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 a support and resistance indicator based on the prior candle’s high, low, and close. For daily levels, it uses the previous day’s candle and applies the resulting levels to charts below the daily timeframe; levels are recalculated each…
This research review summarizes a study of manager changes at Chinese equity funds, distinguishing internal transfers, moves to another public fund, moves to a private fund, and exits from the fund industry. The study compares benchmark-adjusted buy-and-hold…
The document introduces John Ehlers’ Center of Gravity indicator as a finite impulse response filter applied to price, with a configurable calculation period. It presents the indicator as a low-lag tool for spotting potential turning points. The basic signal…
The article explains how to estimate the probability of a binary outcome using Bayesian inference. It uses coin flips to introduce a Bernoulli likelihood for observations and a Beta distribution as a prior for the unknown success probability. Because these…
This article introduces Bayesian reasoning as a way to estimate an unknown probability from limited observations and revise that estimate when new evidence arrives. It distinguishes a prior probability, based on information available beforehand, from a…
This brief description introduces a linear regression indicator for estimating prices from historical observations. It says the indicator fits a straight line to price points using the least-squares method, with the number of bars as an input parameter. A…
The M1MA indicator estimates an effective price for each chart bar by averaging price data from the corresponding one-minute bars. It is intended for timeframes of five minutes or higher, with intraday charts recommended. The result is presented as an…
This research review examines momentum in Chinese equities and compares several ways to measure it. It reports that simple one-year returns, with a recent period excluded, did not predict returns reliably across the full A-share market in the tested sample.…
This event description focuses on why quantitative strategy results in backtests can differ from live trading performance, a concern it identifies specifically for Chinese A-share strategies. It presents the topic as a practical challenge for traders and…
This article recommends a three-part process for improving trading discipline: record each trade’s rationale, timing, size, planned risk and reward, and emotional state; review those records regularly for rule violations and emotional decisions; then analyze…
The document introduces stationarity as a time series property in which statistical characteristics remain stable over time, then contrasts it with nonstationary series whose means can drift. It explains that a stationary series may tend to return toward its…
The article argues that trading researchers should judge machine learning papers by simplicity, reproducibility, and generality, alongside reported empirical results. A method that performs well on a benchmark may not transfer to financial prediction, and a…
This study compares ARIMA, a multilayer perceptron, an LSTM, and an ARIMA-GARCH combination for next-day NVIDIA share-price forecasts. It describes ARIMA as a model for linear time-series structure, neural networks as ways to capture nonlinear patterns, and…
This report develops a framework for rotating between the CSI 300 and ChiNext indices, whose valuations, market capitalizations, and industry exposures differ. It organizes potential drivers of their relative performance into factors covering real economic…
This introductory overview explains machine learning as using historical data to fit a model and then applying that model to new cases. A scheduling example illustrates how prior observations can inform a prediction, while noting that a practical model may…
Fundamental analysis estimates a security’s intrinsic value by examining economic conditions, industry trends, company finances, and qualitative factors such as management. Analysts use public information to form a value estimate and compare it with the…
The report estimates a fund’s equity exposure by regressing daily fund returns on daily returns for 29 first-level industry indices. It treats the sum of fitted industry coefficients as the estimated position. Because industry returns are correlated, it…
This indicator description adds a three-level trend-strength display to the i-CAi indicator. It calculates standard deviation and compares it with two user-set thresholds. When the value falls between the thresholds, a small colored dot appears on the moving…
The document describes CoRa_Wave, a moving average that assigns weights through a compound-ratio progression. The weights rise from older observations to newer ones with a consistent step ratio, unlike the linear sequence used by a conventional weighted…
The document describes a Chinese equity screen for companies in the metaverse theme, with a minimum circulating market capitalization and positive price-to-earnings ratio. It presents the positive PE condition as a basic profitability filter and the size…
The document proposes screening Chinese stocks in the metaverse theme for a circulating market capitalization above 10 billion yuan and a prior-day actual turnover rate within a stated range. Its final rule specifies turnover between 5% and 20%, while an…
The indicator compares the current regression slope of closing prices with stored slopes from confirmed pivot highs and lows. A pivot is identified using highs or lows across a configurable window; the slope over the bars leading into each pivot is saved in…
This document describes a Bollinger Bands variant built around a linear regression of closing prices. The regression line serves as the center; upper and lower bands are formed by adding and subtracting a multiple of the standard deviation of that regression…
This document describes a trading rule that measures a spread’s latest value against its recent average and standard deviation. It uses separate lookback windows for the mean and standard deviation, then calculates a z-score to identify unusually high or low…