small and medium-sized board, chosen for its activity and…
This guide explains how to participate in a BigQuant quantitative challenge using A-share minute bars and order-book snapshots to predict future 30-minute VWAP returns. It covers the factor-mining and end-to-end modeling tracks, available templates and data…
The document describes Temporal Routing Adaptor (TRA), a way to extend a stock prediction model so it can learn from different patterns in market data. It notes that momentum and reversal behavior may coexist, which challenges the assumption that…
This research summary explains how to build a machine-learning stock-selection process using historical factor values to predict subsequent returns. In the training stage, a supervised model learns the relationship between inputs and returns; in the testing…
The document summarizes CapTE, a model for predicting stock movements from social media text. A Transformer encoder extracts semantic features from posts, while a capsule network is used to represent structural relationships in the text. The approach is…
This short platform discussion explains that an adjust factor is used to convert a stock’s real price into an adjusted price. Adjusted prices, including forward- and backward-adjusted series, are intended to keep price charts continuous across corporate…
This sample describes a high-dividend stock-selection model for Chinese equities. The process excludes special-treatment stocks, suspended securities, and Beijing Stock Exchange listings. It then screens for larger companies by market-capitalization rank,…
This forum post reports a suspected data-quality problem in a Chinese stock valuation dataset. The author observed that the September 14, 2022 snapshot appeared to contain more than 1,600 missing or erroneous records, while the adjacent dates seemed to have…
This tutorial shows how to implement a collection of Chinese stock features and screening rules in BigQuant AIStudio 3.0. It divides them into expression features and expression filters, then explains that the same calculations can be entered as a SQL query.…
This Chinese-language research digest summarizes two separate topics. The first reviews the United States target-date fund market, covering market share and flows, relative performance among fund series, and glide paths. It discusses glide-path averages and…
This report describes a Chinese equity index-enhancement strategy built from a composite stock-selection signal and portfolio constraints. It combines factors spanning company size, valuation, growth, profitability, technical behavior, liquidity, and…