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 document describes a Chinese stock screen focused on the metaverse sector. It looks for three concurrent bullish crossovers: MACD over its signal line, the 5-day moving average over the 10-day average, and the 5-day average over the 20-day average. It…
This document presents an equity selection rule that combines a positive MACD signal, a 20-day moving average above the 120-day moving average, and year-over-year parent-company net profit growth above 20% and up to 100%. Its example implementation scans…
This note outlines a daily stock screen combining a positive MACD reading, a share price below a stated ceiling, and a measure of trading activity based on the prior day’s turnover rate and the ratio of current auction volume to the previous day’s volume. It…
This note describes a stock screen based on three chart conditions: amplitude above a threshold, a rising price base, and a rounded-bottom pattern. It presents the pattern as a way to find stocks whose lows are gradually moving higher, while filtering out…
This brief template note describes a stock-screening strategy that selects companies using natural-language queries. Its example combines a small-market-cap screen with recent net inflows from large investors, a prior period of contracting volume, and a…
This proposed EUR/USD strategy combines Bollinger Bands, a conventional RSI, a Traders Dynamic Index (TDI), and a custom ATR-based stop line. Long setups begin when price and the momentum measures reach specified lower extremes; the system then waits for the…
This indicator recreates Renko-style bricks with visible high-low wicks in ProRealTime, whose built-in Renko candlestick display does not show them. Because the platform cannot draw these candles directly on a non-time-dependent chart and its Renko bricks…
This indicator description outlines a semaphore-style signal based on divergence between fast and slow Commodity Channel Index oscillators. It compares the oscillators at extreme points within the most recent five bars and requires a suitable candlestick…
The document outlines a stock screen that selects companies in a designated metaverse category, requires a positive recent return, and excludes firms listed for less than a chosen period. Its rationale is to combine thematic exposure and recent performance…
This post describes a rule-based screen for Chinese main-board stocks. It selects shares with turnover between 3% and 12%, a daily gain above 1%, and a circulating share count no greater than 5.5 billion. The stated intent is to combine trading activity,…
Ultra Trend is presented as a trend-detection indicator built from multiple Jurik-smoothed price series. The described procedure calculates several smoothed values, checks the slope of each, and combines the upward and downward slope readings to determine…
DS Stochastic is described as a version of the Stochastic Oscillator that applies exponential moving average smoothing. The entry identifies it as an indicator and notes that its implementation relies on a smoothing library for intermediate calculations. It…
This Chinese-language stock screen combines an amplitude threshold above 1, a ranking among the five highest stocks by the day’s auction amount, and a rising K value from the KDJ indicator. Its rationale is to find stocks with larger price movement and…
The Gann High-Low Activator tracks separate averages of recent highs and lows. The document says the close determines which curve is displayed, and describes the resulting line or channel as a level that price must break to signal a possible trend change.…
This Chinese-language screening note describes selecting stocks with turnover between 3% and 12%, an external-to-internal trading volume ratio above 1.3, and a reversal pattern called “fan bao.” The stated rationale is to find active stocks where buying-side…
The document presents daily portfolio rebalancing as a Markov decision process. An agent selects nonnegative weights for Dow 30 stocks, normalized to sum to one, using a state that combines a rolling covariance matrix with MACD, RSI, CCI, and ADX indicators.…
This stock-selection post proposes combining three filters: an increase in holdings share above a stated threshold, weekly MACD above its zero line, and at least two limit-up sessions within a stated lookback period. The accompanying explanation treats these…
This brief indicator description presents Stowell’s upper and lower three-bar net line patterns. It explains the indicator’s interpretation: a breakout of the three-candle line is treated as a possible trend change. The description distinguishes between…
This tutorial explains how to add reusable template libraries to trading strategies and use a cryptocurrency spot-trading library. It demonstrates calling library buy and sell functions from interactive controls, while the template handles details such as…
Q-Trend combines a rolling price midpoint with average true range to create a trend line and volatility-adjusted thresholds. The midpoint is calculated from the highest and lowest source values over a chosen trend period. A sensitivity band is formed by…
The document describes a chart indicator that organizes price swings into higher highs, higher lows, lower highs, and lower lows. For each completed move, it displays the distance in pips, the number of bars, and the retracement percentage. A Market Story…
This post presents a Chinese stock-selection screen using three main conditions: RSI below 65, float market capitalization between 5 billion and 10 billion yuan, and positive net buying attributed to major participants during the opening auction. Its Python…
This post describes a Chinese A-share stock screen combining daily price range, company size, profitability, turnover activity, and a weekday filter. It selects stocks with a daily high-to-low range of at least 1%, market capitalization up to 10 billion…
This stock-selection method screens Chinese equities using three filters: a 14-period RSI below 65, a ten-day price gain above zero but below 35%, and a ranking by individual-stock popularity. The accompanying Python example additionally considers stocks…