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.
16,761 documents
This report outlines a method for rotating among equity style factors as the macroeconomic environment changes. It proposes two signals: identify macro events historically associated with stronger performance in particular styles, and compare current macro…
The document describes one Expert Advisor that runs three independent trading systems based on ColorJFatl_Digit indicator color changes. It generates signals when a bar closes and an indicator changes color, treating that as a trend change. Each system has…
This MetaTrader expert advisor uses the Blau Ergodic MDI oscillator to generate trades. The described entry choices include reacting to a histogram direction change, a zero-line crossover, or a change in the signal cloud’s color. A configurable time window…
The document sketches a simple breakout trading example based on candlesticks alone, without technical indicators. It refers to a buy entry shown in an image and says the sell entry would reverse that setup. The supplied text does not include the image, so…
This report tests thirteen momentum and modified-momentum signals in China’s A-share market. The signals include returns over several lookback periods, turnover-weighted returns, exponentially weighted turnover-adjusted returns, and a long-horizon regression…
This account describes extending a backtest of a Chinese small-cap strategy and finding that returns before 2024 were negative. It attributes the later high returns largely to growth in the Beijing Stock Exchange, whose smaller companies strongly affected a…
This research summary explores whether statistics computed from intraday stock data can add new signals to established multi-factor selection approaches in China’s A-share market. It evaluates realized volatility, realized skewness, and realized kurtosis as…
These release notes describe Hummingbot 1.20.0, centered on a V2 strategy framework for building modular strategies that can be backtested and deployed. The framework separates market data candles, controllers that coordinate strategy decisions, and…
This document presents a three-condition MACD setup intended to filter ordinary signal-line crossovers. For a buy signal, the MACD line first crosses below its signal line, then crosses below zero, and later crosses back above the signal line. For a sell…
The document describes an expert advisor that uses the ColorXDerivative indicator. It generates a signal when a bar closes and the oscillator changes direction, making the direction change the system’s stated trigger. The advisor depends on the compiled…
This short assignment defines quantitative investing as making trading decisions with data-supported methods instead of relying on intuition. It frames the approach as a way to turn an investment idea into explicit rules and encode those rules in a strategy,…
This configuration specifies a Qlib workflow for training a CatBoost model on Alpha158 features for the CSI 500 universe. The data spans 2008 through mid-2020, with training through 2014, validation during 2015–2016, and testing from 2017 onward. The model…
The document presents factor analysis as a core research step for multi-factor, index-enhancement, and market-neutral strategies. It frames the analyst’s work as evaluating candidate factors before strategy researchers use them, with the quality of that…
The report proposes refining the book-to-price (BP) valuation factor by splitting book value into retained earnings and invested capital. It forms two market-value ratios: REP for retained earnings and CCP for invested capital. The stated motivation is that…
This example configures a backtest for a mean-reversion strategy on an AUDUSD perpetual contract. It feeds quote data into a backtest engine, forms one-minute midpoint bars, and instantiates a strategy configured with Bollinger Bands and RSI. The listed…
The document describes an Expert Advisor that combines moving-average changes from hourly, four-hour, and daily charts. Each timeframe contributes three relative changes between recent moving-average values, weighted by adjustable parameters and rounded…
This document describes an Expert Advisor that trades changes in the FiboCandles indicator. It opens a trade when the indicator’s candle color switches between pink and green, with the signal evaluated at the bar close. The system depends on a compiled…
This tutorial compares a high-frequency grid market-making strategy across cryptocurrency exchanges, emphasizing that different order flows can change results even for the same trading pair and parameters. The strategy places layered limit bids and offers…
This Chinese equity screening idea combines a daily amplitude threshold above 1, excluding stocks marked ST, with a five-part limit-up method and at least one limit-up during the prior 25 days. It also says to select stocks before 10 a.m. The accompanying…
This post presents a daily DAX spread-bet strategy adapted from Larry Connors-style rules. It buys when the close is above its 200-day average, below its 5-day average, and the 2-period RSI is below 13. The long position exits when the close crosses above…
The document answers a strategy implementation question: how to prevent a stock from being bought again for a specified period after a prior trade. Its proposed approach is to record the relevant date when a position is sold, then compare that stored date…
This post proposes screening Chinese stocks associated with the metaverse theme using a long-term price trend condition and an activity measure combining the prior session’s turnover rate with the current opening auction volume relative to prior volume. It…
This forum post describes a BigQuant backtest that raises a pandas KeyError for the column named buy_signal. The strategy code queries daily stock data and creates a factor field, then the trading handler filters context data using buy_signal equal to one.…
This document describes an Expert Advisor that predicts a future price from a sequence of past prices using linear prediction. Burg’s method estimates model coefficients by reducing mean-square prediction error over a training window. The strategy’s…