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.
31,205 documents
This reference describes a Python toolkit for calculating technical indicators and analyzing trading returns on pandas time series. Its functions cover lagged values, rolling standard deviation and simple averages, exponentially or linearly weighted…
This document outlines a Chinese A-share screening rule that selects stocks with a daily high-low range above one percent, excludes Beijing-listed shares, and requires five moving averages to coincide. It gives example indicator and pandas-style…
This short indicator description proposes calculating a stochastic oscillator from an adaptive, averaged price series instead of raw prices. The filtering step is intended to suppress some price noise before the stochastic calculation, with the adaptive…
This stock screen selects shares with turnover between 3% and 12%, at least one limit-up event in the prior 25 days, and prior-day actual turnover within a stated range of 3 to 28. The document frames these filters as a way to combine market activity and…
This stock selection method looks for shares with daily price amplitude above 1%, a closing or average price above the five day moving average, and an opening price near the ten day moving average. The rationale is that larger price movement may offer…
This document describes an MQL5 indicator that estimates downside tail risk using the Peaks-Over-Threshold method. It converts price changes into loss magnitudes, selects a high quantile as a threshold, and fits a Generalized Pareto Distribution to losses…
This stock-screening idea combines metaverse-sector membership, an arc-shaped price pattern, and daily MACD above zero. The document then expands the proposed conditions with Bollinger Band width below three and a 20-day volatility measure below ten. Its…
This document describes an indicator that marks crossings among three moving averages with arrow objects on a chart. Its stated behavior is that a crossing detected on the current bar can leave a signal behind even if the intersection later disappears. That…
This document describes a variation on the Commodity Channel Index (CCI). Instead of calculating the indicator from the usual typical-price moving average, it uses the average line of a Volatility Step Channel as its input. The channel line is described as…
The document describes a Chinese equity screen requiring a relative strength index below 65, a daily gain above 1%, main-board listing, a robotics concept classification, and circulating market value below 10 billion yuan. It combines a momentum condition…
This document describes an Expert Advisor built around a momentum entry signal. For a buy, it scans a series of MACD readings for a transition from negative to positive, then checks that the close of a selected bar is above its moving average by a…
This post presents a simple Chinese equity screening rule based on daily price range and turnover, while excluding stocks from the STAR Market. The intended rationale is to find shares with both price movement and trading activity, potentially suitable for…
This document describes John Ehlers’ Adaptive Relative Strength Index, an oscillator whose lookback changes with an estimated market cycle. It first smooths the midpoint of each bar, then applies digital filters to derive in-phase and quadrature components.…
The Alf Winner oscillator combines a price measure based on close, high, and low with volume. It normalizes that volume-weighted price against its recent range and applies two weighted moving average smoothing steps, whose periods can be adjusted. The…
The document describes a MetaTrader 5 variant of the ColorMaRsi-Trigger indicator that adds a selectable chart timeframe through an input parameter. Its example default is a four-hour period. The indicator depends on the base ColorMaRsi-Trigger file, which…
The document describes a Chinese equity screen for Shenzhen Main Board stocks. It selects stocks with amplitude above 1, price-to-earnings ratios from 0 to 29.01, price-to-book ratios from 0 to 3.11, and 10-day returns above 0 but below 35. The author frames…
This stock-selection rule screens for turnover between 3% and 12%, a rising K value from the KDJ indicator, and a daily price change bounded around the previous close. The article describes these as technical and market-activity filters intended to identify…
This stock screen selects companies in the metaverse theme whose institutional-flow measure is above zero and whose 14-period RSI is below 65. The stated rationale combines exposure to a popular sector with a positive institutional signal and a momentum…
This short-term stock screen targets A-share companies in the metaverse sector that appeared on the Dragon-Tiger trading list the previous day and had a volume ratio between 1.5 and 6. The document presents the conditions as a way to find stocks with…
The document describes a moving average built with four-fold exponential moving average averaging. The indicator displays its latest value as a price label and allows the displayed indicator levels to be rounded to a selected number of digits. The rounding…
The document describes an equity screen combining a 14-period RSI below 65, three consecutive down days, and the prior day’s actual turnover between 3% and 28%. It presents these filters as a way to find stocks after a short decline while retaining a minimum…
This Chinese A-share screening proposal looks for stocks with daily MACD above zero and weekly MACD above zero, then ranks eligible names by a market-attention measure. The selection is intended to run after the weekly close. Its accompanying example…
A brief VeighNa forum exchange addresses how to calculate daily moving averages, such as a five-day average crossing above a twenty-day average, when a strategy works with intraday data. The suggested approach is to aggregate that data into daily bars and…
This A-share screening idea combines price movement, a short-term reversal pattern, and institutional ownership. It describes filtering for an amplitude threshold above 1, a Morning Star-like signal, and evidence of institutional buying. The indicator…