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 article explains several ways to limit the effect of extreme observations in financial data, where outliers can distort derived features and model training. It covers percentile clipping, which caps values outside chosen quantiles, and a…
This article explains random forests through CART decision trees, Gini impurity, bootstrap sampling, and aggregation. Trees are trained on resampled data, while their predictions are combined by voting for classification or averaging for regression. It also…
This short listing describes an expert advisor whose trading approach is based on a custom ZeroLag MACD indicator. It identifies the idea’s originator and the person who implemented the MQL5 code, but does not explain the indicator settings, signal…
This research summary describes a method for forecasting year-over-year inventory growth across six products in China’s ferrous commodity chain. It treats inventory as an indicator of relative supply and demand, while noting that true inventory is difficult…
The document introduces Bayes’ theorem as a way to update the probability of an event after receiving new evidence. It defines the prior probability, likelihood, marginal probability, and posterior probability, and gives the formula relating them. It also…
This document describes a Chinese stock strategy that combines classification and regression models to forecast intraday returns. It uses XGBoost and an ensemble of machine learning models, drawing on 15 features related to overnight returns, opening auction…
This article outlines a data-driven approach to cryptocurrency compliance. It recommends continuous transaction monitoring that uses machine learning to flag behavioral anomalies and graph analysis to trace fund flows and indirect links to mixers, darknet…
This research review describes a study of whether year-over-year changes in company filings contain information about future stock returns and business performance. It compares filings using four text-similarity measures, sorts stocks by similarity, and…
The report describes a weekly market-timing model based on a linear-chain conditional random field (CRF). It frames the task as inferring market direction states from observed features, with CRFs able to represent dependencies between observations and…
This introductory explanation presents Bayesian statistics as a framework for revising probability beliefs when new observations arrive. It contrasts this view with frequentist statistics, which treats probability through long-run frequencies in repeated…
This article describes modifying OpenFE’s automated feature engineering workflow for futures research. It adds rolling mean, standard deviation, and sum operators across instrument groups, with preset window lengths, and explains how these operators are…
This personal account explains how an equity prediction model can appear strong in-sample yet fail when applied to unfamiliar stocks or later periods. The described experiment uses fundamental and technical features, including valuation, profitability,…
This strategy combines model-based stock ranking with trend filters for a selected universe of China A-shares drawn from the CSI 150 and related large-cap core-asset stocks. It aims to rank candidates for potential five-day strength, then apply timing…
This article is a catalog of beginner-oriented examples for learning TensorFlow and TFLearn. It lists introductory operations, nearest-neighbor and regression models, neural networks, autoencoders, model saving, visualization, and multi-GPU basics. The…
The document compares a traditional Chinese equity strategy that buys the smallest market-cap stocks with a StockRanker model that ranks stocks using market capitalization as its sole feature. Both are evaluated over 2015–2016, illustrating how a…
The post proposes screening Chinese stocks for a daily high-low range of at least one percent, a nonempty outstanding convertible-bond name, and three simultaneous technical golden crosses. It explains the range filter as selecting volatile names and treats…
This review describes machine-learning approaches to predicting stock returns from accounting data and company characteristics. It compares linear methods, including ordinary least squares, LASSO, and ridge regression, with nonlinear models such as random…
This submission reflects on a quantitative strategy test whose results changed substantially when the author replaced its default factors or broadened the stock universe to include main-board and ChiNext shares. The observation highlights two practical…
This abstract copula framework provides shared methods for bivariate copula implementations, with named families including Archimedean, Gaussian, and Student forms. It evaluates copula density and cumulative joint probability, and calculates a conditional…
This guide describes a workflow for training an end-to-end stock model locally on compressed minute and multi-minute bar data, then submitting its weights and training code for cloud prediction. It explains that local files store prices and amounts as scaled…
This overview introduces artificial intelligence and machine learning as tools for analyzing financial data and supporting investment decisions or trading operations. It groups methods by task: supervised learning for prediction and classification,…
This workflow demonstrates an equity prediction pipeline using CSI 300 constituent data, Alpha158 features, and a multilayer perceptron. It defines training, validation, and test periods, prepares constituent-filtered data, normalizes features using robust…
This community post describes a discrepancy between a CNN-based stock strategy's backtest and simulated-trading signals. With a rolling window of five, both reportedly signal the same stock on January 4. With a window of ten, the simulation notification…
This educational overview introduces common inputs for quantitative stock selection, beginning with simple and exponential moving averages, RSI, Bollinger Bands, MACD, moving-average crossovers, VWAP, and historical volatility. It describes their usual…