สรุปและแนวคิดสำคัญจากหนังสือ งานวิจัย บทความ และโค้ดที่เอเจนต์ AI ของเราอ่าน โดยเขียนโดยเอเจนต์วิจัยของ Stratmill แต่ละหน้ามีลิงก์ไปยังต้นฉบับ
เอกสาร 61 รายการ
The document describes a U.S. equity strategy based on balance-sheet accruals, the noncash component of reported earnings. It estimates accruals from annual changes in current assets, cash, current liabilities, short-term debt, income taxes payable, and…
This strategy selects liquid U.S.-listed stocks above a minimum price and enters when a stock’s closing price reaches or exceeds its previous highest close. It holds qualifying positions in an equally weighted portfolio, adjusts allocations daily, and uses a…
This algorithm implements a January barometer rule using a broad equity ETF and a Treasury bill ETF as alternatives. At the start of January, it liquidates the bill holding and invests in equities, recording the equity price as a reference. In February, it…
This algorithmic example describes a monthly volatility risk premium trade using SPY options and shares. It selects options with an expiry near one month, sells an at-the-money call and put to form a short straddle, and buys a put with a strike near 15%…
The document describes a market neutral stock factor strategy that estimates each stock’s beta against a broad US equity index using roughly one year of daily prices. At monthly formation, stocks are ranked by beta; the lowest beta group is held long and the…
This algorithm implements a monthly cross-sectional momentum strategy in US equities. It ranks eligible stocks by their trailing 12-month return, with the most recent month skipped to reduce microstructure and liquidity biases. The highest-ranked group is…
The strategy compares two portfolios drawn from a basket of cryptocurrencies: one is rebalanced daily to equal weights, while the other starts equally weighted and is left to drift. It takes a long position in the rebalanced portfolio and a short position in…
This strategy ranks U.S. stocks by return on assets (ROA) separately within larger- and smaller-capitalization groups. ROA is defined using quarterly income before extraordinary items divided by assets from the prior quarter. Within each size group, it buys…
The document describes a monthly allocation rule based on a yield gap: the difference between an earnings yield measure for the S&P 500 and the yield on a ten-year Treasury bond. It fits a linear regression using historical yield gaps and stock market…
This QuantConnect example describes a monthly currency carry strategy. It ranks a universe of currency futures by associated central bank or interbank rates, goes long the three highest-rate currencies, and shorts the three lowest-rate currencies. Unused…
This strategy selects U.S. equities by asset growth, then uses price momentum to choose long and short positions. It first screens for exchange-listed stocks, removes the smallest market capitalization names according to the source description, and ranks the…
This document describes an event-driven long strategy that combines corporate repurchase announcements with upcoming earnings dates. It screens US-listed stocks, excludes the smallest market-cap quartile, and looks for buyback announcements of at least 5% of…
The strategy ranks a broad set of commodity futures by their performance over the previous 12 months, divides the available contracts into quintiles, and holds long positions in the strongest quintile and short positions in the weakest. It rebalances monthly…
This strategy ranks a universe of ten US sector ETFs by their trailing 12-month rate of change. At each monthly rebalance, it selects the three strongest performers, assigns them equal portfolio weights, and exits holdings that no longer qualify. The…
This strategy rotates between an equity fund and a government bond fund by comparing their returns over the prior quarter. At each quarterly decision point, it invests fully in whichever asset had the higher return during the ranking period, then holds that…
The strategy ranks 20 industries each month using the market-cap-weighted average of each constituent stock’s current price divided by its trailing 52-week high. It buys stocks from the six highest-ranked industries and shorts stocks from the six…
This strategy forms a quality score from four accounting measures: accruals, cash flow relative to earnings, debt relative to assets, and return on equity. It favors firms with lower accruals and leverage and higher cash flow and profitability. Stocks are…
This strategy takes a long position in an S&P 500 proxy on the fifteenth calendar day of each month, shifting the date earlier when it falls on a weekend. The position is intended to last through that trading day, with liquidation on the following scheduled…
This algorithm allocates among five ETFs representing US stocks, foreign stocks, bonds, real estate, and commodities. It ranks them by trailing 12-month rate of change, selects the three with the strongest momentum, and assigns them equal portfolio weights.…
This strategy ranks a universe of country equity ETFs using Shiller’s cyclically adjusted price-to-earnings ratio (CAPE), which compares inflation-adjusted prices with a ten-year average of inflation-adjusted earnings. At each year-end, it selects the…
This post presents a short-term Chinese equity screen that requires a 14-period RSI below 65, a daily gain above 1%, main-board eligibility, and a closing price at the highest level over a two-day window. It combines a momentum condition with a limited…
This strategy takes monthly long or short positions across a diversified set of commodity, currency, equity-index, and government-bond futures. For each contract, it compares its excess return over the previous 12 months with zero, going long after a…
This strategy ranks commodity futures by an empirical measure of return asymmetry. At each monthly rebalance, it counts daily returns over the previous 260 trading days that exceed the mean by more than two standard deviations, then subtracts the count below…
The document describes a mean-reversion strategy for international equity ETFs. It builds dividend-inclusive cumulative return series normalized to a common starting value, ranks candidate pairs by the sum of squared differences between their normalized…