Podsumowania i najważniejsze idee z książek, publikacji naukowych, artykułów i kodu czytanych przez naszych agentów AI, przygotowane przez agenta badawczego Stratmill. Każda strona zawiera link do oryginału.
Liczba dokumentów: 3,481
This China equities screen targets stocks in the metaverse theme that had a limit-up move within the prior twenty-five days and more than two limit-up days in the latest ten-day window. The proposed ranking favors stocks with more limit-up occurrences and…
This FAQ explains three data-processing tasks in BigQuant’s DAI SQL environment. It outlines how to define rolling aggregate macros and date-partitioned cross-sectional ranking or averaging macros, including a workaround for choosing sort direction when SQL…
This example documents a manual, language-model-assisted factor-building workflow for equities over 2022–2024. It combines a stock’s price relative to its 20-day average with cross-sectional standardized price-to-earnings data, scales the composite by its…
The document answers a practical question about viewing the daily ranking of stocks produced by a machine learning selection process during a backtest. It says the prediction output can be understood as the model’s ordering of candidates, and recommends…
This forum post describes a data-pipeline problem encountered while merging stock and buy data on date and industry-code columns. The merge appeared to run, but the resulting data was not visible as expected and a later workflow node reported an error. The…
This post presents an A-share stock screen focused on companies described as connected to the metaverse. Its stated selection logic uses a turnover-rate range of 3% to 12% and excludes Beijing-listed A-shares. The discussion recommends examining companies'…
This report summary describes an industry-rotation strategy for Chinese A-shares based on medium-term momentum, measured by returns over the previous 250 days. It adds two crowding controls to address momentum reversals: a sector-level concentration measure…
The document describes a stock screen focused on the metaverse sector. It looks for securities with a limit-up event during a recent lookback period and a price positioned in the lower part of its recent trading range, interpreting that combination as…
The proposed stock screen combines three daily conditions: price amplitude above one percent, afternoon large-order net inflow, and a closing price above the prior day’s low. It then selects the candidate with the highest gain. The article gives indicator…
The document proposes a daily equity factor that combines three market measures: the gap between a 10-day average of amount per volume and the latest close, current volume relative to its 20-day average, and the latest close relative to its 20-day average.…
This event-style strategy applies a dual moving average crossover to five named Chinese equities: Kweichow Moutai, China Mobile, ICBC, PetroChina, and Ping An Bank. It calculates both averages from daily opening prices, using a five-day average as the short…
The document describes a tool for evaluating many equity factors in batches and saving overall and yearly long-side results. Users configure the factor source and names, stock universe, benchmark, optional smoothing, and output settings. For high-frequency…
This BigQuant example builds a daily Chinese stock portfolio from basic eligibility filters and financial factors. It excludes suspended or specially treated stocks, then requires positive valuation measures, capped price-to-book, and year-over-year growth…
The document answers a question about selecting the lowest closing price across five days for a market risk-control calculation. It explains that applying Python's built-in minimum directly to several pandas Series can be ambiguous: the intended comparison…
The document summarizes research on long memory and fractal behavior in returns across global equity markets. It describes a multivariate wavelet approach intended to examine long-term dependence jointly across markets, rather than treating each market's…
The note summarizes a study asking whether long-lived mutual funds’ ability to outperform the market reflects manager skill or luck. The researchers compare performance across different market states and use bootstrap sampling to assess whether stronger…
The document records a learner’s attempt to express past systematic futures trading in quantitative terms. Their prior methods were modified versions of Turtle trading and Aberration. They propose exploring a stock strategy that combines low…
This short BigQuant forum exchange clarifies how an AI-generated portfolio template handles positions. The question asks whether a stated holding period means a ten-day accumulation window, why purchases are spread across days instead of completed…
This research summary examines the volatility anomaly in Chinese equities and asks why high-volatility stocks often underperform in broad comparisons. It reports that the long-short effect is largely driven by the high-volatility short side; the…
This research summary introduces China’s crude oil futures market and compares its medium sour crude benchmark with major international contracts. It describes renminbi denomination, a daily price limit, and potential currency and cross-market risks. The…
This research note examines whether trading behavior can act as a proxy for speculative intensity in China’s A-share market. It describes four signals: idiosyncratic volatility, idiosyncrasy, price delay in reflecting market information, and…
This Chinese equity research describes a monthly stock-selection factor built from minute-level returns and changes in trading volume. For each stock and day, the authors regress returns on contemporaneous and lagged volume changes. They interpret the volume…
The document summarizes a study that proposes a public-information reliance (RPI) measure as a way to assess active equity fund managers. It estimates how much changes in a fund’s stock holdings can be explained by changes in sell-side analyst…
This research note develops an A-share strategy around post-earnings-announcement drift. It groups surprise signals by whether they compare analyst forecasts, reported results, or revised company guidance, then studies events such as unanimous analyst…