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
This opinion piece challenges the idea that quantitative trading is uniformly harmful to retail investors. It contrasts high-frequency approaches, which it says may use speed and securities lending to affect momentum-driven trades, with lower-frequency…
This strategy forms a quality score from four accounting measures: accruals, cash flow relative to earnings, debt relative to assets, and return on equity. It favors firms with lower accruals and leverage and higher cash flow and profitability. Stocks are…
The article describes a risk-model framework for evaluating fund managers beyond headline returns, Sharpe ratio, or drawdown. It decomposes active returns into factor and stock-specific contributions, and examines risk contributions from a fund’s style…
This overview proposes using machine learning to support value investing by combining financial statements, market data, and macroeconomic information. It lists a broad set of candidate measures, spanning performance and risk statistics, valuation and…
A forum participant asks whether a CTA approach fits trading futures and options from indicators while seeking to hedge positions. The response points to a portfolio strategy module for developing strategies that trade multiple contracts. This suggests…
This Chinese-language research report outlines an equity index evaluation framework based on Barra-style risk factors. It proposes decomposing index returns into market, industry, and style contributions, including size, valuation, and volatility, then using…
This research summary describes a multi-factor tracking framework built around the pricing logic of factors. It examines historical returns for technical, expectation-based, and financial factors using a modified cross-sectional regression approach. To…
This article explains the All Weather approach as a way to build a portfolio without relying on precise economic-cycle forecasts. It frames the macro environment along growth and inflation dimensions, then describes matching stocks, bonds, commodities, gold,…
The article explains diversification as spreading cryptocurrency exposure across tokens to reduce the damage from a weak or failed holding. It proposes grouping assets by industrial use case, ecosystem solutions such as protocols and service providers,…
The document explains how to set custom trading intervals in a trading engine by using a trading calendar. It points to two examples: rebalancing on Mondays and rebalancing at the start of each month. These schedules let a strategy follow calendar-based…
This configuration defines a Qlib workflow for ranking CSI 500 equities with a LightGBM model and Alpha360 features. It uses cross-sectional rank normalization for labels and trains on data from 2008 through 2014, validates on 2015–2016, and evaluates a…
This guide defines a black swan as an outlier that is difficult to anticipate, has a large impact, and is often explained as foreseeable after it occurs. It applies that framework to crypto, where hacks, software flaws, fraud, regulation, or failures at…
This Qlib configuration defines a Chinese equity research workflow using the CSI 300 universe and Alpha158 features. It trains a double ensemble of gradient-boosted models, with both sample reweighting and feature selection enabled, then evaluates signals…
This summary of a research report examines the Chinese New Year calendar effect in mainland Chinese equities and how it relates to common quantitative factors. It reports that the broad market historically had an unusually favorable risk-adjusted profile…
This document explains how MetaTrader 5 strategy tester cache files for individual test runs can be read, saved, and inspected without rerunning the tester. Example scripts convert real account trading history into a tester-compatible file and retrieve the…
The paper proposes a decision-focused learning framework for prediction followed by optimization when the downstream objective includes linear soft constraints. Such constraints penalize violations with a maximum function, which is nondifferentiable at its…
This research digest summarizes two finance topics. The first reviews competing claims about the size effect, the long-discussed idea that smaller companies have higher average returns than larger ones. It says the underlying study uses simple tests and…
This report examines domestic Chinese fund products that provide exposure to Hong Kong equities and their role in broader asset allocation. It reviews access routes and fund types, including actively managed products and index funds, then compares the…
The document reviews thematic funds, which target long-term narratives such as artificial intelligence or alternative energy. It describes a surge in US thematic-fund assets and inflows through early 2021, then frames investment as a difficult selection…
The document outlines how sequence learning can fit into a conventional quantitative investment process: feature extraction, individual asset return prediction, portfolio construction, and trade execution. It describes using price and volume measures,…
This weekly factor report describes two forms of diagnostics for the ten highest-ranked anonymous ModelScore factors: average style exposures estimated through daily multivariate regressions, and average industry-level Rank ICs measuring the cross-sectional…
The document outlines a daily stock rotation schedule: buy ten stocks at the open, hold each cohort for five days, and sell it at the close on its fifth day. It allocates capital in five equal portions, investing one portion in each day’s new cohort. From…
This MetaTrader 5 utility analyzes closed trading history to show how reported performance was formed. Alongside net and gross results, profit factor, win rate, and average and median trade, it examines reliance on the best trades, sensitivity to removing…
This document outlines a machine learning workflow built around feature engineering, model training, and model combination. Feature engineering includes creating, extracting, and selecting variables. It contrasts linear models, which capture linear…