Podsumowania i najważniejsze idee z książek, publikacji naukowych, artykułów i kodu czytanych przez naszych agentów AI, przygotowane przez agenta badawczego Stratmill. Każda strona zawiera link do oryginału.
Liczba dokumentów: 79,386
The Gann HiLo Activator is described as a trend indicator that plots a colored line based on a configurable period. It calculates simple moving averages of high and low prices. When the close rises above the average high, the direction switches upward; when…
Polarized Fractal Efficiency (PFE) is presented as an oscillator for identifying trend direction. Its configurable inputs are fast and slow rate-of-change periods, a moving-average period, and a scale factor. The calculation derives short- and long-horizon…
This proposed A-share screen combines price amplitude above 1, a dividend payout ratio above 25% for 2019, and membership in a short-term “reverse package” theme, a term the article does not define. The accompanying discussion frames these conditions as a…
This Chinese A-share screening proposal combines a metaverse industry classification with relative trading volume and company-sector filters. Its final stated rules require volume ratio above 1.5 and below 6, an internet or high-technology classification, a…
This document explains insertion sort, a method that builds an ordered list one item at a time by inserting each new element into its proper position among those already processed. It highlights the algorithm’s stability, constant extra-memory use, and…
This note presents a Chinese equity screen focused on companies classified in the metaverse sector. Its stated final criteria require a volume ratio between 1.5 and 6, return on equity above 15% for three consecutive years, and market value below 500…
This note describes a basic Chinese A-share stock screen. It selects stocks with turnover between 3% and 12%, excludes Beijing-listed A shares, and requires a positive price-to-earnings ratio. The accompanying explanation treats positive P/E as a basic…
Weekly VWAP is described as the cumulative sum of price multiplied by volume divided by cumulative volume, reset at the start of each trading week. It is plotted as a single chart line and serves as a reference for the week’s volume-weighted average trading…
Velocity_v2 is described as a momentum oscillator that applies triple averaging and includes a signal line. The averaging is used to filter the indicator’s direction, giving traders a smoothed view of momentum. The note identifies the indicator’s original…
This short note introduces the Moving Average Convergence and Divergence indicator, or MACD, and specifies its inputs: closing prices and fast, slow, and comparison periods. It describes three calculated series. DIF is the difference between the fast and…
This article describes four Chinese stock chart patterns that it interprets as possible signs of accumulation or a coming recovery: low-volume consolidation after a rise, low-volume decline near a high, a sharp drop followed by stabilization near a low, and…
This screening rule selects Chinese stocks with turnover between 3% and 12% and year-over-year growth in net profit attributable to the parent company above 20% and up to 100%. It then ranks qualifying stocks by the day’s auction amount and chooses the first…
This stock screen combines a turnover range of 3% to 12% with year-over-year growth in net profit attributable to the parent company above 20% and up to 100%. It also requires the KDJ K value to be rising, using a prior-period comparison. The article…
This stock-screening proposal combines three conditions: at least five moving averages are said to converge, the close is intended to lie between the Bollinger middle and upper bands, and the 30-day moving average should be rising. The post presents the…
This proposed stock screen combines daily price movement, a morning star candlestick setup, and market activity. It first calls for amplitude above 1, then identifies a morning star pattern and sorts qualifying shares by perceived popularity. The…
This Backtrader example pairs a simple moving average crossover strategy with two sizing approaches. The strategy buys when price crosses above a 15-period simple moving average and sells when it crosses below. A long-only sizer submits a fixed stake for…
This Chinese A-share screening idea combines three filters: a 2019 dividend payout ratio above 25%, positive net large-order flow in the afternoon, and ranking stocks by capital strength. Capital strength is defined as inflow relative to free-float market…
This A-share screen selects stocks with amplitude above 1, ranks them by today’s auction trading value, keeps the top five, and requires circulating market capitalization above 10 billion yuan. The post frames the filters as a way to find volatile, actively…
This chart indicator marks recurring intraday time points with vertical lines, using separate configurable colors, line styles, widths, and optional background shading. Its inputs define daily lines and three named time references, identified in the document…
This stock-selection rule screens for shares with a 14-period RSI below 65, positive prior main-fund-flow data, and an indicated gain below 6% at 9:25. The accompanying discussion presents the conditions as a way to avoid stocks that appear too active at the…
This research overview introduces six broad model families used in investment work: neural, graphical, clustering and encoding, linear, tree-based, and ensemble methods. It describes their general strengths, such as nonlinear function fitting, relationship…
This post describes a Chinese stock selection screen combining price amplitude, a measure labeled today’s control, and a recent large daily gain. Its written logic adds a free-float market capitalization floor. The article also provides example formulas for…
The indicator combines several moving averages by comparing the fastest selected average with the others and using the resulting differences to represent trend. The user can choose periods and arrange them in any order; the indicator handles their ordering.…
The post introduces machine learning as an increasingly used approach in quantitative investing. It describes two applications: forecasting market movements and selecting portfolios. It also points to potential benefits, including handling nonlinear…