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 research digest summarizes two studies on equity signals. The first trains a deep neural network on five years of fundamental data to forecast future fundamentals. The summary reports that, relative to a simple forecast assuming current data persist,…
This issue of a Chinese financial engineering research series introduces three overseas studies across stock selection, accounting analysis, and strategic asset allocation. The first builds a stock-selection approach using listed-company merger data and…
The document presents asset allocation as a combination of choosing assets and timing the market, with portfolio risk and expected return as the objectives. It explains that a hidden Markov model infers unobservable market states from observable data, then…
This code implements a bivariate mixture of Clayton, Frank, and Gumbel copulas, framed as a dependency model for a mixed-copula pairs trading strategy. It transforms each input series to empirical marginal quantiles, then fits the mixture weights and…
This study describes a revised deep Q-network approach for timing individual Chinese stocks. The earlier version used daily OHLCV data and several common factors as its state. The revision instead takes a configurable window of recent closing prices and…
The document summarizes a study that applies multiple machine-learning algorithms to a broad set of return predictors in Chinese equities. It highlights differences from earlier United States research: liquidity ranks as a particularly important predictor in…
This guide explains how to connect OctoBot to Reddit and configure it to monitor selected subreddits for traded cryptocurrencies. When its forum evaluator is enabled, the bot analyzes post sentiment with VADER and produces a coin-level summary for use by a…
This report summary examines how to turn a supplied composite stock factor into a long-only index-enhancement portfolio. It contrasts common linear optimization, which is simple to compute but ignores relationships among stocks and can produce concentrated…
This compilation presents three quantitative finance topics. First, it reports that news frequency and tone are associated with stock return jump likelihood and jump size characteristics. News frequency appears especially important for jump probability, and…
The article introduces XGBoost as a boosting ensemble and contrasts its sequential fitting of weak learners with the parallel training used in bagging. It explains residual-based improvement, complexity penalties, sample and feature subsampling, and…
The document explains a portfolio approach built around StockRanker, a model that ranks stocks using historical data. Each day, the strategy buys the highest-ranked candidates and sells or replaces holdings that fall toward the bottom of the ranking. The…
This notebook explores relationships among the Canadian dollar, crude oil benchmarks, and other currencies or commodities. It compares normalized series and fits separate ordinary least squares regressions of CAD against candidate variables, using R-squared…
This Japanese-language README curates resources for systematic trading research and implementation, including backtesting and live-trading frameworks, analytics tools, data sources, books, papers, blogs, and courses. Its practical framing is to reproduce…
This short exchange explains how to assign groups when training a learning-to-rank model on stock observations. The questioner expects each trading date to form a group, so the model ranks stocks against other stocks observed on that date, and asks where…
This indicator exports the Stochastic oscillator’s main and signal lines to a CSV file for analysis in MATLAB. The author presents the data as input for techniques such as neural networks, support vector machines, nearest-neighbor methods, and fuzzy systems,…
This Chinese-language tutorial describes implementing a stock-ranking strategy in BigTrader by passing StockRanker predictions directly to the trading engine and coding the trade logic there. It allocates purchases among the top-ranked stocks using weights…
The document presents a literature-search workflow for financial machine learning and quantitative finance, where relevant work may be spread across econometrics, machine learning, and other fields. It describes using a paper-mapping service to find related…
The document describes a hybrid trading system in which a neural network is trained to handle conditions that a basic trading system (BTS) does not address well. Its example assumes the base system follows trends, while neural network components identify…
This brief tutorial describes replacing the default stock-ranking model in a visual AI strategy template with gradient boosted decision tree training and prediction modules. It gives an example configuration: a regression loss, root mean squared error as the…
This article proposes comparing a model's information coefficient (IC) and mean IC across training and test samples to assess predictive stability. It defines IC as the correlation between a model score and subsequent returns, using a five-day return…
The document describes a daily macro trading process built around distinct research perspectives. Separate agents assess economic growth, inflation and interest rates, and debt, liquidity, currency, and central bank policy. A disagreement agent challenges…
The report summary describes a stock-selection factor built from a multinomial logistic model that estimates the probability of a large stock decline over the following month. It tests several decline thresholds and reports broad factor coverage, including…
This report overview introduces supervised neural networks, unsupervised learning, and reinforcement learning, then considers their possible roles in finance. It describes deep networks as learning nonlinear mappings through layered representations;…
This strategy framework assembles entry and exit rules from a broad catalog of technical indicators. The catalog spans moving averages, momentum and volume measures, volatility, price transforms, cycle measures, candlestick patterns, and statistical…