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 Choppiness Index is presented as an oscillator for classifying market conditions as trending, flat, or undergoing unstable consolidation. Its histogram uses colors to distinguish established trends, trend transitions, flat conditions, and unstable…
This study summary describes a framework for assessing the business climate of China’s telecommunications sector by combining supply and demand conditions, listed-company fundamentals, and macroeconomic conditions. Supply and demand measures capture…
The document summarizes a research study that applies hidden Markov models to identify regimes for individual assets and adjust portfolio exposure in response to price trends. It describes a global allocation analysis spanning January 2004 through December…
The document introduces EMA deviation as an alternative to standard deviation for measuring volatility. Standard deviation is described as a measure of dispersion, commonly used in technical analysis to quantify current volatility. The usual calculation uses…
The document outlines a stock screen combining three conditions: convergence among five moving averages, a high ranking on large-order net volume, and a positive but bounded 10-day return. The moving averages span short to longer lookbacks and are treated as…
The document describes a reproducible process for building a U.S. equity symbol master from Nasdaq Trader’s listed-symbol directory. It parses the pipe-delimited file, checks for expected fields, removes rows marked as test issues, and saves the cleaned…
This document describes SSIFT, an indicator that combines a stochastic calculation with an inverse Fisher transform and Rainbow Smoothing. It identifies three configurable inputs: the calculation period, the slowing setting, and the price series used in the…
This example applies a linear support vector machine to classify each new bar’s next close-to-close movement as upward, downward, or relatively flat. It builds training examples from historical bar open and close prices, labels each example according to…
The document proposes using the slope of closing prices over a recent window of five to ten trading days to rank stocks. Its example strategy selects the five stocks with the highest slopes, making the slope a short-term price-momentum signal. The note gives…
The i-DRProjections indicator forecasts a trading day’s price range using data from the preceding day. Its calculations are described as based on Thomas R. DeMark’s book New Science of Technical Analysis. The accompanying usage guidance says the projection…
The document explains the Deviation-Scaled Moving Average (DSMA), an adaptive moving average attributed to John Ehlers. Its core idea is to adjust an exponential moving average’s smoothing factor using the absolute size of a price-derived oscillator after…
This document explains a volatility indicator based on a GARCH(1,1) recursion. The model estimates conditional variance from a constant term, the latest shock, and persistence in earlier variance. Its displayed forecast line rises after large return changes…
The document describes a tool for assessing portfolios of expert advisors (EAs) across three dimensions: correlation between daily profit-and-loss series, the hours and weekdays when strategies trade, and diversity across asset classes. It combines dimension…
This stock-selection proposal combines a market capitalization ceiling, positive profitability, a minimum amplitude condition, and a concentration measure described as being below a threshold. Its discussion frames the screen as a mix of volatility, company…
The document introduces TA-Lib as a technical analysis library and surveys indicator groups used to transform price and volume data. It covers moving averages and bands, momentum oscillators, volume measures, cycle tools, price transformations, volatility…
The document briefly introduces a Dynamic Zone version of the relative strength index with substantial smoothing. It places RSI in the familiar context of an oscillator bounded between low and high values, which are described as reflecting market sentiment…
Color RMACD is a MACD-style histogram that uses the RVI indicator for its fast component and a custom True RVI indicator for its slow component. Its histogram and signal line change color according to the active market trend. The signal line can use one of…
This document describes a configurable Commodity Channel Index (CCI) indicator. Its settings cover the CCI calculation period and applied price, optional smoothing of input prices before calculating CCI, optional smoothing of the resulting indicator, and…
The normalized moving average slope oscillator expresses the change in a moving average relative to average true range. Its formula takes the difference between the current and previous moving average, divides by ATR, and multiplies by 100. This scales the…
This article explains how to simplify factor research by converting many distinct raw values into a smaller number of bins. Its example is a daily return factor across thousands of stocks, where nearby values may be treated as equivalent for exploratory…
The Kairi Relative Index is presented as an oscillator that measures how far the closing price has moved from its moving average, expressed as a percentage of that average. The document specifies a 14-period lookback and includes a zero reference level.…
This research summary reviews the 2021 commodity futures market and examines whether exchange increases in trading fees affect commodity prices and CTA strategies. It describes a year of broad price gains followed by sharp reversals amid market…
This article explains how seemingly routine backtesting choices can make a strategy look more reliable than it is. It focuses on three assumptions: orders fill immediately at the modeled price, market data and trading opportunities form a continuous…
This introductory tutorial explains machine learning concepts before focusing on linear regression. It defines features and labels, distinguishes supervised from unsupervised learning, and outlines classification, regression, clustering, and parameterized…