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.
22,592 documents
This Chinese-language post describes a simple stock screen: select shares with an amplitude above 1, listed for more than a year, and remove those that hit the daily price limit on the previous day. It presents range as a measure related to price movement…
This post describes a stock screen combining three conditions: daily high-low range above 1% of the prior close, a code beginning with 60, and a 2019 dividend ratio above 25%. It explains the range filter as a way to select more volatile stocks, the code…
The author recounts developing and repeatedly revising a set of strategies, then lays out a research-to-deployment process. It begins by defining intended capital capacity, trading costs and order size, the balance between win rate and payoff, training data…
This stock-selection approach ranks candidates by capital strength, described through activity measures such as turnover and volume ratio, and combines that ranking with two price filters: the 20-day moving average must be above the 120-day average, while…
This screen selects listed stocks outside the Beijing A-share market with turnover between 3% and 12% and circulating market value between 5 billion and 10 billion yuan. The article frames the market-value range as a way to focus on stocks with a chosen size…
The article describes a short-term stock screen based on daily price movement and trading activity. It selects stocks with amplitude above 1, a daily percentage change between −5% and −4%, and a volume ratio above 1.5 but below 6. The proposed interpretation…
This stock screen combines turnover between 3% and 12%, membership in a robotics concept group, circulating market value below 10 billion yuan, and a recent KDJ golden cross. The article presents the crossover as a possible sign of upward price movement and…
This research summary explains a monthly stock-selection factor built from news sentiment. It combines the direction of news evaluations with their volume by summing sentiment scores for each stock over the month. The factor is refined by retaining news…
This note describes a simple Chinese stock screen that selects companies classified in the metaverse theme, with a positive one-day price change, while excluding stocks on the STAR Market. It also adds a positive market-capitalization condition. The article…
A user questions a discrepancy between a stock’s reported historical prices and the value returned by a platform’s current-data function. For a Chinese-listed equity on one trading date, the user reports historical opening, closing, and low prices that…
This page summarizes momentum and reversal as opposing patterns in stock returns. Momentum describes the tendency for stocks with stronger past performance to continue outperforming weaker past performers; the note associates it with trend trading and…
This Chinese equity screen first limits candidates to the metaverse sector. It then looks for stocks whose opening price is near or above the 10-day moving average, using a prior-close and prior-open comparison in the provided formula, and requires the…
This Chinese stock-selection strategy looks for shares in the beverage and alcohol import-export industry with a 14-period RSI below 65 and large-order net flow above 0.05 for at least three consecutive days. Its stated rationale is to combine a technical…
This Chinese equity screen combines three conditions: the opening price at 9:25 must be less than 6% above the previous close, the 20-day moving average must be above the 120-day average, and stocks are ranked by volume ratio, with the top 100 selected. The…
This guide introduces statistical learning through an example of predicting the S&P 500 from company fundamentals. It frames the task as estimating a response from predictor features plus residual error, then distinguishes prediction, which prioritizes…
This note describes a Chinese equity screen that combines a turnover band of 3% to 12%, a rising DEA condition, and a prior-day closing price of 18.5 yuan. It provides a platform formula and a Python-oriented example, both intended to identify stocks meeting…
The document proposes screening Chinese equities for price amplitude above a threshold, a high daily control measure, and at least five overlapping or aligned moving averages. It presents this as a technical filter intended to narrow the universe, then…
This report overview surveys supervised learning methods with potential uses in investment research. For regression, it distinguishes penalized models such as Lasso, ridge, and elastic net, which can address correlated inputs, from nonparametric approaches…
This Chinese-language post outlines an A-share screening idea focused on metaverse stocks. Its initial filters are prior-day turnover above 8% and a measure intended to identify afternoon large-order net inflows. The author discusses dependence on…
This note presents an equity screen combining turnover between 3% and 12%, seven consecutive non-rising closes, and a positive daily MACD reading. It frames the setup as a way to identify stocks that have recently weakened while retaining a positive trend…
This note describes a short-term A-share screening rule for stocks classified in the metaverse industry. It looks for an opening price crossing above the 10-day moving average after the previous close and open were below that average, along with a daily…
This post describes a Chinese equity selection rule that combines an amplitude threshold, a morning-star style candlestick condition, and an estimate of prior-day dominant capital control. The accompanying implementation sketch adds moving-average alignment…
The document proposes a short-term Chinese equity screen combining an amplitude threshold, a 15-minute MACD histogram that is negative but contracting, exclusion of special-treatment stocks, an early-morning selection time, and a limit-up pattern referred to…
This stock screen selects shares with turnover between 3% and 12%, a positive product of daily price change and net large-order flow, and a bullish alignment among three moving averages. The accompanying example uses the 5-, 10-, and 20-day averages,…