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 community post describes a restart issue in a portfolio strategy. A position and target are saved when the system shuts down, then restored during startup; however, historical bars are replayed through the strategy’s bar handler during initialization.…
This strategy rebalances on the first trading day of each month, removes special-treatment stocks and shares listed on the STAR Market or Beijing Stock Exchange, then invests equally in the 30 stocks with the smallest market capitalizations. It has no…
This method uses principal component analysis to separate broad equity return drivers from stock-specific residuals, then trades residual portfolios expected to revert toward equilibrium. Returns are standardized before estimating their correlation matrix;…
The article outlines market, liquidity, credit, operational, and systemic risks in cryptocurrency trading, then presents four basic controls: position sizing, risk/reward assessment, stop-loss and take-profit orders, and diversification across tokens. It…
The guide distinguishes OctoBot’s historical portfolio profitability from its historical profit and loss (P&L). Portfolio profitability values configured tradable assets in a selected reference market, then compares the total value of current holdings with…
This report examines the small-cap premium in China’s A-share market and a model for timing shifts between small- and large-cap stocks. It measures the premium through small-cap stocks’ monthly excess return over large caps, and reports that the historical…
This overview surveys ways institutions can seek yield in decentralized finance, including lending, liquidity provision, staking, and yield farming. It outlines the main exposures associated with each: smart-contract and liquidation risks in lending,…
This research summary presents a framework for replicating the returns of an index, fund, or individual stock with a selected set of holdings. It first applies LASSO regression to choose securities that capture the target portfolio’s characteristics, then…
The document introduces Smart Beta as index investing that uses rules other than market-cap weighting to increase exposure to selected risk factors. It presents the approach as a middle ground between active and passive investing, with the potential to build…
This study note explains how to build a directed stock network from news coverage: a stock named in a headline is treated as a lead, while stocks mentioned in the article body are followers. Edge weights count their co-occurrences over a rolling window. The…
This survey reports on U.S. crypto holders’ intentions, confidence, preferences, and portfolio changes. It says 88% planned to continue investing over the next 12 months, compared with 73% in the prior-year survey, and 70% reported greater confidence.…
This strategy selects Chinese convertible bonds nearing maturity, aiming to combine a price discount to redemption value with screening for issuer credit risk. It excludes bonds linked to special-treatment stocks, requires positive trailing earnings and…
This brief response defines quantitative investing as using tools to extract patterns from historical data. It frames time-series analysis as a way to improve the odds of making a successful decision and cross-sectional analysis as a way to seek better…
This research summary explains how alpha factors can be represented by factor-mimicking portfolios (FMPs) under a stock covariance model, and how linear combinations of factors correspond to combinations of their mimicking portfolios. It frames factor…
This research report studies portfolio construction around a weekly stock-selection model called AlphaNet, built from price and volume data. It examines several portfolio optimization approaches intended to accommodate different risk and return objectives.…
This article explains a contrarian rebalancing strategy that keeps a Bitcoin position and cash at roughly equal market values. It begins with the broader stock-and-bond allocation idea, then applies it to a BTC account. When the asset values diverge beyond a…
This example describes an AI trading team modeled on concentrated investing. A quality researcher selects a high-quality large-cap company, an activist bull develops the case for catalysts and value creation, and a short-seller challenges the thesis on…
The forum exchange discusses a limitation in VeighNa’s graphical backtesting interface. A user notes that CTA strategies can be tested through the GUI, while other strategy types, including multi-contract strategies, are run from code in Jupyter. They ask…
The document outlines a training program on cross-sectional multi-factor strategies, also described as alpha strategies. Its curriculum spans factor data preparation, supervised learning, model evaluation and interpretation, portfolio construction, and…
This strategy rotates among a fixed pool of 11 ETFs spanning several asset classes and markets. Every five trading days, it ranks candidates using a 25-day linear-regression slope multiplied by R-squared, then adds a short-term signal based on the five-day…
This French-language guide explains how to run several automated crypto strategies or asset baskets through OctoBot Cloud. Its key operating rule is to assign only one strategy or basket to each exchange portfolio, which is intended to prevent strategies…
This Chinese research note proposes using capital flow ratios within style-factor portfolios to guide style rotation. It argues that raw inflow and outflow totals mostly move alongside prices, while flows measured against trading value or by…
This Qlib workflow configuration specifies a linear ordinary least squares model using the Alpha158 feature handler for the CSI 300 universe. The data span begins in 2008 and ends in 2020; the training segment runs through 2014, validation covers 2015–2016,…
The document translates David Polen’s bottom-up investment principles into a quantitative stock-selection approach. The original framework emphasizes financially strong companies, ample free cash flow, earnings momentum, valuation based on expected returns…