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 surveys a collection of blog posts for readers learning about algorithmic trading. The topics range from mathematical and statistical foundations to strategy families such as momentum, arbitrage, market making, and machine learning. It also…
The article explains why systematic research depends on reliable, structured inputs and outlines a Python workflow that retrieves end-of-day prices and fundamental growth data through financial data APIs. Its illustrative research question is whether…
The document introduces LangChain as a way to connect large language models with external data and compose repeatable analysis workflows. It explains basic components including model calls, prompt templates, chains, batching, and agents. Its equity-analysis…
The document explains how the risk-constrained Kelly criterion modifies standard Kelly position sizing. Standard Kelly sizing seeks to maximize long-run log growth using estimated win probability and win/loss payoff, but can lead to prolonged, deep…
The article explains random forests as ensembles of decision trees that reduce reliance on any single tree’s prediction. Trees are built from randomly selected data features, and their classifications are combined by majority vote; for continuous outputs,…
This overview explains the academic and practical skills that can support work in algorithmic trading. It maps computer science to programming, mathematics and statistics to probability and quantitative methods, finance and economics to markets and risk, and…
This profile follows a California data analyst’s move toward quantitative and algorithmic trading. His engineering, econometrics, and data work led him to explore Python, futures, automated analysis, and discretionary trading based on macro news sentiment.…
The article presents reinforcement learning (RL) as a trial-and-error approach in which an agent learns actions from rewards, with an emphasis on maximizing longer-term outcomes. It maps the framework to trading through states, such as price and indicators;…
This article introduces Bayesian inference by estimating the unknown probability of heads for a coin. It contrasts the frequentist view, where the parameter is fixed but unknown, with the Bayesian view, where uncertainty about the parameter is represented by…
This guide introduces algorithmic trading as a process of turning trading rules into programs, evaluating them with historical data, and deploying them for automated or partly automated execution. It outlines a learning path covering financial markets and…
The article outlines a supervised learning workflow for classifying EUR/USD direction. It introduces features, feature selection, and support vector machines, then describes a model using hourly EUR/USD data dating back to 2010, with MACD and Parabolic SAR…
The document outlines a conference about artificial intelligence, machine learning, and sentiment analysis in financial services. It describes research that processes news, social media, and other alternative data to classify sentiment and study its…
The article describes a workflow for using generative language models to assemble a thematic universe of healthcare companies involved in artificial intelligence. It starts with S&P 500 constituents, filters for healthcare firms, gathers company news, and…
This strategy uses a large language model to set long-only exposure for AAPL according to market states, rather than asking it to predict price direction. Historical price features are discretized into readable states, and monthly statistics for each state…
This project builds a random forest regression model to estimate the next day’s EUR/USD closing price from daily price data, technical indicators, and Twitter sentiment. Predictors include OHLCV values, short and long EMAs, RSI, OBV, and daily mean sentiment…
The document introduces general and finance-tuned language models, then describes using natural language processing to turn financial text into sentiment measures. It outlines a workflow for collecting and preprocessing Federal Open Market Committee…
This project describes two classifiers intended to predict whether Bank Nifty and its leading constituents would open higher or lower on the following trading day. The stock models use daily OHLCV history and technical indicators for five constituents; the…
The article explains data engineering as the work of collecting, preparing, organizing, and maintaining data so analysts and trading models can use it reliably. It describes engineers as building data infrastructure and pipelines, removing problems such as…
The article considers how increasingly capable artificial intelligence could change trading and financial markets. It distinguishes current rule-based automated trading from systems that learn and adapt, then speculates that AI could assess technical,…
The article explains Linear Discriminant Analysis (LDA) as a supervised method for classifying observations and estimating the probability of belonging to a class. It contrasts LDA with logistic regression and describes LDA’s use of Bayes’ theorem, class…
This document explains ADDM, a method for detecting changes in a trading model’s prediction errors and adapting the model when market conditions shift. Its detector uses a Self-Exciting Threshold Autoregressive (SETAR) model to divide error behavior into…
The article explains how a time-series generative adversarial network can produce synthetic financial observations when historical data is limited. It describes the generator and discriminator conceptually, then focuses on the conditional probabilistic…
The article demonstrates simple and multiple linear regression on historical returns for Coca-Cola, PepsiCo, the S&P 500 ETF, and the US Dollar Index. It first uses pairwise correlations, then fits a single-predictor model for Coca-Cola returns using the S&P…
The article introduces the Kalman filter as a recursive method for estimating a changing, partly unobserved state by combining model predictions with noisy measurements and their uncertainty. It explains concepts including normal distributions, variance,…