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 meetup note surveys several topics in equity quant work. It outlines reversal-factor ideas based on recent returns, technical indicators such as RSI and Bollinger Bands, and fundamental changes such as valuation or a return to profitability. It suggests…
This assignment outlines a style-rotation process built from three stock strategies: small-capitalization, below-book-value, and oversold-rebound approaches. After backtesting each strategy, it proposes saving the return and position series, then comparing…
This article proposes four warning signs to watch for during an equity boom: historically extreme valuation multiples, a high ratio of total stock-market capitalization to GDP, broad insider selling, and speculative surges in weak or distressed stocks. It…
This report examines how portfolio optimization settings affect a multi-factor equity strategy. It considers the interaction of active-weight limits with the lambda coefficient, the share of non-index constituents, industry and size exposures, turnover…
This Chinese-language research summary examines analyst consensus estimates as a source of information beyond company financial statements and trading data. It cautions that analyst forecasts tend to be optimistic and that A-share consensus estimates often…
The author describes a stock-selection approach intended for people who cannot monitor prices throughout the trading day. Signals are checked at 2:50 p.m.; the proposed routine is to buy a leading candidate scoring above 6 before the close, and to skip…
This short platform discussion distinguishes a backtest’s pre-market processing from live or simulated trading. In a backtest, the pre-market step may access the current day’s opening price, allowing a rule to use that price to decide on afternoon trading…
This note outlines a basic toolkit for constructing low-frequency factors from price and trading data. For each selected input, it proposes calculating rolling means, sums, standard deviations, and skewness over several lookback windows, ranging from short…
The discussion addresses a user who sees a cloud task appear stopped after reopening a browser and asks for more detailed logs. The platform response says closing the browser does not stop the program running in the cloud, but output logs cannot be…
The document raises a question about reproducing a 60-day moving-average comparison in a Chinese quantitative research platform. The user’s feature flags observations when a close-price series is below a 60-day mean calculated from prices divided by an…
This note presents a simple crossover interpretation of Kaufman’s Adaptive Moving Average. It describes a possible entry when price has been below the indicator and a bullish candle moves above it. A downward cross, especially when the indicator changes…
This brief BigQuant discussion answers how to retrieve the MACD histogram value for the day on which the highest close occurred within a prior 30-day window. The suggested workflow is to use a time-series argmax function to locate the offset of the maximum…
The document explains MACD as a trend and momentum indicator made up of a MACD line, a signal line, and a histogram. It describes bullish signals when the MACD line crosses above the signal line or rises while below zero, and bearish signals when it crosses…
This research summary describes adapting daily price-volume alpha research to intraday high-frequency data for stock selection. It examines relationships between price and volume, including ordinary and unusual activity, and reports constructing three…
This brief Chinese-language forum post reports a data-quality issue discovered while debugging backtests: some runs returned no results, and inspection showed that certain factors were empty in many cases. The author notes that noticing the missing values…
This technical note introduces the Money Flow Index (MFI) and outlines its calculation from high, low, close, and volume data over a chosen lookback period. It first computes typical price as the average of the day's high, low, and close. Volume-weighted…
The post reports a BigQuant backtest failure with a KeyError for the start_date field. The example pipeline screens Chinese stocks, uses recent fund holdings and moving-average conditions to generate signals, ranks selected stocks by market capitalization,…
This report excerpt reviews Chinese equity market performance and factor behavior for the week summarized in an early 2020 research note. It says major indices and most sectors fell during the week, while quality factor effectiveness improved and analyst…
This essay argues that experienced investors may communicate in a direct, plainspoken way because they have worked through complexity and learned to focus on what they understand. It frames simplicity as the result of discernment rather than lack of…
This article explains how a rolling average smooths a time series and why longer lookback windows produce smoother but slower signals. It compares simple, exponential, linearly weighted, triangular, and volatility-adjusted moving averages, focusing on how…
This document summarizes lifecycle funds, which adjust their asset allocation as a target date approaches. The described approach gradually reduces portfolio risk over time, with the aim of matching investment risk to the holder’s changing capacity for risk…
The note addresses a stock forecasting workflow that displayed an unexpected prediction date and failed when publishing a simulated trade. Its proposed fix is to make sure the training period and prediction period are not set to the same dates. This is a…
This document introduces a pivot indicator that uses high and low prices together with left- and right-strength parameters. It describes locating local lows and highs within a rolling window whose size depends on those settings, then comparing how recently…
This short exchange explains how to interpret a long-short portfolio return in factor analysis and how to select a chosen group from a factor-ranked universe. It describes the long-short spread as the return from buying the lowest-ranked group and selling…