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.
1,441 documents
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…
The document describes a commodity futures strategy that ranks 28 markets by changes in Twitter-derived sentiment. It calculates daily sentiment from keyword-matched posts using a financial sentiment dictionary, then forms equal-weighted long and short…
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…
This forum post presents a workflow for combining predictions from three model outputs. It merges the datasets on instrument and date, preserves columns that are not already present, renames each model’s prediction column, and computes their arithmetic mean…
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 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 forum post raises an implementation question about deploying BigQuant StockRanker models for live trading through a brokerage server. The author believes StockRanker includes a gradient boosting decision tree model and asks whether deployment transfers…
The report describes a CTA approach for Chinese stock index futures that combines weekday return patterns with intraday effects. Its analysis notes higher return probabilities overnight and during the first half hour after the open, and different weekday…
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…
This discussion examines whether the length of a model’s training window changes an AI strategy’s results. It describes manually rolling training for a visual template strategy, comparing longer histories of five to ten years with shorter windows ranging…
The document answers how to allocate weights across strategies in a multi-strategy backtest. Its proposed workflow is to extract each strategy’s daily return series and use an optimization package to find portfolio weights. This frames the task as portfolio…
The document describes a basic workflow for evaluating a trained quantitative model. After fitting the model on training data, apply it to a validation set, then compare its predictions with the observed values to assess performance. This separates model…
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…
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 short forum exchange explains how to configure BigQuant’s trading engine to rebalance on a weekly or monthly schedule. For weekly scheduling, it specifies the weekly trading-day mode and a day value of 5; for monthly scheduling, it specifies the monthly…
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 report surveys several approaches to allocating across asset classes: macro and cycle-based fundamentals, mean-variance optimization, Kelly-CVaR, Black-Litterman, and risk parity. It describes a macro model that separates directional forecasts from…
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…
The report outlines a framework for timing equity factors whose performance has become less stable. It first examines indicators such as valuation spreads and pairwise correlations, testing their relationship with future factor returns. It then uses a random…
This assignment response translates two discretionary stock approaches into rule-based proposals. One combines recent institutional fund inflows, positive company earnings, improving per-share profit, elevated trading volume, and a price ceiling relative to…
This forum post describes an AttributeError in a BigQuant high-frequency backtest. The copied trade-module code treats each key in the portfolio positions mapping as an object with a symbol attribute. In the HFTrade interface, the key is already a string…
This research summary examines quantitative stock selection among Chinese technology companies. It highlights research and development spending as a candidate signal and also discusses profitability, earnings growth, valuation, company size, turnover, and…
This example builds a simple portfolio analysis workflow that generates a daily value series for several allocation weights and plots the paths together. A configuration object holds the tested weights, chart dimensions, and date range. The demonstration's…