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 Expert Advisor builds several price patterns at different historical depths from tick movements. It encodes each pattern as a binary sequence according to whether each sampled price rose or fell, then tracks virtual trades for pattern configurations and…
The document outlines a class for building a generalized regression neural network. Its constructor sets input and output vector sizes, while a learning method accepts training patterns arranged in flat input and output arrays. Training stops when it reaches…
This document explains a market-regime indicator built on Bag-of-SFA-Symbols (BOSS). It converts normalized price windows into symbolic words using a low-pass Fourier representation and data-learned bins, then compares stretches of market history as…
This Chinese-language announcement outlines a community session devoted to building FinRL agents and comparing reinforcement-learning algorithms for quantitative investment. Its educational agenda covers how the DRLAgent class wraps different algorithms…
This Chinese-language account describes a learning project that builds a China stock selection strategy with five ranked factors and a two-day forward price label. It trains a Ridge regression model, standardizes the inputs, and periodically retrains before…
This account summarizes a 2021 conference talk on using machine learning to price stocks. A simple example feeds market-wide daily open, high, low, close, and average prices into an LSTM time-series model. The speaker stresses preparing noisy financial data,…
This guide explains how to configure OctoBot’s GPT interface and evaluator to use a language model for trading-related predictions. It covers adding an API credential for the hosted service, setting a custom LLM base address for another compatible server,…
This short BigQuant forum post concerns a modified XGBoost strategy whose equity curve and recent trade records appear unchanged despite adjustments to the data. The response offers two possible explanations: the backtest module may be reusing cached…
The article explains recurrent neural networks and LSTM gates, then outlines an A-share stock-selection example that predicts five-day returns from sequences of seven factors. It describes missing-value handling, outlier filtering, standardization, and…
The article explains how risk-constrained Kelly sizing modifies the classic Kelly approach, which chooses a capital fraction to maximize long-run log growth but can produce large drawdowns. The constrained version adds a limit on the probability that wealth…
This overview introduces regression as a way to model relationships between predictors and outcomes, then compares seven techniques: linear, logistic, polynomial, stepwise, ridge, lasso, and ElasticNet regression. It describes common use cases and…
This overview surveys a broad set of quantitative approaches, describing their basic mechanisms and the market settings or investor capabilities they may suit. It covers trend following, machine learning, arbitrage and market neutral methods, factor…
This study compares time-series and cross-sectional standardization of price and volume factors in an A-share stock-selection setting. It uses a dataset spanning 2011–2022 with 98 daily factors and a five-day return label, and compares LightGBM and deep…
This documentation explains how to define model inputs and prediction targets in FreqAI strategies. It distinguishes base features, such as price indicators, volume, and time variables, from configuration-driven expansions across periods, timeframes, shifted…
This configuration describes a Qlib workflow that trains an XGBoost model on China A-share data for the CSI 300 universe, using Alpha360 features. Its label is a forward close-to-close return over the next interval. Training and validation use earlier…
The post reports an observation from more than six months of intraday modeling in Chinese equities using five-minute bars. At each time point, the author independently tuned parameters with tree-structured Parzen estimation. The reported optimization…
This short assignment response defines quantitative investing as turning recurring patterns observed through statistical analysis of financial markets into structured methods for future trading. It mentions stocks, futures, and digital assets, and describes…
This guide explains how to carry training data for a StockRanker model from BigQuant’s 1.0 environment into version 3.0. In the older environment, it exports the training dataset with date, instrument, label, and the selected feature columns. The file is…
This tutorial introduces a basic neuron model, describing weighted inputs, a threshold, and an activation function. It explains why a smooth sigmoid function is used in practice in place of an idealized step function, then applies a neural network to…
This example describes a daily SPY strategy that assigns market analysis and order decisions to separate AI agents. The research agent compares the latest completed daily close with its 20-bar average and reports the date, observed prices, evidence for and…
The document explains a two-stage model that uses gradient-boosted decision trees to create features for logistic regression. The trees split and combine the original inputs; each sample is then represented by a binary vector marking the leaf it reaches in…
This tutorial demonstrates extracting minute-level stock data for a selected date range with a distributed query platform, then calculating features through SQL-style operators. The examples cover a five-period moving average of closing prices, a…
The document explains how to define and run a Qlib research workflow through a YAML configuration and the `qrun` command. It lays out the main stages: loading and preprocessing market data, training and applying a model, then analyzing forecast signals and…
This beginner-oriented guide distinguishes algorithmic trading from quantitative, automated, and high-frequency trading. It describes algorithmic trading as expressing a trading idea in an algorithm that can be tested and then executed manually or…