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 document answers how to allocate weights across strategies in a multi-strategy backtest. Its proposed workflow is to extract each strategy’s daily return series and use an optimization package to find portfolio weights. This frames the task as portfolio…
The document describes a basic workflow for evaluating a trained quantitative model. After fitting the model on training data, apply it to a validation set, then compare its predictions with the observed values to assess performance. This separates model…
This research summary explains how to build a machine-learning stock-selection process using historical factor values to predict subsequent returns. In the training stage, a supervised model learns the relationship between inputs and returns; in the testing…
This brief coding question outlines a way to calculate fund performance statistics from a price series. It first derives periodic returns from price changes, then uses a performance-analysis library to compute cumulative return, annualized return, Sharpe…
The document summarizes CapTE, a model for predicting stock movements from social media text. A Transformer encoder extracts semantic features from posts, while a capsule network is used to represent structural relationships in the text. The approach is…
This short platform discussion explains that an adjust factor is used to convert a stock’s real price into an adjusted price. Adjusted prices, including forward- and backward-adjusted series, are intended to keep price charts continuous across corporate…
This short forum exchange explains how to configure BigQuant’s trading engine to rebalance on a weekly or monthly schedule. For weekly scheduling, it specifies the weekly trading-day mode and a day value of 5; for monthly scheduling, it specifies the monthly…
The document summarizes a study that develops a probabilistic classifier to identify high-frequency trading activity from intraday order data. Using French BEDOFIH market records, the researchers engineered features describing orders, including their prices,…
The document summary highlights two applications of machine learning in quantitative investing. First, it describes forecasting volatility to inform how capital is allocated among strategies, based on the claim that many strategies’ profitability is closely…
This sample describes a high-dividend stock-selection model for Chinese equities. The process excludes special-treatment stocks, suspended securities, and Beijing Stock Exchange listings. It then screens for larger companies by market-capitalization rank,…
This forum post reports a suspected data-quality problem in a Chinese stock valuation dataset. The author observed that the September 14, 2022 snapshot appeared to contain more than 1,600 missing or erroneous records, while the adjacent dates seemed to have…
This report surveys several approaches to allocating across asset classes: macro and cycle-based fundamentals, mean-variance optimization, Kelly-CVaR, Black-Litterman, and risk parity. It describes a macro model that separates directional forecasts from…
This tutorial shows how to implement a collection of Chinese stock features and screening rules in BigQuant AIStudio 3.0. It divides them into expression features and expression filters, then explains that the same calculations can be entered as a SQL query.…
This Chinese-language research digest summarizes two separate topics. The first reviews the United States target-date fund market, covering market share and flows, relative performance among fund series, and glide paths. It discusses glide-path averages and…
This report describes a Chinese equity index-enhancement strategy built from a composite stock-selection signal and portfolio constraints. It combines factors spanning company size, valuation, growth, profitability, technical behavior, liquidity, and…
This research report describes a Chinese equity fund approach that first selects industries through fundamental analysis, then applies a multi-factor model to stocks within those industries. Industry research estimates long-term growth across more granular…
This research note reviews the growth and allocation case for quantitative funds in China, focusing on index enhancement and equity long-short strategies. It reports that in the first half of 2021, CSI 500 enhancement strategies outperformed selected active…
This document introduces mobile network activity as an alternative data source for quantitative investing. It explains that mobile devices continually exchange signals with cell towers and Wi-Fi access points, and that legally anonymized records may reveal…
This beginner tutorial uses the MNIST handwritten digit dataset to introduce TensorFlow through a simple image classification task. Each image has a digit label, and the model is intended to predict that label from the image. The tutorial chooses softmax…
This stock screen combines three conditions: daily price range above 1%, closing price below 20, and more than two limit-up sessions in the preceding ten days. The document provides example implementations in a Chinese stock analysis formula language and…
This article challenges three barriers commonly associated with quantitative investing: needing advanced mathematical credentials, being able to code extensively, and having a large portfolio. It presents quantitative analysis as a way to use statistics and…
This guide explains simple and exponential moving averages as ways to smooth price series. An SMA averages prices over a selected window, while an EMA updates recursively and gives more weight to recent prices. It illustrates both calculations with a short…
This report examines three connected areas of China’s technology sector: 5G communications, artificial intelligence, and semiconductor chips. It presents 5G as infrastructure for faster data transfer and connected devices, AI as an application area that…
The document describes a convertible-bond setup that enters after a V-shaped recovery when price rises through the left shoulder of the pattern, above its right shoulder. The right shoulder must be at least 3.5 points above the V’s low. The trader then uses…