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
This Chinese-language post describes an equity screen for metaverse-related stocks. It combines an upward-sloping 30-day moving average with a measure intended to compare aggressive buying volume (外盘) against selling volume (内盘), using a stated threshold of…
The Simple Decycler is described as a price indicator based on John F. Ehlers’s Decyclers article. It has configurable settings for the high-pass filter period and applied price. The stated purpose is to remove low-frequency directional components while…
This career guide outlines a self-study path for aspiring quantitative developers. It emphasizes strong programming and numerical implementation skills, with language choices shaped by likely workplaces: C++ and Python for broad applicability, while Java or…
This Chinese-language post describes an equity screen using three conditions: RSI below 65, turnover between 3% and 12%, and at least two limit-up price moves during the prior 500 days. It presents the screen as a way to find stocks with recent strong moves…
This MetaTrader 5 indicator displays two colored rectangular zones derived from the Bollinger channel levels of an XMA_BBx5_Cloud indicator. The levels are calculated for a selected chart timeframe and shown on a bar, allowing a trader to view bands from a…
The article introduces staking as participation in proof-of-stake networks, where validators help verify transactions in return for rewards. It outlines three routes: staking through a centralized exchange, delegating through a staking pool, or operating as…
The Range Action Verification Index (RAVI) is described as a trend-detection indicator based on the percentage difference between current and past prices. The document gives threshold-crossing rules attributed to its developer: an upward cross of a 3%…
This example compares a grid market-making strategy in HftBacktest using CME market-by-order Level-3 data and Level-2 depth reconstructed from that same feed. Quotes are arranged around a reservation price that shifts with inventory, with position limits,…
This document describes a Balance of Power indicator displayed as a colored histogram. It presents the histogram as a way to view the strength and direction of the current trend, with colors determined by configured overbought and oversold levels and by…
This stock-selection idea combines daily amplitude above 1, positive large-order net volume for at least three consecutive days, and a nonempty outstanding convertible-bond name. The post treats the bond condition as a way to identify companies that have…
This short-term stock screen selects shares with daily amplitude above 1, evidence of prior-day large-player control, and a closing price between the 20-period Bollinger middle and upper bands. The post explains that Bollinger Bands use a moving average and…
This stock screen combines a metaverse theme filter with RSI below 65 and a 15-minute MACD condition intended to identify a shrinking negative histogram. The post describes it as a way to find short-term trading opportunities in volatile stocks. Its formula…
This research summary reviews several quantitative approaches to interpreting China’s 2018 equity market. It reports historical analysis of rebounds in the CSI 1000, saying smaller-cap indexes tended to outperform larger ones during rebounds, while sectors…
This example describes a short-term screen for Chinese stocks assigned to the metaverse theme. It filters for a volume ratio above 1.5 and below 6, a three-day limit-up sequence as of the prior day, and, in its final stated logic, market value below 500…
This stock-screening example combines a turnover filter, a reversal-style candle condition, and simultaneous moving-average crossover signals. Its stated selection logic requires turnover between 3% and 12%, a reversal pattern, and upward crossings among the…
A forum exchange discusses why VeighNa’s StatisticalArbitrageStrategy example uses a ten-unit price offset when starting its spread-trading algorithm. The questioner describes the order logic: a leg order is sent when the spread order price would otherwise…
This post outlines an A-share screen for stocks with amplitude above 1, excluding ST-designated shares, selected before 10 a.m., priced below 12, and matching a named five-step limit-up approach. The sample Python implementation operationalizes the last…
This post describes an A-share screen combining an intraday amplitude above 1%, a positive ten-day return below 35%, and an auction gain between -2% and 5%. It frames the amplitude as a measure of movement, the return band as a way to avoid both declining…
This Chinese stock-screening post proposes selecting A-shares with at least five converging moving averages, a limit-up day within the prior 25 days, and a 9:25 auction gain below 6%. It presents the combination as a way to find stocks with aligned trends…
This BigQuant framework describes a workflow for computing and evaluating minute-frequency stock factors. Researchers define factors in SQL against a specialized derived minute-bar table, assign an output table name, and run the program to calculate and…
This document describes a chart indicator that marks candles extending beyond a sliding channel formed from recent highs and lows. Candles outside the channel receive different fill colors depending on whether their movement agrees with or opposes the…
A liquidity miner describes using automated market making on crypto pairs, drawing on a traditional finance background and several years of crypto trading. The approach is pure market making: set spreads, monitor inventory, and adjust parameters as…
This short-term stock screen selects shares when MACD is above zero, short-term moving averages are rising or spreading upward, and the latest price change lies between -5% and 2.6%. The article describes the conditions as a way to combine price trend…
This document is a brief outline of a presentation on machine learning in finance. It names four application areas: Lasso regression for commodity futures price prediction, decision trees for detecting possible financial fraud, logistic regression for…