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.
5,018 documents
This Chinese A-share example builds a daily stock-ranking strategy using LightGBM regression. Its features combine market capitalization, recent price and turnover averages, dividend yield and price-to-earnings ranks, plus two custom factors. The target is a…
This guide explains how to connect OctoBot’s GPT interface to a language model for trading evaluations. For OpenAI, it describes adding an API key in the interface configuration, enabling the GPTEvaluator, and choosing a model through evaluator settings. It…
This research note describes two revisions to AlphaNet, a neural model that learns stock selection factors from raw price and volume data. Version two adds ratio features, replaces pooling and dense layers with an LSTM to capture temporal patterns, and gives…
This meetup page collects questions about quantitative trading on the BigQuant platform. Topics include searching for holding-period parameters in a default stock-ranking template, defining reusable Python modules, and building a workflow for developing…
This configuration describes a Qlib machine-learning workflow that trains a CatBoost regression model on Alpha158 features for CSI 300 instruments. It defines separate training, validation, and test periods, then records signal analysis and portfolio…
This article proposes a defensive equity strategy that seeks oversold rebounds or bounces after a pullback. It draws inspiration from research on money-flow factors, including inflow, outflow, net institutional flow, and opening net flow, and proposes…
This overview explains the main stages of a machine-learning workflow for quantitative investing, using a fruit-selection analogy to introduce training data, labels, features, prediction, and validation. It recommends defining the market and stock universe,…
The report describes a stock-selection strategy that predicts the future usefulness of seven style factors and adjusts their portfolio weights over time. It uses historical factor information coefficients (ICs), macroeconomic variables, and market variables…
This Chinese-language support exchange addresses a quantitative research notebook that restarts automatically after two features are added and feature extraction begins. The user reports that the visible CPU and memory figures have not reached their…
This indicator converts RSI behavior into eight normalized features, including level, slope, acceleration, percentile, volatility, fast-versus-slow spread, and regime. It stores sampled feature vectors alongside forward price outcomes grouped into ATR-scaled…
This article collects learning materials for applying machine learning to algorithmic trading, grouped into books, blogs, research papers, videos, and podcasts. The topics span neural networks, structured data, regression, clustering, nearest-neighbor…
This release overview describes VeighNa 4.0 and its new vnpy.alpha module for developing machine-learning, multi-factor strategies. The module is organized around feature datasets, model training, strategy research, workflow management, and example…
The article compares long and short training windows for an AI stock selection strategy and recommends evaluating each window against a fixed validation period. In its rolling experiments, extending the sample from 2005 to 2021 changed labels, factor…
This Chinese post compiles a selection of 65 titles from a much larger collection of publicly released Springer books, focusing on data and machine learning. The bibliography spans foundations such as algebra, probability, statistics, optimization, and time…
This BigQuant framework describes a workflow for computing and evaluating minute-frequency stock factors. Researchers define factors in SQL against a specialized derived minute-bar table, assign an output table name, and run the program to calculate and…
This document is a brief outline of a presentation on machine learning in finance. It names four application areas: Lasso regression for commodity futures price prediction, decision trees for detecting possible financial fraud, logistic regression for…
This tutorial offers practical advice for getting more useful answers from the SuperMind assistant when asking trading and coding questions. It recommends stating the task clearly, providing relevant data formats and context, using precise terminology, and…
The article surveys the development of quantitative trading through stories about Jules Regnault, Edward Thorp, and James Simons. It describes using historical price data and mathematical models to identify market patterns, Thorp’s probability-based…
This Chinese-language conference excerpt introduces how artificial intelligence is being adopted by global asset managers. It frames technology as one response to falling margins per unit of managed assets, alongside efforts to grow assets under management.…
This brief BigQuant support exchange concerns an error triggered after a user changed features in a beginner template. The response identifies a formatting issue in the feature list: comments or notes should be placed on separate lines rather than appended…
The document describes adding a custom Rank Information Coefficient module to a stock-ranking strategy. The module reports average RankIC separately for the training set and the test set, providing a way to assess how well a model’s rankings align with…
The document contrasts OpenAlice, presented as an AI agent for researching and managing trades across a full lifecycle, with LumiBot, a Python framework for building trading strategies. LumiBot can support deterministic strategies, individual AI agents, or…
This strategy-sharing article describes an enhanced China Securities 150 equity approach that blends model-based stock ranking with technical timing. The universe is manually narrowed to roughly 100–300 large, liquid constituent-style stocks. An AI model…
This study tests whether machine learning can explain stock returns left unexplained by a conventional linear equity factor model. It uses 22 style factor exposures to predict standardized stock specific returns, then evaluates boosted trees, random forests,…