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 document describes a stock-screening process that looks for shares rising beyond a chosen percentage threshold during intraday trading, with the aim of identifying candidates for short positions. A first stage compares the current price with the prior…
This article uses a series of trading examples to show how implementation choices can make backtests look stronger or weaker than a live strategy. It highlights omitted transaction costs and slippage, incorrect treatment of reinvestment and whole-unit…
The document introduces XGBoost as a gradient boosting method that combines decision trees sequentially, with each new tree aimed at correcting earlier prediction errors. Its objective balances predictive loss against a regularization term that discourages…
The article explains dark pools as private securities trading venues, often used by institutions seeking to execute large orders with less exposure of their intentions. It describes execution linked to the National Best Bid and Offer, outlines independent,…
The article outlines the mathematical, statistical, and programming foundations associated with quantitative finance, then describes common quant work: trading strategy development, portfolio optimization, risk management, and asset pricing. It recommends…
The article classifies market events as black swans (unexpected), grey swans (possible but unlikely), or white swans (expected), then considers how they may affect stocks, bonds, and derivatives. It suggests that strategies should reflect how much warning…
Survivorship bias arises when analysis includes only assets or funds that remain observable, omitting those that failed, delisted, or closed. The document explains the issue with the military aircraft example: examining only planes that returned can…
The document introduces K-nearest neighbors (KNN), a supervised learning method that classifies or estimates a new observation using nearby examples selected by a distance metric. For classification, neighboring labels determine the predicted class by…
Market impact is an implicit trading cost measured by the difference between the pre-trade reference price and the execution price. The document separates temporary impact, which reflects the concession needed to find counterparties, from permanent impact,…
This beginner overview explains algorithmic trading as rules that use market inputs such as price, time, and volume to generate orders. It outlines a basic architecture with a market-data handler, a strategy module, and an order router, describing how data…
This personal account describes an algorithmic trader’s move from strategies written in Rust without machine learning to Python-based work that combines machine learning with trading-system development. The central lesson is that predictive-model metrics…
The article explains K-means clustering as an unsupervised method for grouping stocks with similar feature values, then applies it to candidate selection for statistical arbitrage. It contrasts clustering with manually choosing pairs by sector or industry,…
This project backtests equal-weight stock portfolios selected using alpha, prior-year returns, volatility, beta, and return divided by volatility. It varies portfolio size, stock universe, and annual, semiannual, or quarterly rebalancing, while describing…
This guide explains VADER, a rule-based text sentiment tool that assigns word-level valence and combines it into a normalized compound score. It describes how the lexicon incorporates words, slang, and emoticons, and how sentence-level heuristics account for…
This beginner tutorial explains how Python variables are created by assignment, updated, and assigned together. It outlines naming rules and readability conventions, then introduces integers, floating-point numbers, strings, and booleans. Examples use values…
This project outlines a market-neutral long-short portfolio framework that selects assets with cross-sectional momentum and compares equal weighting, inverse-volatility weighting, Hierarchical Risk Parity (HRP), and Hierarchical Equal Risk Contribution…
The article explains how standard GARCH models capture volatility clustering while treating positive and negative return shocks symmetrically. GJR-GARCH adds a term activated by negative shocks, allowing downside moves to have an additional effect on…
The article describes directional change (DC), a threshold-based way to represent price movement through confirmed turning points rather than fixed-time observations. It defines upward and downward runs, their overshoots, and three derived measures: total…
The article introduces hidden Markov models as a way to infer unobserved states from observed data. It explains the Markov property, transition probabilities, and time-homogeneous chains, then relates hidden-state estimation to the Kalman filter’s prediction…
This article describes market sentiment as the collective outlook of participants and explains how text sentiment analysis attempts to classify opinions as positive, negative, or neutral. It outlines a workflow of collecting news, social media, or financial…
This beginner guide explains that cryptocurrency wallets manage the keys used to access and transact with crypto assets. It distinguishes a public address, which can be shared to receive funds, from a private key, which must remain protected. The article…
This interview traces a trader’s progression from executing commodity orders to coding trading systems and researching algorithmic strategies. The subject describes developing trend detection and momentum systems with position sizing, then building a…
The document introduces portfolio management when the portfolio’s components are trading strategies as well as assets. It contrasts equal weighting as a starting point with Markowitz efficient-frontier allocation, which balances return against a risk…
In this interview, trader Nikolas Pareschi recounts his move from discretionary trading to algorithmic research and describes how he developed ideas through finance papers and statistical testing. He emphasizes that many indicators and methods fail…