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.
511 documents
The article explains market sentiment as investors’ broad outlook, shaped by economic, fundamental, technical, and other information. It distinguishes momentum approaches that follow prevailing sentiment from contrarian approaches that anticipate a reversal…
The article proposes evaluating automated strategies with two linked measures: win rate and the ratio of average winning to average losing trades. It defines expected edge as win probability times average win minus loss probability times average loss, and…
This article describes an introductory online course on momentum trading offered through B3’s education platform in partnership with QuantInsti. It presents the course as suitable for learners with basic Python knowledge and says the material covers…
This overview introduces multi-leg options strategies, including straddles, strangles, iron condors, and iron butterflies. It explains Delta, Gamma, Theta, Vega, and Rho as measures of how option values and portfolio exposures respond to changes in the…
This project tests a mean-reversion pairs strategy on Mexican stocks. It screens an initial equity universe for complete price histories and minimum average trading volume, then tests within-industry pairs for cointegration with an augmented Dickey-Fuller…
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…
This project describes an automated strategy that uses live EURUSD prices to generate signals for EURUSD, USDCHF, and XOM. A long signal occurs when EURUSD rises above the highest close of the prior five days; a short signal occurs below the lowest close.…
The article introduces principal component analysis (PCA) as a way to reduce the dimensionality of financial data while retaining much of its variation. It explains eigenvectors and eigenvalues as directions and magnitudes of transformation, then connects…
The article explains the order management system (OMS) as a component of an automated trading system. It describes the information an order should carry, including instrument, direction, quantity, price constraints, type, duration, execution algorithm, and…
This article is a curated overview of technical analysis learning resources rather than a single trading method. It points readers toward material on using indicators, combining signals, and creating indicator-based strategies, along with guides to bullish…
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 document introduces LEAPS as options with expirations more than a year away, allowing investors to take long-horizon directional positions or hedge stock holdings without buying or shorting shares outright. It explains that long-dated contracts can…
The document explains how moving averages summarize a rolling window of prices and how traders compare a faster average with a slower one. A cross above the slower average is commonly treated as a potential bullish signal, while a cross below is treated as…
This webinar listing introduces sentiment analysis, also called opinion mining, as the computational classification of text opinions into positive, negative, or neutral attitudes. It frames the technique as potentially relevant to financial markets alongside…
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 article organizes suggested reading for people learning algorithmic trading. Its categories span market microstructure, statistics and econometrics, technical analysis, options, advanced statistics, machine learning, Python, and portfolio management.…
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…
This study tests whether public filings reporting C-suite purchases of common shares are followed by abnormal stock returns. It builds a research sample from SEC Form 4 data, carefully distinguishing transaction rows, aggregated purchase components, and…
This tutorial explains how to connect a trading application to FXCM through the FIX protocol using the QuickFIX engine. It outlines the session settings and credentials, shows how the logon exchange works, and describes requesting trading-session status to…
The document introduces volatility as a measure of return dispersion and distinguishes historical volatility, calculated from past prices, from implied volatility inferred from option prices. Its historical-volatility example uses logarithmic returns and a…
This introductory tutorial presents NumPy as a tool for efficient numerical work in Python. It explains how arrays differ from lists: arrays support element-wise arithmetic, can be multidimensional, and generally hold values of a single type. Examples use…
The document explains how to explore portfolio allocations by repeatedly assigning random weights to four U.S. financial-sector stocks, calculating each portfolio’s annualized return and standard deviation, and comparing the results. It defines three…