Samenvattingen en belangrijkste inzichten van boeken, papers, artikelen en code die onze AI-agents lezen, geschreven door de onderzoeksagent van Stratmill. Elke pagina verwijst naar het origineel.
79,386 documenten
This note describes a Chinese equity screen combining three conditions: price amplitude above 1, circulating market capitalization above 10 billion yuan, and at least one limit-up event in the prior 25 days. It frames the screen as a way to find larger,…
This Expert Advisor uses fast and slow moving averages to determine direction. It opens a buy when the fast average is above the slow average by a configurable minimum distance, and a sell under the opposite condition. The EA can close or retain existing…
The NRTR indicator tracks price extremes using highs and lows, maintaining a preset distance from the most recently reached extreme. In an uptrend it appears below price, while in a downtrend it appears above price. The offset is intended to ignore modest…
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 Expert Advisor opens trades when a colored Sidus indicator arrow appears at a bar’s close. Its entry logic is therefore driven by the indicator’s directional signals, with the signal confirmed only after the bar completes. The document describes the…
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 documentation describes vaults built on HyperEVM using CoreWriter and precompiles. Builders can create and tokenize vaults with customizable accounting, and may follow standards such as ERC-4626. The described design supports onchain read and write…
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 research outline proposes allocating among equity industries by tracking the behavior of different market participants. It motivates industry rotation with the observation that returns can diverge substantially across sectors and styles, so broad asset…
The indicator estimates the relative strength of a selected currency from the closing prices of seven currency pairs that contain it. A zero reference line represents the average over a configurable period, and a colored histogram is intended to make 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…
The document describes a commodity futures strategy that ranks 28 markets by changes in Twitter-derived sentiment. It calculates daily sentiment from keyword-matched posts using a financial sentiment dictionary, then forms equal-weighted long and short…
This document outlines an event-driven study of how MSCI inclusion announcements affected the prices of Chinese A-shares. It describes estimating CAPM parameters from a historical period, using those parameters and subsequent market index returns to…
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 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…
This strategy organizes research and trading for same-day-expiration bear call spreads through separate agents. A researcher gathers account and market information, checks the listed expiration, contract Greeks, and bid-ask quality, then identifies a short…
The document summarizes research on forecasting multiple future steps from limit order book data. Rather than predicting only one future point, the proposed approach uses sequence-to-sequence encoder-decoder networks with attention to generate a path of…