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 post presents an A-share screening idea that combines a price-range threshold, a rising short-term moving-average condition, and a circulating market capitalization between 5 and 10 billion yuan. The stated rationale is to find mid-sized companies with…
This algorithmic example describes a monthly volatility risk premium trade using SPY options and shares. It selects options with an expiry near one month, sells an at-the-money call and put to form a short straddle, and buys a put with a strike near 15%…
This discussion explains why a stock-selection template that lists close divided by the adjustment factor may not produce that calculated feature in its results. The reported output shows the two inputs as separate columns, suggesting the expression was not…
The screen targets Chinese-listed stocks categorized in the metaverse theme, excludes special-treatment shares, and requires price to be above its five-day moving average. It is intended to run before 10 a.m. and adds a short-term pattern described as a…
The document describes a stock screen requiring turnover between 3% and 12%, an external-to-internal trading volume ratio above 1.3, and inclusion on the prior day’s Dragon and Tiger List, a record of stocks attracting notable market attention. The turnover…
This stock selection approach screens for securities with an RSI below 65, a stated circulating market capitalization range of 5 to 10 billion yuan, and at least seven consecutive declining sessions. It combines a momentum oscillator, company size, and…
This proposed equity screen combines three conditions: today's increase in holdings must exceed 5%, the company must have been listed before 2019, and its 2019 dividend payout ratio must exceed 25%. The article interprets the holdings increase as possible…
This Chinese equity screen combines a daily amplitude filter with robotics-sector membership, a circulating market capitalization below 10 billion yuan, and at least two limit-up sessions during the previous 500 days. The rationale is to find smaller…
This indicator note explains a WaveTrend oscillator port and describes two proposed crossover signals: a bearish signal when the oscillator crosses below its signal line while above the overbought band, and a bullish signal when it crosses above the signal…
This note proposes placing stocks in a watchlist when their amplitude exceeds 1%, they had at least one limit-up event during the preceding 25 days, and prior-day turnover exceeds 60 million. The rationale is that a recent limit-up may indicate market…
This note describes a daily stock screen that combines amplitude above 1, a change in a measure labeled institutional buying, and a positive return. Its final rules define amplitude using a platform-specific indicator, institutional activity through the…
This Chinese stock-screening note proposes selecting shares with daily amplitude above 1%, prior-day turnover above 60 million, and five moving averages that coincide. It presents the screen as a way to find active stocks with relatively flat trends, then…
The article outlines an equity screen combining price amplitude above a stated threshold, rising lows as a sign of an improving base, and a ranking by stock or industry attention. It includes example formula logic and Python-like pseudocode, with the latter…
This discussion explains how to calculate the number of consecutive days since a moving average golden cross using BigQuant’s DAI functions. The suggested approach counts consecutive observations for which the golden-cross condition is false, including the…
This proposed equity screen selects companies with price amplitude above one percent, market capitalization within 10 billion, positive average net profit, and a current low below the previous session’s low. The article’s example code extends those…
This Chinese stock-selection proposal combines three filters: price amplitude above one, a rounded chart pattern, and a nonempty name for outstanding convertible bonds. A referenced indicator formula expresses the amplitude and chart-shape conditions, while…
This stock-selection method screens companies in the metaverse industry using three simultaneous bullish crossovers: MACD above its signal line, the 5-day moving average above the 10-day average, and the 5-day average above the 20-day average. It also…
This code example demonstrates a data workflow for a FinRL stock-trading project. It fetches a single stock’s price history through both yfinance and FinRL’s Yahoo downloader, then downloads a Dow 30 universe over configured training and trading periods. A…
This stock-selection example combines three filters: RSI below 65, revenue in 2021 more than 1.1 times its 2018 level, and an opening price within 6% of the prior close. It proposes selecting the first N qualifying stocks and holding them for one year. The…
The proposed Chinese equity screen selects stocks classified in the metaverse industry when the open is near a ten-day moving average and the 2019 dividend ratio exceeds 25%. The post presents a formula-style version and a Python example. It frames…
The Genesis Matrix dashboard combines four colored signals into a directional filter: TVI slope, CCI relative to zero, T3 moving-average slope, and a GannHiLo price test. The described system triggers a buy when all four indicators turn blue from red, and a…
This indicator extends an RSI adaptive EMA by adding floating levels to help decide when the trend state has changed. The added thresholds are intended to reduce false changes caused by frequent shifts in the indicator’s slope during range-bound markets.…
The document proposes a Chinese equity screening rule combining three chart conditions: price amplitude above a threshold, a rounded price pattern, and three consecutive declining closes. It interprets the amplitude condition as a way to find active stocks,…
This configuration defines a Qlib experiment that trains an IGMTF model on Alpha360 features for CSI 300 stocks. It uses historical data from 2008 through 2020, with training through 2014, validation in 2015–2016, and a held-out test period beginning in…