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.
79,386 documents
This brief description introduces an expert advisor built around two moving averages. It pairs their signals with a trailing stop, which adjusts the exit level as a position moves favorably. The document credits one person with the trading idea and another…
This indicator classifies closes by their relationship to the previous bar and tracks the length of each resulting sequence. It describes internal closes as closes within the prior bar’s range, which may reflect sideways or slowly drifting price action; a…
This forum post describes a reproducibility question in a quantitative modeling workflow. The author sets a learning-rate parameter to the same value in the model and in a hyperparameter-search module, and configures the search grid to contain only that…
This document proposes a Chinese stock-selection screen focused on companies associated with the robotics theme. Its initial criteria are daily amplitude above 1%, circulating market capitalization below 10 billion yuan, and return on equity above 15% in…
This document describes a short-term A-share stock screen that excludes Beijing-listed shares, selects stocks with turnover between 3% and 12%, and looks for upward ordering of five moving averages, from the 5-day through the 120-day average. It explains the…
The proposed stock screen combines three signals: daily amplitude above one percent, a reversal or engulfing-style pattern appearing within the recent three-session window, and pre-open auction buying volume from large and very large orders above seven…
This proposed A-share screen selects stocks whose turnover rate is between three and twelve percent, whose codes begin with 60, and whose previous-day turnover exceeds eight percent. The code example retrieves daily basic data, applies these filters, then…
This A-share stock screen combines three conditions: daily high-low amplitude above one percent, a closing price of 18.5 yuan, and the 20-day moving average above the 120-day moving average. The moving-average comparison is intended to select stocks with a…
This post describes a stock-selection screen that combines daily price amplitude above 1, at least one limit-up event during the prior month, and a 2019 dividend ratio above 25%. It frames the conditions as a blend of price movement, recent market attention,…
This proposed stock screen combines price activity with a float-size constraint. It seeks stocks with amplitude above 1%, a three-session limit-up streak ending the previous day, and circulating shares no greater than 5.5 billion. The post’s suggested…
This proposed stock screen combines elevated price movement with a sector-related condition. It looks for amplitude above 1%, at least one daily gain of 10% or more during the preceding 25 trading days, and a link to beverage or alcohol imports and exports.…
This stock screen combines three conditions: daily high-to-low amplitude above 1%, a dividend yield associated with 2019 above 25%, and a rising K value in the KDJ oscillator. The accompanying examples describe calculating amplitude from high, low, and open…
The document introduces an indicator that changes the spacing of price bands according to price’s position relative to a central average. Conventional bands are described as symmetric around that average, which can leave the band on the opposite side far…
This proposed EUR/USD strategy on an hourly chart looks for a sharp move through a Bollinger Band followed by a close beyond the opposite band. The long setup also requires the positive directional indicator to exceed the negative one, while the short setup…
The document explains sliced inverse regression (SIR) as a supervised dimension-reduction method for financial prediction. Unlike principal component analysis, which summarizes variation in predictors without using the target, SIR uses the forecast variable…
This stock-selection proposal combines volatility and momentum conditions to find Chinese equities with pronounced price movement. Its initial screen looks for an amplitude threshold, at least one daily gain of 10% or more during the recent 25 trading days,…
This introductory explanation presents support vector machines as supervised classification models. In a simple two-class example, many lines can separate the data, but SVM selects a boundary that maximizes the margin between the two classes. The closest…
This Chinese A-share screening idea selects stocks with a daily high-low range above 1%, a newly formed KDJ crossover, and a concentration measure below 70%. The post interprets a wide range as elevated volatility and the crossover as improving market…
This proposed Chinese equity screen combines three filters: daily turnover between 3% and 12%, an appearance on the previous day’s 龙虎榜 (a list highlighting unusual trading activity), and external volume exceeding internal volume by a ratio above 1.3. The…
The article explains how data supports several spot Bitcoin ETF functions: calculating net asset value (NAV) and intraday indicative value, checking reserves, and helping authorized participants trade around price deviations. Because Bitcoin trades…
The document describes a daily Chinese-equity stock screen combining price action, trading activity, market capitalization, and profitability. The proposed filters require an intraday range above 1% relative to the prior close, market capitalization below 10…
This Expert Advisor combines a random-number gate with a moving-average price relationship to decide whether to open a position. The random draw is compared with one of eight thresholds represented by poker-hand labels; if it falls below the selected…
The document introduces spot trading as buying or selling cryptocurrency at the current market price, where the trader transacts in the asset rather than a futures or options contract. It outlines basic setup: choose an exchange that lists the desired pair,…
This overview surveys models used to relate asset values or expected returns to risk and other inputs. It describes CAPM as linking expected return to the risk-free rate and market exposure, then introduces multifactor models such as Fama–French, which add…