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 document describes KRev, an indicator intended to estimate the currency risk per unit of a position if price reaches the third Kase Dev Stop. Traders can use that estimate to compare markets or time frames with a fixed per-unit risk limit. The…
This short introduction proposes hidden Markov models (HMMs) as a machine-learning technique for market analysis and timing. It says the article will explain the model, discuss similarities between HMMs and stock markets, and develop a multi-factor…
This document outlines a two-stage workflow for calculating Alpha101 factors. First, it reads daily stock data, derives base series such as returns and VWAP, and computes time-series intermediate variables for storage. Later, factor construction retrieves…
The article describes three ways strategy research can produce misleading backtests: look-ahead bias, excessive parameter optimization, and curve fitting. Its examples show how using a bar’s eventual close to trigger an earlier trade, or assuming a breakout…
This article describes a timing rule that uses changes in the gap between short- and long-term moving averages, instead of relying only on their crossover. It defines the gap as the short average minus the long average, then interprets the gap’s expansion or…
The script described in the document scans bars on a one-hour chart to calculate average volume per hour. It is restricted to that chart timeframe and uses the absolute value of volume in its calculation. The result is displayed in a message box, giving the…
This indicator aims to show whether a trend is present and how strong it is by comparing a moving-average channel with a longer price range. For each selected moving-average period, it measures the recent high-to-low span of the average and compares that…
ATRR is described as an indicator that compares two Average True Range values calculated over different lookback periods. Its two inputs set the periods for the first and second ATR calculations. The intended use is to view the relative behavior of…
The Even Better Sinewave is described as a bounded oscillator for identifying bullish or bearish cycle momentum while filtering price noise. Its construction applies a high-pass stage to price changes, then smooths the result with a low-pass filter. A…
This article argues that trading volume cannot be interpreted through a fixed rule that rising prices must come with rising volume. It recommends judging volume relative to the prior price and volume trend, market setting, and position within a move, with…
This stock selection approach filters for shares with a daily price range above a threshold, at least a year since listing, and a high ranking in large-order net volume. The article frames the range condition as a way to find active stocks, listing age as a…
This Chinese stock screen combines a daily price-amplitude condition, afternoon large-order net inflow, and year-over-year growth in net profit attributable to the parent company. It specifies profit growth above 20% and no greater than 100%. The article…
This opinion piece argues that quantitative trading’s advantages extend beyond execution speed. It emphasizes systematic research and iteration: models can combine many fundamental and price-based signals, test relationships across historical data, and…
This overview compares machine learning and deep learning methods for predicting stock prices and trends. It describes LSTM and GRU recurrent networks for sequential data, CNNs for extracting patterns, bidirectional LSTMs, and deep neural networks. It also…
This ProRealTime indicator estimates average forward price movement conditional on the current reading of RSI, Stochastic, Williams %R, or ADX. It divides indicator readings into 20 five-point ranges, then separates observations according to whether the…
This indicator constructs price bands around an exponentially weighted moving average. It first takes the logarithm of the input price and updates the average with an exponential weighting factor derived from the chosen period. It then calculates an…
This document explains a Smart Money Concepts indicator that labels changes and breaks in market structure as CHoCH, SMS, or BMS. It tracks outcomes in loaded price history and displays estimated probabilities for the next continuation or reversal, alongside…
This Chinese-language post proposes a stock screen combining three conditions: daily price range above 1%, a positive ten-day return below 35%, and a measure labeled today’s position increase above 5%. It interprets the range as a sign of active price…
This literature review explains time-series and cross-sectional momentum strategies applied to equity anomaly factors. The time-series approach buys factors with positive trailing returns and shorts those with negative returns; the cross-sectional approach…
This literature review explains time-series and cross-sectional momentum strategies applied to equity anomaly factors. The time-series approach buys factors with positive trailing returns and shorts those with negative returns; the cross-sectional approach…
This research summary examines whether extreme investor sentiment can help identify short-term industry allocation opportunities. It argues that industry valuation comparisons are difficult because sectors differ in profitability and asset intensity,…
This post describes a stock selection screen that combines RSI below 65, seven consecutive sessions with the close below the open, and declining fund-flow strength. It frames the conditions as a mix of technical signals and investor activity, then offers…
The article introduces mutually exclusive events as outcomes that cannot happen at the same time, then applies the idea to investment choices constrained by a limited budget. It contrasts positions that cannot be held together under its example budget with…
This report studies whether one industry’s past returns can help predict another industry’s future returns. It argues that information may spread gradually across related industries because investors cannot immediately assess every impact of a new shock. The…