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.
3,481 documents
The submission contrasts discretionary and quantitative investing. It describes quantitative work as gathering and cleaning high-dimensional data, calculating factors, and using rules to make trades programmatically. It also suggests that machine learning…
The article challenges three barriers often thought to exclude individual investors from quantitative trading: advanced mathematics, coding skills, and large capital. It presents quant methods primarily as statistical tools for understanding markets, and…
The article summarizes research using Forcerank, a platform where participants rank stocks by expected performance over roughly a week. Regressions of consensus ranks on past returns show that participants extrapolate recent performance, with more weight on…
This sector-rotation study uses the absorption ratio to measure how concentrated the risks embedded in a group of asset returns are. The ratio reflects the share of total return variation explained by a small set of orthogonal components: higher readings are…
This research summary examines whether hedge funds’ exposure to changes in investor sentiment predicts subsequent performance. It estimates each fund’s sentiment beta with a rolling 36-month window, forms equal-weighted portfolios by beta, and uses…
This 2018 report reviews managed futures, including how CTA strategies trade futures and options and how they differ by analysis method, trading style, holding period, and markets covered. It describes systematic and discretionary approaches alongside trend…
The document describes a high-risk, high-turnover strategy for Chinese stocks under the T+1 trading convention. It looks for strong stocks that pull back and rebound, possible second moves in leading stocks, and sentiment-driven stocks that may reverse after…
This article outlines an asset allocation method that combines risk parity with the Black-Litterman framework. Risk parity portfolio weights serve as prior weights, which are used to infer prior expected returns. Short-term momentum views are then…
This article explains how to model multi-stock trading as a Markov decision process and train a deep deterministic policy gradient (DDPG) agent with FinRL. The state includes prices, holdings, and cash; actions change holdings through buying, selling, or…
This weekly review summarizes Chinese equity market conditions for the week ending April 3, 2020, then presents Haitong Securities’ market timing, factor, industry rotation, and strategy-performance views. Its evidence includes index returns, valuation and…
This brief BigQuant forum exchange concerns errors in a factor-analysis template when a factor’s coverage falls below the platform’s threshold. One concrete cause is division by zero: the expression using the difference between the high and close prices can…
This essay groups purportedly successful stock market participants into six styles: patient traders who wait for rare setups, long-term value investors, specialists focused on one stock, short-term leaders-of-the-hot-sector traders, traders who repeat a…
The document summarizes a portfolio construction method that translates target macroeconomic factor exposures into asset weights. It considers six factors: equity, real interest rates, credit, inflation, emerging markets, and commodities. A standard linear…
This support note explains how to access trade details from a BigQuant backtest. It says the trade detail page does not offer a direct export, and recommends downloading the results for separate analysis. It also points users to the backtest module’s raw…
The post compares a hand-built 120-period Commodity Channel Index calculation with the platform’s built-in CCI function and asks why their results differ. It identifies the typical price as the average of the high, low, and close, then describes a formula…
This BigQuant forum post describes a custom Python module that runs successfully in AIStudio 3.0 but fails when submitted as a scheduled data task. The example reads the latest stored date, loads a local CSV of Bitfinex symbols, requests newer daily market…
The post reports a BigQuant backtest failure in which the data extraction stage returned zero rows, and the backtest then raised an IndexError while constructing its rebalance schedule. The logs show the sequence from feature processing to an empty extracted…
This brief BigQuant community post reflects on the challenge of improving a strategy’s Sharpe ratio while also controlling drawdowns. The author’s remarks suggest that judging a strategy by Sharpe alone can overlook the difficulty of managing losses. They…
This Chinese research summary describes an equity index-enhancement approach using listed companies’ reported revenue by business line. It uses annual and interim report data, then applies Chinese-language keyword extraction and similarity analysis to…
The article presents quantitative investing as a way to replace discretionary buy and sell decisions with signals from systematic models. It attributes common retail timing mistakes to fear, greed, and reactions to market sentiment, and says rules-based…
This research summary presents TS-Boost, an equity factor-selection framework designed for financial data with shifting cross-sectional patterns and low signal-to-noise ratios. Instead of pooling observations from different dates into one training set, it…
This discussion describes a model-stacking problem in a Chinese quantitative research platform. A user has trained and persisted twenty StockRanker models using the same factor set but differing in date ranges, data filters, or prediction horizons. The goal…
The document describes a daily classification approach for detecting positive momentum, negative momentum, or normal conditions in Bitcoin, Ethereum, and Litecoin. It uses historical prices and technical indicators as features, then compares classifiers…
This report describes a process for choosing equity factors and turning them into portfolios. It first narrows a larger set of candidate factors by weighing their correlations, volatility, cumulative returns, Sharpe ratios, and economic rationale. It then…