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
The document discusses how to judge excess returns in an index enhancement strategy. It argues that alpha should be evaluated by its persistence and consistency against a benchmark, rather than by the size of gains in a brief period. It describes monthly…
This Chinese-market stock screen combines a turnover filter, buy-side order-book volume, and a named candlestick pattern. It selects listed main-board stocks with turnover between 3% and 12%, buy-one volume greater than sell-one volume, and a same-day…
This document describes a stock selection screen combining three conditions: prior-session price range greater than 1%, the current open within 5% of the 10-day moving average, and turnover above 60 million. It presents the conditions as indicators of recent…
This document outlines a Turtle style trend following strategy for stocks. It buys when the close crosses above the prior 20 trading days’ high and exits when the close crosses below the prior 10 trading days’ low. The examples define signals using current…
This document presents a Chinese equity screening rule that selects stocks with turnover between 3% and 12%, circulating market value between 5 billion and 10 billion yuan, and a positive 10-day price return below 35%. It frames turnover as a measure of…
The post proposes a stock-selection screen based on three conditions: RSI below 65, seven consecutive declining sessions, and previous-day turnover above 8%. It frames RSI and the run of down sessions as technical filters, with turnover as a measure of…
The document uses national input-output tables to map intermediate flows between industries and represent those relationships as networks. It compares China across several historical snapshots with the United States in a later snapshot, using network…
The post asks how to calculate, for each instrument, the rolling five-day count of sessions when a stock value exceeds an index value, then assign those counts back to the original dataframe. The author reports that the grouped calculation produces the…
The post proposes a short-term stock-selection rule combining daily amplitude, the 9:25 price gain, and a product of price change and a large-order net-flow measure. It screens for stocks with amplitude above one, a 9:25 gain below six percent, and a…
The document examines a top-ranked strategy that selects the smallest stocks in the CSI 1000 and reports unusually strong historical performance. Its central lesson is that a backtest must use index constituents as they were known at each historical date.…
This post outlines a Chinese equity screen for stocks classified in the metaverse industry. It ranks candidates by the day’s opening-auction amount, keeps the top five, and restricts the auction price change to between -2% and 5%. The post gives…
This post describes a Chinese equity screen that selects stocks with a daily high-low range above 1%, at least two apparent limit-up moves over the preceding 500 days, and prior-day turnover above 60 million. It presents both indicator-style conditions and a…
This stock-selection rule screens for shares with turnover between 3% and 12%, circulating market value between 5 billion and 10 billion yuan, and a proprietary “today control” measure above 21. The post presents the combination as a way to find stocks with…
The document explains an indicator that automates wedge-style chart analysis by detecting pivot highs and lows, connecting selected pivots into upper and lower trendlines, and projecting each line forward by 20 bars. A configurable lookback determines which…
The document describes a Chinese equity strategy that first screens for companies with high dividend yields, PEG below one, and share prices between 3 and 15, then selects stocks with market capitalizations below 10 billion yuan. It frames these rules as a…
This equity factor study measures buying and selling pressure through the amount of time a stock spends at different positions within its recent price range. It first normalizes price between the interval’s high and low to obtain relative price position…
This Chinese equity research note develops industry-rotation signals from several types of institutional money flow. It treats a flow source as useful when its derived signals show a reasonably orderly relationship across ranked groups and a tolerable…
This stock-selection recipe screens mainland China main-board shares using three conditions: RSI below 65, a daily gain above 1%, and a 9:25 change below 6%. The author presents the RSI threshold as a way to find shares with room to rebound, the positive…
This Chinese-language post introduces a custom-coded stock selection strategy on the BigQuant platform. It says the strategy combines five selection rules, mainly seeking stocks that have fallen sharply and begun to stabilize, while also attempting to…
This Chinese-language post describes a stock selection screen combining three conditions: RSI below 65, the day’s volume above 1.05 times the prior day’s volume, and the absolute move from the previous close to the opening price below 6%. It frames the…
The report presents a framework for dynamically adjusting multi-factor portfolio weights by forecasting factor returns and their covariance. It replaces unconditional expectations with conditional expectations based on external market variables, allowing…
The document presents a Chinese equities screening idea focused on beverage manufacturing stocks. It filters for turnover rates between 3% and 12% and a ratio of outside to inside trading volume above 1.3, with the sector described as having active beverage…
The document presents a short-term stock screen combining daily price range, positive large-order net volume over several consecutive days, and a stock-code prefix. Its stated final conditions are amplitude above one, net volume above 0.05 for at least three…
The document describes adding a custom Rank Information Coefficient module to a stock-ranking strategy. The module reports average RankIC separately for the training set and the test set, providing a way to assess how well a model’s rankings align with…