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.
232 documents
The document presents hypothesis testing as an early step in quantitative strategy research. It uses a claim about whether the average return of Nifty 50 stocks exceeds a specified benchmark to explain how to define null and alternative hypotheses, choose a…
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 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 study proposes distinguishing human-originated orders from high-frequency algorithmic orders using the time taken to modify an order before execution. Orders with a minimum or average replacement time below a selected threshold are labeled algorithmic;…
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…
The article describes trading ideas as hypotheses about how an asset may behave in particular circumstances, then suggests developing them through experience, research papers, forums, books, and learning from practitioners. It gives momentum research as an…
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…
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 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…
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…
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 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…
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 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…
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…
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…
This webinar description explains how high-frequency prices can extend portfolio risk analysis beyond the low-frequency data commonly used in portfolio metrics. The proposed approach uses intraday observations to estimate risk and support portfolio…
This article uses simple betting examples to explain expected value as the probability-weighted average of gains and losses. It shows how a favorable payoff structure can produce positive expectation even when a win is uncertain, while a symmetric…
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…