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.
4,194 documents
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…
The article compares two ways of selecting cryptocurrencies for a weekly basket: the 20 lowest unit prices and the 10 lowest market values. For the market-value calculation, it estimates value using total supply and price, and limits the analysis to coins…
This article outlines an automated workflow for turning a written trading hypothesis into a preliminary factor test. A user submits an idea through messaging; an AI model translates it into JavaScript, the workflow retrieves cryptocurrency data, checks data…
The document explains a basket strategy that ranks assets by an expected-return signal, buys the highest-ranked group, and shorts the lowest-ranked group with equal dollar exposure. The intended market neutrality reduces sensitivity to broad market moves,…
The document introduces WorldQuant Alpha101 as a collection of formulaic signals intended to inspire quantitative strategy research. It groups examples into price-based and volume-price factors, with descriptions that associate some formulas with trends,…
APFF is a framework for ranking a changing universe of crypto perpetual futures and combining interpretable signals into a long-short portfolio. Its seed factors cover momentum, short-term reversal, funding, premium, and open-interest behavior.…
This beginner-oriented article outlines a cross-sectional factor research workflow using hourly Binance USDT perpetual futures data. It describes collecting price, volume, trade-count, and taker-buy information across many contracts, then forming candidate…
This tutorial presents a beginner framework for testing factors across a universe of USDT perpetual futures. It starts with hourly price, volume, trade count, and aggressive-buy data, then describes factor evaluation through forward-return regressions,…
The document explains a market-neutral portfolio approach that ranks a universe of related assets, buys equal-sized positions in the highest-ranked group, and shorts the lowest-ranked group. Its returns depend on whether the ranking factor separates future…
The document describes a workflow that turns a natural-language trading hypothesis into code, retrieves cryptocurrency data, calculates factor diagnostics, and returns an interpreted report. Its stated checks include information coefficient, monotonicity,…
The article describes a prototype that converts crypto traders’ stated approaches into a layered signal process. It first represents market conditions with price trend and momentum indicators, volatility, recent highs and lows, and macro variables such as…
The article presents an automated workflow for discovering and refreshing cross-sectional factors for crypto perpetual contracts. It selects liquid markets, classifies volatility conditions, prompts an AI system to propose factors, and evaluates candidates…
The article develops the Psychological Line (PSY), an indicator that counts rising bars over a lookback, into a measure intended to better represent the balance of buying and selling pressure. It notes that a simple count treats a small rise like a large…
The article compares two token-selection ideas: buying low-priced coins and rotating into coins with low market capitalization. It describes a weekly ranking approach, selecting the twenty lowest-priced assets for one index and the ten lowest-cap assets for…
This document outlines an automated research and execution workflow for mining factors across liquid crypto perpetual contracts. At scheduled intervals, it selects markets by trading volume, classifies volatility conditions, reviews the health and coverage…
This interview with trader Priyanka S. includes practical advice for developing and testing equity signals. She cautions that familiar technical indicators such as moving average crossovers may contain little information about future prices, and encourages…
This profile traces a trader’s move from business analytics to US equities trading and then quantitative investing in digital assets. A practical motivation for automation was the difficulty of manually monitoring many potential tickers at once. He describes…
The article presents value investing as buying shares below an estimate of their intrinsic worth, with the gap between estimated value and purchase price serving as a margin of safety. It describes selling when price approaches or exceeds estimated value and…
The article combines company fundamentals with quantitative ranking to select equities. It describes comparing a group of large technology stocks using valuation and leverage ratios, inverting the ratios to represent value, standardizing them with Z-scores,…
This project backtests equal-weight stock portfolios selected using alpha, prior-year returns, volatility, beta, and return divided by volatility. It varies portfolio size, stock universe, and annual, semiannual, or quarterly rebalancing, while describing…
The article explains how company fundamentals can be turned into machine-readable rules for systematic equity selection. It outlines the need for structured, reliable financial data, names several data providers, and discusses risks in reported figures,…
This article explains how academic and industry research can generate ideas for systematic trading. It describes a screening approach that considers whether a strategy can be implemented, the length of its backtest, and the overall soundness of its evidence…
The article outlines the development of the Fama-French five-factor asset-pricing model from CAPM and the three-factor model. Alongside market, size, and value effects, the five-factor version adds profitability and investment. The article frames these…