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.
20 documents
This equity strategy ranks stocks by their trailing 252-day returns, after screening for average dollar volume above $10 million over 30 days. Each day before the market opens, it selects the three highest-ranked stocks. At a scheduled rebalance 30 minutes…
This QuantConnect example demonstrates estimating the QC500 index constituents through the platform’s built-in universe selection. It configures daily data resolution, sets a historical test interval covering 2018, assigns starting cash, and adds the QC500…
This system describes an adaptive long/short strategy for Binance USDⓈ-M perpetual contracts. It starts from five seed factors spanning momentum, reversal, funding, premium, and open interest, then evaluates additional candidates through a constrained factor…
This market-neutral strategy ranks USDT perpetual contracts using a weighted composite of cross-sectional price momentum and funding-rate information. It goes long the highest-scoring coins and short the lowest-scoring ones. Funding intervals are normalized…
This UMD strategy ranks stocks by returns over a long lookback while omitting the most recent month, then buys the strongest group and shorts the weakest. It updates the selections monthly, uses equal weighting, and delays positions by one period before…
The code describes three candidate factors for ranking crypto assets: an ATR-based volatility measure, a volume-distribution measure, and an inverse-price measure. The volatility function calculates ATR over 14 periods, ranks the observations, and maps the…
The document outlines an automated process for turning a natural-language crypto factor idea into a calculated signal and evaluation report. A language model identifies the factor’s direction and data needs, generates a JavaScript function, and the workflow…
This strategy selects a portfolio of NYSE stocks through liquidity, momentum, and return consistency screens. It excludes financial firms, ADRs, and REITs, then ranks eligible stocks by average dollar volume over 90 sessions. From that liquid subset, it…
This equity-selection strategy first screens tradable constituents of the China Securities Index 1000, excluding B-shares and stocks with negative price-to-earnings ratios. It sorts the remaining stocks by P/E, keeps the 1,000 lowest, then selects the five…
This document sketches a periodic stock-selection and execution process focused on the five smallest companies by market capitalization in the CSI 300 universe. On the first incoming market bar, the strategy selects the target basket and compares it with…
This framework example selects a changing equity universe by estimating each stock’s alpha relative to a benchmark. On a scheduled monthly selection date, it retrieves daily returns for a set of Dow constituents and the SPY benchmark, fits a linear…
This cross-sectional stock strategy ranks securities by price-to-book ratio, using book value per share calculated from total assets, total liabilities, and shares outstanding. It buys the lowest-ratio group and shorts the highest-ratio group, with the…
This example demonstrates a daily equity universe selection process using fundamental data. It filters for securities with available fundamental data and a price above $1, sorts the remaining names by daily dollar volume, then ranks them by P/E ratio and…
This algorithm example demonstrates a two-stage method for narrowing a stock universe. Its coarse filter ranks available stocks by daily dollar volume and passes the five highest-volume names onward. A fine filter then ranks those candidates by…
The document defines price-to-book as closing price divided by book value per share, with book value per share calculated from total assets less total liabilities, divided by common shares outstanding. It implements this measure as a pipeline factor using…
This example builds a daily stock universe from currently tradable Shanghai and Shenzhen listings, then intersects it with the CSI 300 constituents. It ranks the remaining stocks by market value and keeps the five smallest. The selection is a small…
This document describes an AI-assisted workflow that turns natural-language crypto factor ideas into computed signals and validation reports. A language model interprets the idea, generates a factor function, and assigns signal direction; the system then…
This US equity strategy builds an equal-weighted portfolio through successive screens. It starts with liquid NYSE stocks while excluding financial companies, ADRs, and REITs; selects firms with low enterprise-value-to-EBIT ratios; then favors stronger…
This US equity strategy builds a machine learning feature set from fundamentals, quality measures, price and volume behavior, technical indicators, securities data, and market signals. Its target is the following week's return. Fundamental inputs are…
This document describes a quarterly, long-only US equity strategy modeled on a value ETF. It starts with NYSE stocks, removes financial firms, ADRs, and REITs, then screens for liquidity using average dollar volume over a 90-day window. Among eligible…