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.
36 documents
This code implements a collection of cross-sectional and time-series equity alpha factors, mainly using close, open, high, low, volume, returns, and VWAP data. The factors combine operations such as rolling ranks, correlations, moving averages, extrema, and…
This document outlines a two-stage workflow for calculating Alpha101 factors. First, it reads daily stock data, derives base series such as returns and VWAP, and computes time-series intermediate variables for storage. Later, factor construction retrieves…
This document is a historical price table for a broad set of country and regional exchange-traded funds. It lists dates alongside one price series for each ETF, with examples spanning markets such as Japan, Brazil, Germany, India, and the United Kingdom. The…
This note proposes screening Chinese metaverse-sector equities using two signals: rank by the day’s opening-auction value and retain the leading five, then require at least two limit-up events within a stated 500-day lookback. It includes platform-specific…
This code sample implements parts of the Alpha101 factor set using historical equity fields such as open, high, low, close, volume, returns, and volume-weighted average price. The formulas combine rolling ranks, moving averages, correlations, price changes,…
This Chinese equity screening proposal combines three conditions: daily price amplitude above 1%, a dividend ratio above 25% for 2019, and a 15-minute MACD histogram that is shortening while below zero. The rationale is to find volatile shares with a history…
This module describes ways to select groups of stocks for vine copula analysis, a component of a statistical arbitrage approach. It starts from price histories, calculates daily returns and ranked returns, and narrows candidate partners for each target stock…
This note proposes a short-term Chinese equity screen that selects stocks with a price amplitude above one, an appearance on the prior day's trading list with buying greater than selling, and a rising DEA indicator. The rationale is to combine elevated…
The document explains how to form and evaluate long-short stock portfolios, focusing on pairs trading. It compares hedge-ratio methods: ordinary least squares minimizes portfolio variance under a correlated random-walk and Gaussian framework, while total…
This implementation describes a pairs-trading method based on modeling the log price relationship between two stocks as an Ornstein–Uhlenbeck process. It constructs the spread as the difference between the stocks’ log prices, fills missing observations…
This method uses principal component analysis to separate broad equity return drivers from stock-specific residuals, then trades residual portfolios expected to revert toward equilibrium. Returns are standardized before estimating their correlation matrix;…
The Pearson approach forms equity pairs by ranking stocks on the correlation of their monthly returns during a formation period. For each stock, it selects the most highly correlated peers and combines their returns into a benchmark portfolio, using either…
This module describes selecting three partner stocks for each target in a four-stock vine-copula statistical arbitrage framework. It compares four approaches using ranked daily returns: a baseline that sums pairwise Spearman correlations, a multivariate…
This implementation describes a distance-based statistical arbitrage method for forming and trading equity pairs. In a training period, each price series is scaled using its own minimum and maximum, and candidate pairs are ranked by the sum of squared…
This implementation describes an equity pairs strategy that selects stocks with highly correlated historical returns, then compares each stock’s return with a portfolio of its selected peers. It estimates a regression coefficient during a formation period…
This stock-screening note selects companies in the beverage and alcohol import-export industry, requiring daily turnover between 3% and 12% and displayed best-bid volume greater than best-ask volume. It characterizes the turnover range as a liquidity filter…
This reference explains two utilities for copula-based trading research: a linearly interpolated empirical cumulative distribution function (ECDF), and a quick selector for candidate pairs. A standard empirical CDF is a step function, which can map sparse…
This document presents a Chinese stock screening rule that combines RSI below 65, seven consecutive sessions in which the close is no higher than the open, and a latest price above its five-day moving average. It frames the conditions as a way to identify a…
The introduction presents a machine-learning framework for selecting securities for pairs trading. It frames pair discovery as a search-space problem: limiting candidates to securities in the same sector may exclude useful relationships, while searching…
This Python class implements a broad collection of formula-based equity signals using daily close, open, high, low, volume, returns, and volume-weighted average price data. Its methods translate rank, correlation, rolling-window, change, volatility, and…
This Chinese stock-selection note combines three filters: MACD above its zero axis, a 2021-to-2018 revenue ratio above 1.1, and a gain below 6% at 9:25. The rationale is to pair positive technical momentum and multi-year revenue growth with a limit on the…
This module describes a candidate-selection process for pairs trading based on dimensionality reduction and clustering. It starts from a panel of asset prices, converts prices to returns, standardizes them, and applies principal component analysis to create…
This note explains a long-short pairs strategy that uses a copula to model the dependence between two stocks. After selecting a pair, for example with a cointegration test, the method fits the copula and each stock’s empirical distribution on a formation…
This Python class assembles a subset of Alpha101-style equity signals from price, volume, VWAP, returns, and precomputed factor series. Its methods apply operations such as cross-sectional ranking, rolling correlation, covariance, time-series ranking, decay,…