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
This July 2020 market commentary assesses whether a brief cooling in China’s hot A-share market signaled an end to the rally. It frames the issue as a balance between incoming capital and potential selling, identifying new fund launches, northbound foreign…
The article presents an “A” shaped price drop after a quiet base and a sharp, high-volume rise as a possible shakeout before a further advance. It claims the decline may reflect coordinated institutional accumulation and quantitative short selling, rather…
This research report develops a low-lag trendline (LLT) using a second-order low-pass filter. It frames the design as a response to the smoothing-versus-delay trade-off in simple and exponential moving averages: stronger smoothing can make conventional…
The discussion addresses how to search for effective weights when combining two stock-ranking factors. It proposes using BigQuant’s parallel tuning module, m.tune, rather than treating each weight as a separately exposed parameter. The key implementation…
This article introduces Transformer attention and adapts the architecture to stock-factor time series. Its proposed model adds Time2Vec representations to factor inputs so the network can represent both periodic and non-periodic time patterns. Self-attention…
This talk describes how a Chinese quantitative investment firm approaches team design, hiring, researcher autonomy, collaboration, and leadership. Its central claim is that a research organization should support strategy development with data, computing, and…
This literature summary reports research on investment strategies in Chinese commodity futures and notes that long-only approaches produced limited economic returns. Among twelve long-short strategies examined, momentum and term-structure strategies showed…
This brief BigQuant community exchange explains two platform variables used in quantitative trading workflows. It describes context as the interface for accessing objects in the backtesting modules, with the available methods depending on the module. It…
The document is a brief summary of a research report on constructing a manager-of-managers portfolio. It names three stages: selecting an asset-allocation approach, evaluating investment managers, and managing the portfolio dynamically over time. It also…
This example shows a basic workflow for comparing portfolio value paths across several strategy weights. It sets a date range and starting value, generates daily returns from a normal distribution whose mean scales with the selected weight, compounds those…
The document outlines a way to construct daily, cross-sectional high-frequency equity factors. It proposes ranking stocks by their volume as a percentage across the market, then calculating the cross-sectional variance, skewness, and kurtosis of those ranks.…
This strategy note outlines a Chinese equity model that ranks stocks using three cross-sectional features: market capitalization, turnover, and five-day price change. Each feature is converted to a daily percentile rank. A shallow XGBoost regressor is…
This brief discussion proposes a feedback loop for a short-horizon stock strategy: observe the result of a trade, such as a buy today and sell tomorrow, then use the realized daily return to adjust strategy parameters. The intended outcome is a model that…
This guide describes operational practices for receiving foreign-exchange prices for research, backtesting, and live systems. It recommends a WebSocket push connection instead of frequent polling for actively used currency pairs, retrying after temporary…
This strategy combines a qualitative earnings-surprise screen with the stock’s price gap after an earnings announcement. It identifies surprise candidates by analyzing analyst report titles for language suggesting results exceeded expectations, rather than…
This Chinese-language note points to a reproduction of a China International Capital Corporation research report on using intraday trading data to give an early warning of crowded institutional trading. It directs readers to a code-share implementation and…
This report compares artificial intelligence stock-selection portfolios across five Chinese equity universes and benchmark or sector-neutral setups. The models include support vector machines, random forests, naive Bayes, and logistic regression. It reports…
This overview explains how traders can translate technical rules into algorithms that monitor markets and place or manage orders. It describes automation’s practical uses: tracking multiple assets and indicators, high-frequency trading, cross-market…
The post records a China A-share backtest using market capitalization ranks to select smaller companies, with filters for trading activity, positive trailing earnings, and positive price-to-earnings ratios. It reports that increasing the portfolio from 10 to…
The article reviews a research method that assigns firms to startup, growth, maturity, shakeout, or decline stages using the signs of operating, investing, and financing cash flows. The three cash flow signs create eight possible patterns, which the method…
This research summary compares machine-learning stock-selection models trained on conventional return labels with models trained on alternative labels based on information ratio and Calmar ratio. The rationale is that these labels capture more of the path…
This research review examines whether education ties among Chinese hedge fund managers are associated with fund performance. It builds time-varying networks in which funds are connected when their managers share a school and a similar field of study, then…
The document describes a method for converting company-level factor views into industry-level indicators by aggregating the factors of each industry’s constituent stocks. It reports that mapped measures based on changes in asset turnover and leverage,…
The document proposes combining two stock-ranking models in a stock-picking strategy: one predicts stocks likely to rise, and another predicts stocks likely to fall. The intended process is to use the downside model to remove candidates flagged as likely…