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 interview follows an engineer with mathematics, statistics, and machine learning experience who moved toward financial analytics and algorithmic trading. He describes learning about markets independently, finding that general online material did not…
The article introduces deep learning as a branch of machine learning and outlines neural network types, including multilayer perceptrons, convolutional networks, and recurrent networks. It describes supervised and unsupervised learning at a high level, and…
The article introduces extensions to ordinary linear regression for financial modeling. Polynomial regression adds transformed predictor terms to represent curved relationships while remaining linear in the coefficients. The article describes forward…
The article introduces algorithmic trading as software that computes signals from market data and can submit or divide orders. It organizes preparation around five requirements: access to appropriate data, charting and research platforms, programming…
This interview follows a trader’s education in economics, technical analysis, and algorithmic trading. He describes using data and backtests to examine discretionary trading ideas while continuing to place trades manually, rather than operating a fully…
This tutorial demonstrates evaluating a decision-tree model that predicts whether the next adjusted close of Apple stock will rise or fall. It constructs features from the open-close relationship, daily range, and rolling return volatility and average…
This article explains the difference between model parameters, which are learned from training data, and hyperparameters, which are set before training and guide the learning process or model structure. Examples include learning rate, training epochs,…
The document explains Python Pickle as a way to serialize Python objects into binary files and restore them in a later session. It frames the technique as useful when data preparation, backtesting, or model training takes substantial time, since saved…
The document lays out a workflow for turning financial text into trading signals: collect news or social posts, preprocess the text, assign sentiment scores, combine those scores with technical indicators to form signals, and backtest the resulting model. It…
This interview follows Srinivas Hosur’s path from an MBA in finance and a trading job into compliance and risk analysis at a high-frequency trading firm. He describes moving from manual trading toward systematic methods after encountering the limits of…
This announcement describes the early contents and development plans for MLFinLab, a Python package based on methods from a financial machine learning text. Its covered techniques include financial data structures built from raw tick data, such as imbalance…
This document reviews research practices for applying machine learning and quantitative methods to investing. It outlines common barriers to financial machine learning, including the interdisciplinary nature of the work, limited data, and markets shaped by…
The article explains Theory-Implied Correlation (TIC), a method for estimating portfolio correlations by combining observed correlations with an externally specified hierarchy of assets. It describes three stages: fit a hierarchical tree to empirical…
The article describes an experiment applying meta-labeling to S&P 500 E-mini futures data. It combines event-based sampling, the triple-barrier method, and meta-labeling with two example strategies: trend following and mean reversion using Bollinger Bands.…
The document explains online portfolio strategies that find historical market windows resembling current conditions. CORN measures similarity with Pearson correlation rather than Euclidean distance and uses the resulting matches to guide portfolio weights.…
This article outlines a research workflow for quantitative finance teams, from reviewing prior work to framing a research question, planning a study, conducting analysis, preparing a paper, and organizing group learning. It recommends assessing the quality…
The article describes the entry challenge in quantitative finance as learning both the financial ideas behind markets and the technical skills used to analyze them. It situates the field across mathematics, statistics, finance, and computing, with…
Meta labeling adds a secondary classifier to a primary model that already proposes a trade direction or classification. The primary model is tuned for high recall, accepting some false positives; the secondary model then estimates whether those proposals are…
The article presents Model Fingerprints as a way to describe how machine learning features affect predictions. It estimates partial dependence by varying one feature while averaging predictions over other observations, then separates that dependence into…
This project update describes research notebooks for financial machine learning topics, including tick, volume, and dollar bars; CUSUM event filtering; vertical barriers; and triple-barrier labels. It outlines comparisons of bar sampling using weekly count…
The article draws on employee accounts and public descriptions of several quantitative investment firms to discuss how research teams are organized. Common themes include scientific inquiry, freedom to test ideas, cross-disciplinary discussion, shared…
The article explains why financial time series are often made stationary for statistical inference and supervised machine learning, then presents fractional differentiation as a way to reduce nonstationarity while retaining more of a price series’ memory…
This article develops a framework for comparing several trading strategies and potentially selecting among them over time. It introduces object-oriented MQL5 classes with a shared parent interface for updating indicators and generating buy and sell signals,…
The article describes the Camel Algorithm, a population-based optimizer inspired by camel movement and survival in desert conditions. Each candidate solution is treated as a camel, and search behavior is shaped by randomized temperature, declining supplies,…