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.
21,023 documents
This tutorial demonstrates extracting minute-level stock data for a selected date range with a distributed query platform, then calculating features through SQL-style operators. The examples cover a five-period moving average of closing prices, a…
This research summary examines why stocks can earn excess returns before earnings announcements. It proposes that investors and analysts resolve some uncertainty ahead of the report, supporting price gains before the announcement itself. The study measures…
This document describes transforming the X2MA moving average into an oscillator by normalizing its values to a scale from zero to one hundred. It then shifts the resulting indicator downward by fifty units for visual presentation. The text names both the…
XMA_TrendSignal classifies market movement into flat, moderate-trend, and strong-trend states, with separate positive and negative readings to represent direction. Its bars use colors to show both direction and strength. Trend force is measured by the rate…
The document reports a single-factor study of operating-efficiency and growth measures. For six operating factors, it says the analysis used grouped portfolio net values, long-short group returns, information coefficients, and Fama–MacBeth methods. Four…
This beginner-oriented guide distinguishes algorithmic trading from quantitative, automated, and high-frequency trading. It describes algorithmic trading as expressing a trading idea in an algorithm that can be tested and then executed manually or…
The study summary examines whether active stock selection that increases a mutual fund portfolio’s technological similarity predicts subsequent performance. Company patent distributions are compared using cosine similarity, and a fund’s active technological…
This Chinese-language forum post raises practical questions about what factor weights from a labeling or training process represent. It distinguishes possible interpretations, including neural-network parameters, regression coefficients, and tree-based…
This overview introduces autoregressive and moving-average models for representing serial dependence in financial time series. An ARMA model combines lagged observations with lagged errors and assumes stationarity; the article notes that differencing or…
This summary of an equity research report explains portfolio optimization as a framework for managing risk exposures, holdings, weights, turnover, and market impact while incorporating investor views. It argues that simple portfolio construction may perform…
This brief report summarizes a quantitative market-timing view of Chinese A-shares as of early April 2020. It describes the market as being in a bottoming phase with limited downside, and lists sector strength, index and industry valuations, stock-market…
This research tool estimates the bid and ask as separate time series, fitting local quadratic curves to each side of the quote. It reports position, velocity, acceleration, standard errors, z-scores, and an estimated turning time across three event-count…
This Python module prepares portfolio optimization inputs for a greedy allocation routine. It takes target and prior weights, covariance estimates, instrument values, trading costs, and optional constraints, then aligns the data to instruments with valid…
TSICloud is described as a cloud-style oscillator built from the correlation of two smoothed momentum measures calculated over different timeframes. Its presentation gives a high-level account of the indicator’s construction and identifies it as an MQL5…
The article describes a supervised learning approach that classifies whether a stock’s closing price will be higher or lower after a chosen horizon. It smooths historical prices with exponential weighting, then uses six technical indicators—including RSI,…
This tutorial explains XGBoost through the broader framework of supervised learning and gradient-boosted decision trees. It introduces loss functions for regression and classification, then describes an ensemble as the sum of tree outputs. Training adds one…
This document summarizes a study of whether slow updating of beliefs about future earnings can account for the profitability anomaly: the tendency for more profitable stocks to outperform. In its model, expectations combine prior beliefs with rational…
This research summary presents a model in which investors update beliefs about future profits too slowly. When earnings signals are persistent, that sluggish updating can leave information out of stock prices, helping explain why more profitable firms and…
A forum question presents Python code that tests combinations of rolling volume windows and percentile thresholds on a Chinese stock index series. The script creates a signal when average volume over a chosen window exceeds a five-year rolling percentile,…
The Range Coefficient is a histogram indicator that compares a short-term average of candle ranges with a longer-term average. It computes the ratio of the recent average range to the longer average range, then subtracts one. Positive readings mean recent…
This module describes a method for selecting upper and lower trading thresholds for a mean-reverting cointegration pair. It estimates a hedge ratio using either Engle–Granger or Johansen analysis, constructs the cointegration error as the spread, and fits an…
The document explains Williams’ Variable Accumulation Distribution (WVAD), an indicator that combines a bar’s price movement with its volume. It describes the close-to-open change as a directional signal and scales that change by the high-to-low range, then…
This stock screen combines a turnover rate between 3% and 12%, year-over-year growth in net profit attributable to parent-company shareholders above 20% and no more than 100%, and a price pattern described as a rising bottom. The accompanying indicator logic…
This indicator adds a three-level trend-strength display to a Kalman-filter moving average. It calculates the filter’s standard deviation and compares it with two adjustable thresholds. When the value falls between the thresholds, a small dot appears in the…