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 introductory article explains how rapid price rises fueled by investor enthusiasm can develop into cryptocurrency bubbles and sharp declines. It identifies new investor inflows, fear of missing out, media attention, and imitation of other buyers as…
This brief description presents an indicator that uses Average True Range as a reference for placing stop losses. It draws two bands at equal distances from a moving average, making the bands a volatility-based guide around a baseline. The text also proposes…
This document describes a Chinese equity screening rule combining daily range, a reversal condition, three consecutive down days, and a comparison of recent highs with a longer lookback period. Its final version also requires the highest price over the last…
The document describes an on-chart spread monitor for tracking trading costs on a selected symbol. It calculates spread from the live Ask and Bid, then displays the current value alongside the minimum, average, and maximum observed since the indicator was…
The document traces several ways to build moving average trading rules, using a 15-minute Chinese rebar futures index as its backtest example. It starts with price crossing a single average and short-period averages crossing longer ones, then adds…
The document describes two strategies using an adaptive baseline and volatility bands. For momentum breakouts, it calls for a candle close beyond an outer band and a baseline color indicating the same direction; the trade is held until the baseline changes…
The proposed equity screen combines intraday range, Bollinger Band position, and a specific opening-time price-change condition. It looks for stocks whose range exceeds its recent average, whose close lies between the middle and upper Bollinger Bands, and…
This stock-screening proposal combines a range condition, rising lows, and a long-term trend filter. It selects shares with amplitude above one, a rising bottom, and the prior close above the 250-day moving average. The suggested expanded version adds a…
This proposed stock screen looks for prior-session amplitude above the stated threshold, an opening price within a narrow band around the 10-day moving average, and a rising K value in the KDJ indicator that remains below its ceiling. The explanation treats…
This proposed equity screen selects stocks with MACD above zero, positive net profit, and a rounded price-recovery pattern. The document interprets positive MACD as evidence of an upward trend, profitability as a basic quality check, and the rounded shape as…
This stock-selection proposal combines a positive MACD reading, a favorable but undefined company-quality condition, and at least two limit-up events within the stated 500-trading-day lookback. The rationale is that positive MACD suggests an upward trend,…
This report adapts Peter Lynch’s six company categories to China’s A-share market: slow-growing, stable-growing, fast-growing, cyclical, turnaround, and asset-rich companies. Its selection framework first screens for a positive price-to-earnings ratio and a…
This document describes an RSI divergence indicator that draws RSI in a separate pane and marks detected divergences both there and on the price chart. Its inputs include the calculation period, applied price, overbought and oversold thresholds, and separate…
This A-share stock screen selects candidates before each trading day’s open using three conditions: MACD above zero, price below 12, and RSI below 65. The document presents the rules as a technical filter intended to combine a positive trend signal with a…
This MetaTrader 5 indicator combines pivot-based swing analysis with Fibonacci retracements. It labels swing points as higher highs, higher lows, lower highs, or lower lows, and classifies breaks as continuation events (BOS) or possible reversals (CHoCH).…
This post outlines a China A-share screening approach focused on companies classified in the metaverse industry. Its final version combines a volume ratio between 1.5 and 6, market capitalization above one billion yuan, a top-100 relative-strength ranking,…
This indicator divides a bar’s reported volume into upward and downward components using the bar’s open, high, low, and close. It defines the upward coefficient as the distance from the open to the high, and the downward coefficient as the distance from the…
This study examines whether Chinese fund management companies’ visits to listed firms are associated with the performance of the funds they manage. It proposes that visits can improve investment decisions by giving institutions access to operational details…
This mid-week recap summarizes derivatives conditions in Bitcoin and Ether as of April 21, 2023. It reports stable Bitcoin 10-day realized volatility in the low 30s while attention shifted toward Ether, and describes term structures for both assets as moving…
This post presents a long-only DAX strategy on 15-minute bars. It looks for a prior lower low, a current high below the earlier rolling high, a bullish candle, a limited bar range relative to the recent high-low span, and a close above SuperTrend. The…
This Chinese-language post describes a stock screen combining three conditions: at least five moving averages converge, price-to-earnings ratio is positive, and the gain observed at 9:25 is below 6%. The post frames moving-average convergence as a sign of a…
The Absolute Strength Histogram estimates buying and selling pressure by calculating separate bull and bear series, smoothing both, and plotting the absolute difference as a histogram. Its calculation can use RSI-style price changes, a stochastic range…
The document proposes a stock screen for companies in the metaverse industry. It selects stocks whose opening price is near the 10-day moving average and whose latest close is above the previous close. It gives two implementations: a formula using industry…
The document describes a trading approach that combines an adaptive moving average (AMA) with Kaufman’s adaptive moving average (KAMA). It says the AMA builds on KAMA by accounting for where the close falls within the high–low range. The proposed system uses…