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
The article presents market feel as a learned pattern-recognition skill, built by repeatedly observing price structure, volume changes, and trading behavior. It recommends focusing on three intraday periods, watching whether moves attract volume and support,…
This beginner post presents a small script for counting runs of consecutive rising or falling market observations from candlestick data. Its example compares each candle’s high with neighboring highs, resets the run counter when direction changes or prices…
This document describes preprocessing checks for event data that records both exchange timestamps and local receipt timestamps. One routine detects when the local clock appears ahead of the exchange clock, then shifts local timestamps by the largest observed…
This brief summary of a multi-factor stock-selection report describes three ways to refine traditional factors using high-frequency returns. High-frequency return variance is presented as a closer measure of volatility than variance calculated from daily…
The indicator reports statistical characteristics of price bars: mean, variance, skewness, and kurtosis. These measures summarize a bar series through its average level, dispersion, asymmetry, and tail behavior. The page notes that several instances can be…
This short example shows how an automated trading script can detect that both long and short positions have become flat, then respond when its internal state still indicates an active trade. It compares the current account balance with the previously saved…
This tutorial presents a workflow for evaluating a factor on Chinese equities, using the lower Bollinger Band as an example. It introduces factor analysis as a way to summarize variables and derive composite scores, then describes building daily factor data,…
This research summary tests whether splitting stock volatility into components yields useful selection signals from high-frequency returns. The reported system-versus-idiosyncratic volatility split performs poorly at the one-minute sampling interval; its…
The article argues that traders should avoid relying on elaborate market theories or stacks of technical indicators. It recommends choosing a method that suits the trader, then checking it against historical charts and defining entries, stops, scaling…
This BigQuant help response clarifies which data fields are available in its visual automatic stock-labeling module. It says the module directly supports only the standard open, high, low, and close price fields. A user asking about unavailable documentation…
This code module implements three neural-network architectures that could be applied to quantitative prediction tasks: a feed-forward multilayer perceptron, an LSTM-based recurrent network for sequential inputs, and a Pi-Sigma network that multiplies…
This research digest summarizes two studies. The first revisits the claim that small-cap stocks earn higher average returns than large-cap stocks, reviewing competing views about the size effect and describing simple tests with public data intended to…
The discussion addresses missing values from a rolling maximum over a fixed 1,000-day window when a stock has been listed for less than the full period. It proposes making the window length depend on the stock's available listing history: use the smaller of…
This research summary examines valuation factors for industry allocation and explains why raw valuation measures can be difficult to compare across sectors. It reviews five common measures, including earnings, book, sales, and operating-cash-flow based…
This platform discussion shows how to count Chinese stocks whose total market capitalization is below three billion yuan on each trading date. It queries daily stock-factor data for a selected date range, filters the rows by market capitalization, and groups…
A user reports that custom normalization code works on its own but produces no result when run as a module. The response explains that the normalization step operates on cross-sectional data: it compares observations across securities at a given point in…
This tool graphs cumulative simulated outcomes after consecutive runs of bullish or bearish candles, considering run lengths from one to twelve. For each qualifying pattern, it records a one-candle return in the direction of a hypothetical long position and…
This document introduces a library of averaging algorithms intended for intermediate calculations on price series. It points readers to a separate article for detailed descriptions of the algorithms and notes that the library has an MQL4 version. An EMA…
This document describes an indicator that displays how often the Evening Star candlestick pattern appears for a selected symbol and timeframe over a specified period. It focuses on counting and visualizing detected patterns, rather than defining a trading…
This document introduces an Ease of Movement indicator displayed as a color histogram. Ease of Movement is a technical indicator intended to represent how readily price moves in relation to trading activity. The text identifies the indicator’s presentation…
This reading note summarizes two quantitative research papers. The first applies the thermal optimal path method, a nonparametric approach motivated by statistical physics, to study how stock indices and their futures lead or lag one another over time. The…
This brief Chinese-language listing points to a lecture on model-free prediction in reinforcement learning. It identifies the topic as the relationship between prediction methods and Monte Carlo and temporal-difference algorithms, and names Hado van Hasselt…
This document explains how to model a mean-reverting portfolio with a Cox-Ingersoll-Ross (CIR) process, whose volatility scales with the square root of its value. It describes fitting the process by maximum likelihood and selecting portfolio weights to…
This stock-selection approach combines market activity with a profitability screen. It looks for shares with amplitude above 1, turnover between 2% and 9%, and return on equity above 15% for five consecutive years. The article presents ROE as a way to…