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This stock selection approach filters for shares associated with the metaverse theme, then checks for an upward-sloping 30-day moving average and sorts qualifying names by a measure of individual stock interest. The article presents this as a way to combine…

This Chinese stock-selection note proposes screening for price amplitude above 1, large-order net-volume readings above 0.05 over at least three consecutive days, and then ranking by fund strength. It presents the combination as a short- to medium-term way…

This Chinese stock-screening note combines three ideas: rank stocks by volume ratio as a proxy for fund strength, require the previous day's adjusted turnover rate to exceed 8%, and look for a shortening MACD histogram on a 15-minute chart. It presents the…

This Chinese stock-selection note combines a turnover-rate range of 3% to 12% with a reversal pattern and a signal described as the start of a major advance. Its example formula adds a close-above-moving-average condition and platform-specific filters. The…

This short-term equity screen combines a large daily range, a recent strong up day, elevated current volume, and an opening price above the prior close. The stated lookback is 25 trading days for the strong-gain condition. The article describes the…

This stock screen combines an amplitude threshold with simultaneous crossovers among three moving-average pairs and at least one limit-up event during roughly the prior month. It is presented as a way to find shares showing strong recent movement and…

The post describes a stock screen requiring a ticker that begins with 60, turnover between 3% and 12%, and total market value above 200 million yuan. Its Python example retrieves listed-stock information, checks the ticker prefix, and then filters daily data…

This post outlines a stock selection screen based on three stated conditions: association with the metaverse theme, an upward-sloping 30-day moving average, and turnover between 2% and 9%. The accompanying indicator references and Python example illustrate…

This document presents a short-term Chinese stock selection rule based on three market activity measures: turnover between 3% and 12%, first-level bid volume greater than ask volume, and a volume ratio between 1.5 and 6. It frames the turnover and order-book…

This document describes a daily stock screen combining price movement and a basic valuation condition. It selects stocks with amplitude above 1, at least two limit-up events within the prior 500 days, and a positive P/E ratio. The rationale is that recent…

This tutorial compares three ways to train an XGBoost model for stock selection: ranking securities by a score, classifying outcomes into categories, and predicting a numeric target through regression. It frames these choices within a broader modeling…

This Chinese-language article proposes screening mainland-listed stocks for a turnover rate between 3% and 12%, excluding Beijing-listed shares, and requiring a rising-bottom pattern. Its accompanying Python example adds further filters, including excluding…

The document describes a Chinese equity screening rule combining three conditions: daily amplitude above a threshold, evidence of main-fund control on the previous day, and a close above the previous day’s low. It frames the combination as a way to find…

This China-stock screen combines three conditions: turnover between 3% and 12%, appearance on the previous day’s Dragon-Tiger list, and a 20-day moving average above the 120-day moving average. The post frames turnover and the market activity list as…

This stock screen selects companies associated with the metaverse concept, then applies a price condition and a relative-volume band. It requires the close to exceed the previous session’s low and volume relative to its five-session average to be above 1.5…

This Chinese equity screen combines three conditions: at least five moving averages are described as converging, the tradable share float is no more than 5.5 billion shares, and the ten-day return is positive but below 35%. The article frames this…

This stock screen combines turnover between 3% and 12% with seven consecutive sessions in which the closing price falls, then filters for a daily price change below 2.6% and above -5%. The article presents the rule as a way to find stocks after a sustained…

This stock screen combines three daily price conditions: amplitude greater than 1%, an opening price within 5% of the 10-day moving average, and a current low below the previous day's low. The document includes formula and Python examples for calculating…

This equity screen targets stocks in the metaverse sector that recorded a limit-up move within the prior 25 days and whose opening price falls between 2% below and 5% above the reference close. The post describes selecting candidates before 10 a.m. for…

This stock screen combines a daily turnover rate between 3% and 12%, a positive change in the KDJ K value, and an indicator intended to identify institutional accumulation. The document provides formula and Python examples, with the Python version also…

The document explains the Crank–Nicolson implicit finite-difference scheme for solving the one-dimensional heat equation. It contrasts this approach with an explicit method that requires small time steps, describing Crank–Nicolson as averaging spatial…

The document proposes a stock screen combining three conditions: relatively large price amplitude, upward divergence in the day’s moving averages, and a limit-down price at the prior session’s 9:15 matching stage. The stated rationale is to find volatile…

This Chinese A-share stock screen selects shares with a daily high-low range above a stated threshold, while excluding Beijing-listed stocks and specified board categories. The article also describes a refinement that keeps prices close to a 60-period moving…

The document describes a Chinese stock selection screen that combines a 14-period RSI below 65, the product of percentage price change and an oversized-order net inflow measure above 1, and a circulating market capitalization between 5 billion and 10 billion…