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.
3,481 documents
This example outlines a China A-share stock selection and portfolio backtest workflow. It screens for eligible, non-suspended stocks, calculates 30-day and 90-day returns, and ranks those returns cross-sectionally. The illustrated filter favors stocks in the…
The study groups fifty Wind order-flow measures into eight categories, including inflows, outflows, net active buying, and large-order flows. It evaluates these signals as stock-selection factors using portfolio sorts, cross-sectional regressions, and…
This brief Q&A explains how to change the scoring function used during hyperparameter search. The question asks how to include annualized return, win rate, and maximum drawdown alongside a Sharpe-based score. The response says the score function can read…
This speech transcript outlines a research framework spanning short, intermediate, and long horizons. It associates short-term work with factor models and machine-learning stock selection, intermediate work with industry and style rotation, and long-horizon…
This note describes a daily stock factor that combines weighted order book pressure with the closing price’s deviation from intraday VWAP. It weights the five visible book levels by proximity, giving the nearest level the greatest weight, then uses trading…
This article explains how to measure whether active mutual fund managers adjust holdings in stocks with different downside market sensitivity ahead of market moves. It defines relative downside beta as the portfolio weighted downside beta minus conventional…
The document explains net income compound annual growth rate (CAGR) as an annualized measure of change between an initial and a later profit value. It distinguishes CAGR, which represents compounding across multiple periods, from a simple endpoint growth…
This installment of a quantitative investing research review introduces two overseas studies relevant to stock selection. The first examines how interest rates affect stock pricing and is described as offering a new analytical framework and empirical testing…
This support exchange diagnoses why a futures Bollinger Band strategy produced no backtest results. The reported cause is a mismatch between the date ranges: the data extraction module supplied data from 2021, while the backtest was set to run in 2024. Since…
This tutorial explains how to build and install a reusable BigQuant module that reads a named data table, selects a date range, and rounds chosen floating-point fields to a requested number of decimal places. It describes defining required and optional…
This platform support note addresses why an order query may return no information in a pre-market handler even though it can return information after the market session. The question concerns using existing orders to build a process that cancels orders at…
This research note presents investor mood as a possible explanation for seasonal patterns in both market returns and the relative performance of stocks. It hypothesizes that assets that perform well in historically upbeat periods will tend to do so again in…
The document explains turnover as the number of shares traded during a period divided by the average number of shares available to trade, expressed as a percentage. It illustrates the calculation with a quarterly example in which traded volume is twice the…
The document describes a China A-share index enhancement approach that adds industry rotation to a CSI 300 stock-selection portfolio. Its motivation is that the benchmark has uneven industry weights, making active industry exposure a meaningful part of…
This research summary extends a framework for selecting functional equity funds for fund-of-funds portfolios. It distinguishes sectors where active stock selection may add value from those better served by sector ETFs. For consumer and growth exposures, it…
The article contrasts quantitative trading, which it describes as using statistical models and systematic execution to exploit price deviations linked to investor emotion, with discretionary investing, where patience, independent judgment, and resilience may…
This 2018 report revisits whether the investment clock can guide asset allocation amid trade tensions and political shocks. Its traditional mapping assigns equities to recovery, commodities to overheating, bonds to recession, and cash to stagflation. It…
This Chinese equity strategy selects stocks whose opening price is above the previous close and whose five-day closing-price average is above the ten-day average. It ranks qualifying stocks by price-to-earnings ratio in ascending order, selects up to ten,…
This strategy combines model-based stock ranking with trend-following entry filters to seek short- and medium-term gains in Chinese equities. It starts from a manually selected pool of China Securities 150 constituents, narrowed to roughly 100–300 large,…
The script describes a spread-trading approach linking methanol futures with polyethylene and polypropylene futures. It estimates an MTO production margin by valuing the two polymer contracts together and subtracting the methanol input cost, adjusted for…
The document raises a practical trading question: how to enforce a minimum holding period using trading days rather than calendar days. Its example calculates elapsed time by subtracting the last rebalance date from the current date, which counts weekends…
This short support discussion concerns a simulated trading run that reportedly failed or stopped before reaching its intended endpoint, even though the available logs showed no clear error. Replies suggest two different observations: one participant saw a…
This article explains how the Fama-French model extends CAPM first with size and value factors, then with profitability and investment factors. It describes double sorts by size and book-to-market, return on equity, or asset growth, and forms RMW from…
The document explains how combinatorially symmetric cross-validation (CSCV) can estimate the probability of backtest overfitting (PBO). It distinguishes training overfitting, where a model performs poorly on held-out data, from backtest overfitting, where a…