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
Siddharth Bhatia describes moving from a liberal arts degree and finance work into algorithmic trading. After gaining experience in the financial industry and completing the CFA, he learned programming and trading technology to update his skills and apply…
This interview follows Sanjot Raibagkar’s move from software engineering into algorithmic trading. He describes a decade of trading, work on financial technology projects, and a focus on Nifty and Bank Nifty options. His technical and data science background…
The article presents Stereoscopic Portfolio Optimization (SPO), a framework that combines traditional mean-variance allocation with bottom-up analysis of asset-level market microstructure. It describes volatility as one possible input, while noting that…
This project uses a Gaussian Hidden Markov Model to classify market conditions as high or low volatility, then applies those classifications to a moving-average crossover strategy. Daily returns serve as the observable input, although the author also…
This guide surveys machine learning methods and describes a Python workflow for a simple stock price prediction task. It proposes predicting a day’s closing price from earlier OHLC data, preparing historical observations, creating lagged inputs, and making…
This tutorial outlines a workflow for using machine learning in an algorithmic trading example. It describes sourcing historical prices, preparing lagged OHLC features, transforming price data, splitting observations into training and test sets, tuning model…
Using a restaurant on a food delivery app as an example, the document presents a general six-step data science workflow: define the business problem, collect data, clean and remediate it, analyze and interpret it, model it, and communicate findings. The…
The article introduces logistic regression as a supervised learning method for estimating the probability of a categorical outcome. For trading, it presents a binary classification setup: predict whether a stock will rise or fall using historical inputs such…
The article surveys five skills it presents as useful for modern trading: quantitative analysis, Python, backtesting, machine learning, and risk management. It links quantitative research to statistical testing of market data and technical signals, and…
This guide collects free educational materials for learning machine learning and applying it to trading. It groups courses, books, blogs, research papers, and videos, ranging from introductory machine learning and Python tools to interpretable models, neural…
This project outlines a process for predicting trade exits with machine learning. It samples market data into dollar bars, estimates volatility from each bar’s high and low using Parkinson’s method, and generates initial buy or sell signals from a…
This article presents a basic text sentiment workflow for financial commentary using R. It applies a bag-of-words approach: prepare positive and negative term lists, clean an earnings-call transcript, tokenize it into words and n-grams, and compare the…
This project tests whether a Random Forest classifier using six technical indicators can generate useful long-only signals for ten South African Top40 stocks. The indicators cover trend, oscillation, volatility, and volume. The study trains separate models…
The article introduces mixture models as a possible framework for forecasting asset returns when their statistical behavior changes over time. It notes that returns can exhibit changing means and volatility, nonlinear patterns, volatility clustering,…
The document explains walk-forward optimization (WFO) as a rolling backtest procedure intended to address weaknesses in a single static optimization and validation split. In each cycle, strategy parameters are fitted using a historical in-sample window, then…
The article explains why raw datasets need cleaning and transformation before machine learning, with examples framed around trading data. It discusses missing values, outliers, categorical features, inconsistent date formats, and overfitting. Suggested…
The document is an outline for a webinar introducing machine learning in quantitative finance. It lists an overview of machine learning and its workflow, features of the Quantiacs toolkit, and a demonstration applying machine learning to futures data. The…
The document describes a simple trend-following system built with linear regression. It introduces ordinary least squares and explains rolling regression as fitting a model repeatedly using a recent window of candles. In the example, the model relates each…
The article explains Gini impurity as a measure of class mixing in a decision tree node. It gives the formula one minus the sum of squared class probabilities, then describes evaluating a candidate feature by calculating each resulting branch’s impurity and…
The article explains the triple-barrier method as a way to create supervised learning labels using an upper profit-taking threshold, a lower stop threshold, and a time limit. It frames these barriers as a risk-aware alternative to fixed thresholds and…
This career guide outlines the work of quantitative analysts, traders, data scientists, and developers, then describes skills that may be assessed in quant interviews. It emphasizes practical market knowledge, programming, statistical analysis, and…
The article introduces support vector machines as classifiers that separate labeled observations with a boundary chosen to maximize the margin around nearby points. It explains support vectors, hard-margin classification, and soft margins that permit some…
This project builds daily trading signals for the Nifty 50 using logistic and linear regression. It derives predictors from OHLC relationships, including whether the close is nearer the high or low and whether the current high or low exceeds the prior day’s…
This recap of an industry Q&A discusses learning quantitative and algorithmic trading, career paths, and how AI tools affect research. Its central advice is to focus on process and evidence rather than the excitement of profit and loss or the novelty of…