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.
5,922 documents
The document explains a stochastic-control model for an arbitrageur trading a mean-reverting spread, such as an equity pairs position. It models mispricing with an Ornstein–Uhlenbeck process, allowing convergence timing to be uncertain and the spread to…
This overview explains how a stock-selection model can combine several signals to rank securities or construct a portfolio. It surveys market and systematic-risk measures, valuation ratios, growth and profitability metrics, momentum or reversal, trading…
The document outlines conservative investing as an approach that prioritizes limiting losses and seeking dependable returns over pursuing the highest possible growth. It presents long holding periods and restrained trading as ways to reduce the influence of…
This example organizes an AI-assisted sector ETF strategy into research pods for technology, financials, healthcare, energy, and consumer sectors. The pods rank ETFs using recent news and macroeconomic context, with optional company or sector filings where…
The article discusses why leading Chinese quantitative private funds have become less visible among the largest shareholders of listed companies. It reports that several prominent firms and their products had appeared on such shareholder lists in earlier…
This configuration specifies a Qlib experiment that trains an ALSTM model on China’s CSI 300 universe and evaluates stock selections in a portfolio backtest. The data handler uses Alpha360 features, robust feature normalization, missing-value filling, and…
This report proposes using public mutual-fund holdings in Chinese equities to build portfolios in two stages. First, it selects funds by Sharpe ratio and rebalances the selected group quarterly. Next, within those funds' ten largest holdings, it identifies…
This Chinese-language report summary surveys how artificial intelligence may be applied across asset management. It describes hedge funds and investment banks adopting AI, then discusses potential uses in investment research, trading, risk management,…
This quarterly review examines China's equity index fund market in the second quarter of 2018. It describes faster ETF and LOF product development across broad-market and sector indexes, alongside the shrinking assets of leveraged graded funds. The report…
The document explains how Hyperliquid account abstraction modes determine whether spot and perpetual-futures balances share collateral. Unified accounts combine balances by asset across spot and eligible cross-margin positions. Portfolio margin pools…
The article examines whether alternative equity weighting strategies outperform market capitalization weighting because of factor tilts or because they capture diversification return. It defines diversification return as the difference between a portfolio’s…
This research review examines why the traditional book-to-price value factor underperformed growth stocks over an extended period. It argues that book value misses internally developed intangibles such as software, brands, research, and customer…
This Zipline example builds a daily long-short equity portfolio from the three assets with the highest RSI and the three with the lowest RSI. It assigns each selected long a target weight of one third and each short a target weight of negative one third,…
The document outlines Two Sigma’s systematic investment approach and briefly describes its China macro strategy, which invests across Chinese commodity and financial futures. It presents a process built around research, portfolio construction, and risk…
This study explores genetic programming as a way to discover formula-based signals for commodity futures trading. It adapts a genetic programming package to search combinations of market data and operators, aiming to find signals that could complement…
The document shows a stock-ranking workflow that derives 30-day and 90-day returns, ranks securities cross-sectionally, filters the universe, and creates a forward-return label for model training. A StockRanker module trains on historical data and predicts…
This discussion presents a daily Chinese A-share small-cap strategy that filters stocks by listing age, valuation, and turnover rank, sorts the candidates by market capitalization, and targets a basket of twenty names. Its rebalance handler identifies…
This example describes an AI agent workflow for ranking and trading large-cap stocks. A researcher ranks the stock universe, bull and bear agents present opposing cases, and an interpreter turns the debate into target account weights. A trader agent reads…
The report studies how equity factors behave across groups of A-share stocks sorted by price amplitude, using ten groups from lower to higher amplitude. It reports that price and volume factor effectiveness rises as amplitude increases, while fundamental…
The document introduces covariance as a measure of how two variables vary together, defining it through the expected product of each variable’s deviation from its mean. Positive covariance indicates a tendency to move in the same direction, while negative…
This report summary presents a factor model for ranking managers of Chinese public equity and equity-oriented hybrid funds. It builds a manager-level investment index by weighting the funds each manager oversees by fund size, then evaluates candidate…
This report summary outlines a fixed-income-plus-equity portfolio design in three stages: strategic allocation, tactical timing, and capital management. Strategic weights are set with a mean-variance model using broad equity and bond indices, with periodic…
This live strategy dashboard describes a multi-asset perpetual futures system that combines factor signals, assigns target exposures across contracts, and submits passive limit orders. The listed seed factors cover reversal, momentum, premium, funding, and…
This document outlines a simplified Merrill Clock approach that classifies economic conditions using the direction of year-over-year GDP and CPI changes. It applies moving averages to reduce short-term noise and delays the economic data by one period in…