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.
5,922 documents
The article describes an unsupervised approach to identifying market regimes from the returns of 17 US factors beginning in 1970. A Gaussian mixture model clusters observations by their return distributions; cross-validation is used to choose four clusters.…
The overview presents FinRL-Meta as a framework for data-driven reinforcement learning in finance. It separates the workflow into data, market-environment, and agent layers, with interfaces that allow components to be replaced or customized. It also…
The document explains how to estimate asset and portfolio risk from historical returns. It describes calculating average returns, variance, and standard deviation, with standard deviation expressing return dispersion in the same units as returns. It then…
The article presents a framework for explaining changes in trend-following performance through three components: the size of risk-adjusted market trends, the strategy’s efficiency in converting those trends into returns, and the diversification benefit…
This research summary describes stock-selection factors built from Chinese public mutual-fund holdings. Because quarterly reports disclose only the largest positions and holdings data arrive infrequently, the study fills out recent quarterly holdings using…
The document presents factor-category weighting as a simple neural-network problem. Gradient-based optimization can learn both the weights within categories and the weights across categories from cross-sectional observations. Under certain conditions,…
This report analyzes Chinese active equity funds using Fama-French and Treynor-Mazuy models to separate market timing, stock selection, and style allocation. It reports that positive stock-selection ability was more common than persistent market-timing…
The article introduces factor investing as portfolio construction based on characteristics associated with differences in risk and return. It outlines the development from the CAPM to the Fama–French three-factor and five-factor models, then distinguishes…
This Qlib example demonstrates an end-to-end simulation of rolling online model workflows. It initializes Chinese-market data and experiment settings, generates rolling tasks at a configurable step, and connects those tasks to an online manager and a…
This document summarizes a commodity futures strategy based on carry and the futures term structure. It describes a directional rule in which the nearby, or main, contract is bought when its price is below the next contract and sold when it is above it. The…
This overview describes a commodity futures CTA strategy that ranks contracts using a combined score. It considers technical signals—cross-sectional momentum, volatility, open-interest changes, and term structure—alongside macroeconomic measures including…
The environment layer in FinRL-Meta uses cleaned data to create market simulations with a shared, Gym-style interface. Users can build on these environments and compare strategies across a common framework. The document describes account options for margin…
This excerpt summarizes a two-week review of fundamental quantitative sector allocation and ETF rotation recommendations in China. It compares recommended overweight, benchmark-weight, and underweight industries with the broad Wind All A index, reporting…
This report examines fund advisory services in China, including how third-party fund platforms work with licensed asset managers and how advisory services may evolve. It describes the platforms mainly as places where advisory strategies are presented, with…
This report compares ways to measure similarity among funds, including cosine similarity, Euclidean distance, and return correlations. It argues that the choice of measure should follow the intended meaning of “similar”: resemblance in holdings structure,…
This report compares ways to measure similarity among funds, including cosine similarity, Euclidean distance, and return correlations. It argues that the choice of measure should follow the intended meaning of “similar”: resemblance in holdings structure,…
The document introduces factor research as a way to study characteristics that may help explain the returns and risks of financial assets. It names market capitalization, quality, momentum, low volatility, and yield as examples of factors that researchers…
This document describes methods for selecting long-short portfolios that aim to mean-revert while holding only a small subset of assets. Sparsity can reduce trading costs and make portfolio exposures easier to interpret than dense portfolios that include the…
The article describes an on-chain method for examining which other tokens are held by wallets that own ENA. It starts with a list of ENA holders, retrieves their other token balances, filters likely spam tokens, and transforms the data into a wallet-by-token…
This library is designed to queue repeated MT5 Expert Advisor tests across combinations of symbols, timeframes, and date ranges. It supports both individual runs and optimization, including forward testing. The stated research uses include comparing a…
This research note outlines an extension to traditional risk parity. Standard risk parity assigns equal contributions to portfolio volatility, but this approach accounts for only variance and can leave exposure to skewness and kurtosis uneven. Those higher…
The document argues that grid trading works best in assets that fluctuate actively within a range, while persistent declines can leave a strategy holding losing positions with few ways to recover. It proposes screening for business or asset stability,…
This Chinese-language forum exchange distinguishes between optimizing a strategy separately for each instrument and optimizing it as a combined portfolio. One reply suggests looping over instrument symbols, initializing the backtest within each iteration,…
This example runs two existing futures strategies independently, using separate instruments, date ranges, trading costs, contract sizes, and capital settings. It then adds their result data frames, removes missing rows, and passes the combined data to a…