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 market commentary interprets crypto options conditions ahead of the U.S. election, focusing on Bitcoin and Ethereum realized volatility, implied volatility, skew, term structure, and relative performance. It describes realized volatility as stable while…
The article reviews research on the 2016 launch of Morningstar’s sustainability ratings for U.S. mutual funds. The ratings use portfolio holdings and company ESG scores to assign funds one to five stars within their categories; the star display appears more…
This guide recommends choosing community trading strategies according to an investor's capital, return expectations, and tolerance for drawdown. It distinguishes a conservative profile, associated with diversified holdings, a smoother equity curve, a longer…
The Deviation Oscillator is presented as a volatility-oriented indicator that measures the distance between an applied price and its moving average, then scales that distance using the minimum and maximum deviations observed over a selected range. The result…
This post outlines a Chinese equity screen combining a daily range above 1%, dates in 2021, and a current low below the prior day's low. Its final stated selection also adds valuation filters: a price-to-earnings ratio below 30 and a price-to-book ratio…
This note describes a simple technical screen for Chinese stocks: select observations from 2021 where the daily high-low range exceeds 1% and the current high equals the highest high over the current and prior day. The author frames a large range as a sign…
The article outlines an A-share stock selection screen combining turnover, company size, profitability, and past price-limit events. It selects shares with turnover between 3% and 12%, market capitalization below 10 billion yuan, no reported loss, and at…
The post presents a chart indicator that uses the highest high and lowest low over a configurable lookback, excluding the current bar, to plot proposed long and short stop-loss and take-profit levels. Its stop logic compares the distance from the current…
The document describes a terminal script for exporting account history into a CSV trading journal. It selects deals from a user-chosen lookback period and groups them by position ID to reconstruct closed positions, including on netting and hedging accounts.…
This post combines a stock universe selected through an intelligent query with a dual moving average rule. The proposed rule buys when the five-day average is above the twenty-day average and sells when it falls below, while the universe is screened for low…
This indicator description explains a ZigZag variant that calculates turning points from the high and low values of Heikin Ashi candles. The Heikin Ashi period is configurable, and users can choose whether the Heikin Ashi candles themselves are displayed.…
This research summary examines whether sell-side analysts’ earnings forecasts change as they issue more forecasts during a day. It frames decision fatigue as a shift from deliberate reasoning toward faster, more heuristic judgments. Using analyst forecast…
This post describes a Chinese equity screen combining daily price amplitude, dividend yield, and a large-order net-flow ranking. It presents the factors as signals for volatility, company profitability, and market interest, then shows example implementations…
This document describes an expert advisor that trades around Bollinger Bands. Its stated rule is contrarian: a downward break below the lower band triggers a buy, while a break above the upper band triggers a sell. An update says entries are placed after a…
This article examines whether financial factors such as value, size, profitability, investment, and momentum can be timed using predictive signals. It groups candidate signals into financial conditions, economic conditions, investor sentiment, factor…
This expert advisor uses a Doji candle as a setup and waits for the next completed candle to confirm a directional break. When a new bar appears, it checks the two preceding bars: if the older candle qualifies as a Doji, a close of the newer candle above the…
The post proposes a stock screen that combines a recent increase in institutional buying, weekly MACD above its zero line, and a high historical dividend payout ratio. It interprets the buying measure as evidence of institutional interest, weekly MACD as a…
This overview surveys recurring problems in quantitative trading, including unreliable or incomplete data, model risk, slippage and fees, parameter selection, real-time monitoring, and processing speed. It also discusses choosing machine-learning methods and…
This document describes a daily Chinese equity screen combining three conditions: turnover between 3% and 12%, the product of price change and net large-order volume above zero, and MACD above zero. It presents the rule as a way to select stocks with…
This A-share stock screen combines a 14-period RSI below 65 with a positive prior-day main-fund net inflow. The stated rationale is that a relatively lower RSI may indicate rebound potential, while the fund-flow condition adds a capital-flow filter. The…
This documentation explains design choices behind TqSdk, a Python trading software development kit. It aims to avoid imposing a strategy model: users can fetch data and issue orders freely, while examples demonstrate possible applications instead of…
This trend-following expert advisor uses the slope of a simple moving average, expressed as an angle, to distinguish directional markets from flat conditions. It also measures price distance from the average as a percentage to avoid entries after a move has…
This Chinese equity screening idea combines three conditions: the 20-day moving average must exceed the 120-day moving average, the stock must have recorded at least two limit-up sessions in the prior 500 days, and its volume ratio must rank among the top…
The report describes an asset allocation approach that measures cycle states and uses machine learning to estimate the probability that assets will outperform one another. It reviews macroeconomic timing frameworks, then describes cycle factors derived from…